The aim of this review is to provide quantum engineers with an introductory guide to the central concepts and challenges in the rapidly accelerating field of superconducting quantum circuits. Over the past twenty years, the field has matured from a predominantly basic research endeavor to a one that increasingly explores the engineering of larger-scale superconducting quantum systems. Here, we review several foundational elements—qubit design, noise properties, qubit control, and readout techniques—developed during this period, bridging fundamental concepts in circuit quantum electrodynamics and contemporary, state-of-the-art applications in gate-model quantum computation.
Quantum processors harness the intrinsic properties of quantum mechanical systems—such as quantum parallelism and quantum interference—to solve certain problems where classical computers fall short.1–6 Over the past two decades, rapid developments in the science and engineering of quantum systems have advanced the frontier in quantum computation, from the realm of scientific explorations on single isolated quantum systems toward the creation and manipulation of multiqubit processors.7,8 In particular, the requirements imposed by larger quantum processors have shifted the mindset within the community, from solely scientific discovery to the development of new, foundational engineering abstractions associated with the design, control, and readout of multiqubit quantum systems. The result is the emergence of a new discipline termed “quantum engineering,” which serves to bridge basic sciences, mathematics, and computer science with fields generally associated with traditional engineering.
One prominent platform for constructing a multiqubit quantum processor involves superconducting qubits, in which information is stored in quantum degrees of freedom (DOFs) of nanofabricated, anharmonic oscillators (AHOs) constructed from superconducting circuit elements. In contrast to other platforms, e.g., electron spins in silicon9–14 and quantum dots,15–18 trapped ions,19–23 ultracold atoms,24–27 nitrogen-vacancies in diamonds,28,29 and polarized photons,30–33 where the quantum information is encoded in natural microscopic quantum systems, superconducting qubits are macroscopic in size and lithographically defined.
One remarkable feature of superconducting qubits is that their energy-level spectra are governed by circuit element parameters and thus are configurable; they can be designed to exhibit “atomlike” energy spectra with the desired properties. Therefore, superconducting qubits are also often referred to as “artificial atoms,” offering a rich parameter space of possible qubit properties and operation regimes, with predictable performance in terms of transition frequencies, anharmonicity, and complexity.
While there are many other excellent reviews on superconducting qubits, see, e.g., Refs. 34–43, this work specifically aims to introduce new quantum engineers (academic and industrial alike) to the terminology and state-of-the-art practices used in the rapidly accelerating field of superconducting quantum computing. The reader is assumed to be familiar with the basic concepts that span classical physics, quantum mechanics, and electrical engineering. In particular, readers will find it useful to have had previous exposure to classical mechanics, the Schrödinger equation, the Bloch sphere representation of qubit states, second quantization, basic concepts of superconductivity, electromagnetism, introductory circuit analysis, classical Boolean logic, linear dynamical systems, analog and digital signal processing, and familiarity with microwave components such as transmission lines and mixers. These topics will be introduced as they arise, but having basic prior knowledge will be helpful.
A. Organization of this article
This review is organized in the following four sections: first, in Sec. II, we explore the parameter space available when designing superconducting circuits. In particular, we look at the promising capacitively shunted planar qubit modalities and how these can be engineered with the desired properties, such as transition frequency, anharmonicity, and reduced susceptibility to various sources of noise. In this section, we also introduce several ways in which interactions between qubits can be engineered, in order to implement two-qubit entangling operations, needed for a universal gate set.
In Sec. III, we discuss systematic and stochastic noise, the concepts of noise strength and qubit noise susceptibility, and the common sources of noise which lead to decoherence in superconducting circuits. We introduce the Bloch-Redfield model of decoherence, characterized by longitudinal and transverse relaxation times T1 and T2, and discuss the implications of 1/f noise. We then define the noise power spectral density (PSD), which is commonly used to characterize noise processes, and describe how it drives decoherence. Finally, we close the section with a review of coherent control methods used to mitigate certain types of coherence and reversible noise.
In Sec. IV, we provide a review of how single- and two-qubit operations are typically implemented in superconducing circuits, by using a combination of local magnetic flux control and microwave drives. In particular, we discuss the family of two-qubit gates arising from a capacitive coupling between qubits, and introduce several recent advances that have been demonstrated to achieve high-fidelity gates, as well as applications in quantum information processing that use these gates. The continued development of high-fidelity two-qubit gates in superconducting qubits is a highly active research area. For this reason, we include sufficient technical details that a reader may use this review as a starting point to critically assess the pros and cons of the various gates, as well as develop an appreciation for the types of gate-engineering already implemented in-state-of-the-art superconducting quantum processors.
Finally, in Sec. V, we discuss the physics and engineering associated with the dispersive readout technique, typically used to measure the individual qubit states in modern quantum processors. After a discussion of the theory behind dispersive coupling, we give an introduction to design of Purcell filters and the development of quantum-limited parametric amplifiers (PAs).
II. ENGINEERING QUANTUM CIRCUITS
In this section, we will demonstrate how quantum systems based on superconducting circuits can be engineered to achieve certain desired properties. Using the most common qubit modalities, we discuss how properties such as the qubit transition frequency, anharmonicity, and noise susceptibility can be tailored by the choice of circuit topology and element parameter values. We also discuss how to engineer the interactions between different quantum systems, in particular, the cases of qubit-qubit and qubit-resonator couplings.
A. From quantum harmonic oscillator to the transmon qubit
A quantum mechanical system is governed by the time-dependent Schrödinger equation
where is the state of the quantum system at time t, is the reduced Planck's constant h/2π, and is the “Hamiltonian” that describes the total energy of the system. The “hat” is used to indicate that is a quantum operator. As the Schrödinger equation is a first-order linear differential equation, the temporal dynamics of the quantum system may be viewed as a straightforward example of a linear dynamical system with a formal solution
The time-independent Hamiltonian governs the time evolution of the system through the operator . Thus, just as with classical systems, determining the Hamiltonian of a system—whether the classical Hamiltonian H or its quantum counterpart —is the first step to deriving its dynamical behavior. In Sec. IV, we consider the case when the Hamiltonian is time-dependent in the context of qubit control.
To understand the dynamics of a superconducting qubit circuit, it is natural to start with the classical description of a linear LC resonant circuit [Fig. 1(a)]. In this system, energy oscillates between electrical energy in the capacitor C and magnetic energy in the inductor L. In the following, we will arbitrarily associate the electrical energy with the “kinetic energy” and the magnetic energy with the “potential energy” of the oscillator. The instantaneous, time-dependent energy in each element is derived from its current and voltage
where and denote the voltage and current of the capacitor or inductor.
To derive the classical Hamiltonian, we follow the standard approach used in classical mechanics: the Lagrange-Hamilton formulation. Here, we represent the circuit elements in terms of one of its generalized circuit coordinates, charge or flux. In the following, we pick flux, defined as the time integral of the voltage
In this example, the voltage at the node is also the branch voltage across the element. In this section, we will simply refer to these as node voltages and fluxes for convenience. For a more detailed discussion of nodes and branches in this context, we refer the reader to Ref. 44.
Note that in the following, we could have exchanged our associations with kinetic energy (momentum coordinate) and potential energy (position coordinate), and instead start with the charge variable Q(t), which is the time integral of the current I(t).
From the Lagrangian in Eq. (7), we can further derive the Hamiltonian using the Legendre transformation, for which we need to calculate the momentum conjugate to the flux, which in this case, is the charge on the capacitor
The Hamiltonian of the system is now defined as
as one would expect for an electrical LC circuit. Note that this Hamiltonian is analogous to that of a mechanical harmonic oscillator, with mass m = C and resonant frequency , which expressed in position, x, and momentum, p, coordinates takes the form .
The Hamiltonian described above is classical. In order to proceed to a quantum-mechanical description of the system, we need to promote the charge and flux coordinates to quantum operators, whereas the classical coordinates satisfy the Poisson bracket
the quantum operators similarly satisfy a “commutation relation”
where the operators are indicated by hats. From this point forward, however, the hats on operators will be omitted for simplicity.
In a simple LC resonant circuit [Fig. 1(a)], both the inductor L and the capacitor C are linear circuit elements. Defining the reduced flux and the reduced charge n = Q/2e, we can write down the following quantum-mechanical Hamiltonian for the circuit
where EC = e2/(2C) is the charging energy required to add “each” electron of the Cooper-pair to the island and is the inductive energy, where is the superconducting magnetic flux quantum. Moreover, the quantum operator n is the excess number of Cooper-pairs on the island, and ϕ—the reduced flux—is denoted the “gauge-invariant phase” across the inductor. These two operators form a canonical conjugate pair, obeying the commutation relation [ϕ, n] = i. We note that the factor 4 in front of the charging energy EC is solely a historical artifact, namely, that this energy scale was first defined for single-electron systems and then adopted to two-electron Cooper-pair systems.
The Hamiltonian in Eq. (13) is identical to the one describing a particle in a one-dimensional quadratic potential, a quantum harmonic oscillator (QHO). We can treat ϕ as the generalized position coordinate, so that the first term is the kinetic energy and the second term is the potential energy. We emphasize that the functional form of the potential energy influences the eigensolutions. For example, the fact that this term is quadratic () in Eq. (13) gives rise to the shape of the potential in Fig. 1(b). The solution to this eigenvalue problem gives an infinite series of eigenstates , whose corresponding eigenenergies Ek are all equidistantly spaced, i.e., , where denotes the resonant frequency of the system, see Fig. 1(b). We may represent these results in a more compact form (second quantization) for the quantum harmonic oscillator (QHO) Hamiltonian
where is the creation (annihilation) operator of a single excitation of the resonator. The Hamiltonian in Eq. (14) is written as energy. It is, however, often preferred to divide by so that the expression has units of radian frequency, since we will later resonantly drive transitions at a particular frequency or reference the rate at which two systems interact with one another. Therefore, from here on, will be omitted.
The original charge number and phase operators can be expressed as and , where and are the “zero-point fluctuations” of the charge and phase variables, respectively. Quantum mechanically, the quantum states are represented as wavefunctions that are generally distributed over a range of values of n and ϕ and, consequently, the wavefunctions have nonzero standard deviations. Such wavefunction distributions are referred to as “quantum fluctuations,” and they exist, even in the ground state, where they are called zero-point fluctuations.
The linear characteristics of the QHO have a natural limitation in its applications for processing quantum information. Before the system can be used as a qubit, we need to be able to define a computational subspace consisting of only two energy states (usually the two-lowest energy eigenstates) in between which transitions can be driven without also exciting other levels in the system. Since many gate operations, such as single-qubit gates (Sec. IV), depend on frequency selectivity, the equidistant level-spacing of the QHO, illustrated in Fig. 1(b), poses a practical limitation.427
To mitigate the problem of unwanted dynamics involving noncomputational states, we need to add anharmonicity (or nonlinearity) into our system. In short, we require the transition frequencies and be sufficiently different to be individually addressable. In general, the larger the anharmonicity the better it is. In practice, the amount of anharmonicity sets a limit on how short the pulses used to drive the qubit can be. This is discussed in detail in Sec. IV D 3.
To introduce the nonlinearity required to modify the harmonic potential, we use the Josephson junction—a nonlinear, dissipationless circuit element that forms the backbone in superconducting circuits.46,47 By replacing the linear inductor of the QHO with a Josephson junction, playing the role of a nonlinear inductor, we can modify the functional form of the potential energy. The potential energy of the Josephson junction can be derived from Eq. (3) and the two Josephson relations
resulting in a modified Hamiltonian
where is the total capacitance, including both shunt capacitance Cs and the self-capacitance of the junction CJ, and is the Josephson energy, with Ic being the critical current of the junction.428 After introducing the Josephson junction in the circuit, the potential energy no longer takes a manifestly parabolic form (from which the harmonic spectrum originates), but rather features a cosinusoidal form, see the second term in Eq. (16), which makes the energy spectrum nondegenerate. Therefore, the Josephson junction is the key ingredient that makes the oscillator anharmonic and thus allows us to identify a uniquely addressable quantum two-level system, see Fig. 1(d).
Once the nonlinearity has been added, the system dynamics is governed by the dominant energy in Eq. (16), reflected in the EJ/EC ratio. Over time, the superconducting qubit community has converged toward circuit designs with . In the opposite case when , the qubit becomes highly sensitive to charge noise, which has proven more challenging to mitigate than flux noise, making it very hard to achieve high coherence. Another motivation is that current technologies allow for more flexibility in engineering the inductive (or potential) part of the Hamiltonian. Therefore, working in the limit, makes the system more sensitive to the change in the potential Hamiltonian. Therefore, we will focus here on the state-of-the-art qubit modalities that fall in the regime . For readers who are interested in the physics in the regime, such as the earlier Cooper-pair box charge qubit, we refer to Refs. 48–51.
To access the regime, one preferred approach is to make the charging EC small by shunting the junction with a large capacitor, , effectively making the qubit less sensitive to charge noise—a circuit commonly known as the transmon qubit.52 In this limit, the superconducting phase ϕ is a good quantum number, i.e., the spread (or quantum fluctuation) of ϕ values represented by the quantum wavefunction is small. The low-energy eigenstates are therefore, to a good approximation, localized states in the potential well, see Fig. 1(d). We may gain more insight by expanding the potential term of Eq. (16) into a power series (since ϕ is small), that is
The leading quadratic term in Eq. (17) alone will result in a QHO, recall Eq. (13). The second term, however, is quartic which modifies the eigensolution and disrupts the otherwise harmonic energy structure. Note that, the negative coefficient of the quartic term indicates that the anharmonicity is negative and its limit in magnitude thus cannot be made arbitrarily large. For the case of the transmon, α = –EC is usually designed to be 100–300 MHz, as required to maintain a desirable qubit frequency GHz, while keeping an energy ratio sufficiently large () to suppress charge sensitivity.52 Fortunately, the charge sensitivity is exponentially suppressed for an increased EJ/EC, while the reduction in anharmonicity only scales as a weak power law, leading to a workable device.
Including terms up to fourth order and using the QHO eigenbases, the system Hamiltonian resembles that of a Duffing oscillator
Since , we can see that the transmon qubit is basically a weakly anharmonic oscillator (AHO). If excitation to higher noncomputational states is suppressed over any gate operations, either due to a large enough or due to robust control techniques such as the derivative reduction by adiabatic gate (DRAG) pulse, see Sec. IV D 3, we may effectively treat the AHO as a quantum two-level system, simplifying the Hamiltonian to
where σz is the Pauli-z operator. However, one should always keep in mind that higher levels physically exist.53 Their influence on the system dynamics should be taken into account when designing the system and its control processes. In fact, there are many cases where the higher levels have proven useful to implement more efficient gate operations.54
In addition to reducing the charge dispersion, the use of a large shunt capacitor also enables us to engineer the electric field distribution of the quantum system, and thus the participation of surface loss mechanisms. In the development of the 3D transmon,55 e.g., a 2D transmon coupled to a 3D cavity, it was demonstrated that by making the gap between the two lateral capacitor plates large (compared to the film thickness) the coherence time increases since a smaller portion of the electric field interacts with the lossy interfaces, e.g., metal-substrate and substrate-vacuum interfaces, which has been studied extensively.56–61
B. Qubit Hamiltonian engineering
1. Tunable qubit: Split transmon
To implement fast gate operations with high-fidelity, as needed to implement quantum logic, many (though not all63) of the quantum processor architectures implemented today feature tunable qubit frequencies.64–67 For instance, in some cases, we need to bring two qubits into resonance to exchange (swap) energy, while we also need the capability of separating them during idling periods to minimize their interactions. To do this, we need an external parameter which allows us to access one of the degrees of freedom of the system in a controllable fashion.
One widely used technique is to replace the single Josephson junction with a loop interrupted by two identical junctions—forming a DC superconducting quantum interference device (DC-SQUID).68 Due to the interference between the two arms of the SQUID, the effective critical current of the two parallel junctions can be decreased by applying a magnetic flux threading the loop, see Fig. 2(a). Due to the fluxoid quantization condition, the algebraic sum of branch flux of all of the inductive elements along the loop plus the externally applied flux equal an integer number of superconducting flux quanta, that is
where . Using this condition, we can eliminate one degree of freedom and treat the SQUID-loop as a single junction, but with the important modification that EJ is tunable (via the SQUID critical current) by means of the external flux Φext. The effective Hamiltonian of the so-called split transmon (ignoring the constant) is
We can see that Eq. (21) is analogous to Eq. (16), with EJ replaced by . The magnitude of the net, effective Josephson energy has a period of Φ0 in applied flux and spans from 0 to its maximum value 2EJ. Therefore, the qubit frequency can be tuned periodically with Φext, see Fig. 2(b).
While the split transmon enables frequency tunability by the externally applied magnetic field, it also introduces sensitivity to random flux fluctuations, known as flux noise. At any working point, the slope of the qubit spectrum, , indicates to first order how strongly this flux noise affects the qubit frequency. The sensitivity is generally nonzero, except at multiples of the flux quantum, , where k is an integer, where .
One recent development has focused on reducing the qubit sensitivity to flux noise, while maintaining sufficient tunability to operate our quantum gates. The idea is to make the two junctions in the split transmon asymmetric,69 see Fig. 2(c). This yields the following Hamiltonian
where and is the junction asymmetry parameter, with . Again, we can treat the two junctions as a single-junction transmon, with an effective Josephson energy . In particular, we can recognize the two special cases; for d = 0, the Hamiltonian in Eq. (22) reduces to the symmetric case with , as in Eq. (21) with . In the other limit, when and the flux-tunability of the Josephson energy vanishes, which is equivalent to the single junction case, recall Eq. (16).
From the discussion above we see that going from symmetric to asymmetric transmons does not change the circuit topology. This seemingly trivial modification, however, has a profound impact for practical applications. As we can see from the qubit spectra, Fig. 2(d), the flux sensitivity is suppressed across the entire tunable frequency range. For example, the performance of the cross-resonance gate is optimized with a certain frequency detuning between two qubits.70 Therefore, by using an asymmetric transmon, a small frequency-tuning range is introduced that is sufficient to compensate for fabrication variations, without introducing unnecessary large susceptibility to flux noise and thus maintaining high coherence. For another example, a surface code scheme based on the adiabatic controlled phase (CPHASE)-gate requires specific frequency configuration among qubits in order to avoid frequency crowding issues, and asymmetric transmons fit well with its well-defined frequency range.71 In general, as the quantum processors scale up and fabrication improves, asymmetric transmons are likely to be found in wider applications in the future.
2. Toward larger anharmonicity: Flux qubit and fluxonium
We see that split transmon qubits, be it symmetric or not, still share the same topology as the single junction version, yielding a sinusoidal potential. Therefore, the degree to which the properties of these qubits can be engineered has not fundamentally changed. In particular, the limited anharmonicity in transmon-type qubits intrinsically causes significant residual excitation to higher-energy states, undermining the performance of gate operations. To go beyond this, it is necessary to introduce additional complexity into the circuit.
One outstanding development in this regard is the invention of the flux qubit,72,73 where the qubit loop is interrupted by three (or four) junctions, see Fig. 2(e). On one branch is one smaller junction; on the other branch are two identical junctions, both a factor γ larger in size compared to the small junction. The addition of one more junction as compared to the split transmon is nontrivial, as it changes the circuit topology and reshapes the potential energy profile.
Each junction is associated with a phase variable, and the fluxoid quantization condition again allows us to eliminate one degree of freedom. Consequently, we have a two-dimensional potential landscape, which in comparison to the simpler topology of the transmon, complicates the problem both conceptually and computationally. Fortunately, under the assumed setting that the array junctions are larger in size (γ > 1), it is usually a good approximation to treat the problem as a particle moving in a quasi-1D potential, which also helps us gain more insight and intuition about the system and draw qualitative conclusions. The Hamiltonian under this “quasi-1D approximation” reads
Note that the phase variable in Eq. (23) is the sum of the branch phases across the two array junctions, ϕ = (φ1 + φ2)/2, assuming the same current direction across φ1 and φ2. The external magnetic flux is denoted . The second term in Eq. (23) is contributed by the small junction with Josephson energy EJ, whereas the third term takes into account the two array junctions, together with Josephson energy 2γEJ. Clearly, the sum of these two terms no longer has the characteristics of a simple cosinusoid, and the final potential profile as well as the corresponding eigenstates depend on both the external flux φe and the junction area ratio γ.
The most common working point for this system is when , where k is an integer—that is when half a superconducting flux quantum threads the qubit loop. At this flux bias point, the qubit spectrum reaches its minimum, and the qubit frequency is first-order insensitive to flux noise, see Fig. 2(f). This point is often referred to as “the flux degeneracy point,” where flux qubits tend to have the optimal coherence time.
At this operation point, the potential energy may assume a single-well () or a double-well (γ < 2) profile. The single-well case shares some similarities with the transmon qubit, where the quadratic and quartic terms of the Hamiltonian determines the harmonicity and anharmonicity, respectively. The capacitively shunted flux qubit (CSFQ)62,74 was explored in this regime, demonstrating long coherence and decently high anharmonicity. Note that as opposed to the transmon qubit, the anharmonicity of the CSFQ is “positive” (α > 0). While the improvement in anharmonicity can be associated with reshaping the energy potential, the improved coherence over the first flux qubits can be attributed to the introduction of the capacitive shunt, similar to the modified Cooper-pair box leading to the transmon qubit.
The double-well case obtained for γ < 2 was demonstrated and investigated much earlier.72,73 The intuitive picture based on circulating current states—so it gets the name persisting-current flux qubit (PCFQ)—gives a satisfying physical description of the qubit degrees of freedom. However, from the perspective of a quantum engineer, the qubit properties are of more interest, even if sometimes we may lose physical intuition about the system in certain regimes; such as when γ ≈ 2 and there are no clear circulating current states. The most important feature of the PCFQ is that its anharmonicity can be much greater than the transmon and CSFQ and the transition matrix elements become considerably smaller given equivalent EJ/EC. Therefore, a longer relaxation time can be expected. These features have been demonstrated even more prominently in its close relative, the fluxonium qubit.75
The flux qubit is a striking example that illustrates how one dramatically can engineer the qubit properties through the choice of various circuit parameters. The introduction of array junctions and consequent biharmonic profile generates rich dynamics as well as broad applications. An extention of this idea is the fluxonium qubit, which generated substantial interest recently, due partly to its capability of engineering the transition matrix elements to achieve millisecond T1 time, and due partly to the invention of novel gate schemes applicable to such well-protected qubits.76,77
Compared to flux qubits, which usually contain two or three array junctions,78 the number of array junctions in the fluxonium qubit is dramatically increased,75,79 in some cases, to the order of 100, see Fig. 2(g). Following the same quasi-1D approximation as for the flux qubit, the last term in Eq. (23) becomes , where N denotes the number of array junctions. For large N, the argument in the cosine term ϕ/N becomes sufficiently small that a second order expansion is a good approximation. This results in the fluxonium Hamiltonian
where EL = (γ/N)EJ is the inductive energy of the effective inductance contributed by the junction array—often known as superinductance due to its large value.79–81 Therefore, we can treat the potential energy as a quadratic term modulated by a sinusoidal term, similar to that of an rf-SQUID type flux qubit.82 However, the kinetic inductance of the Josephson junction array is in general much larger than the geometric inductance of the wire in an rf-SQUID.
Depending on the relative magnitude of EJ and EL, the fluxonium system could involve plasmon states (in the same well) and fluxon states (in different wells). There are a variety of schemes to utilize them for quantum information processing. Generally, the spectrum of the transition between the lowest energy states is similar to that of the flux qubit, see Fig. 2(h). Both long coherence and high anharmonicity can be expected at the flux sweet spot.
Lastly, we want to point out a further extension—the 0–π qubit—which has even stronger topological protection from noise.83,84 However, the strongly suppressed sensitivity to external fluctuations also makes it hard to manipulate.
C. Interaction Hamiltonian engineering
To generate entanglement between individual quantum systems—it is necessary to engineer an interaction Hamiltonian that connects degrees of freedom in those individual systems. In this section, we discuss the physical coupling mechanism and its representation in the qubit eigenbasis. The use of coupling to form 2-qubit gates is discussed in Sec. IV.
1. Physical coupling: Capacitive and inductive
The Hamiltonian of two coupled systems takes a generic form
where H1 and H2 denote the Hamiltonians of the individual quantum systems, which could be any combination of the qubit circuits mentioned in Secs. II A and II B. The last term, Hint, is the interaction Hamiltonian, which couples the variables of both systems. In superconducting circuits, the physical form of the coupling energy is either an electric or magnetic field (or a combination thereof).
To achieve capacitive coupling, a capacitor is placed between the voltage nodes of the two participating circuits, yielding an interaction Hamiltonian Hint of the form
where Cg is the coupling capacitance and V1(V2) is the voltage operator of the corresponding voltage node being connected. Figure 3(a) illustrates a realistic example of a direct capacitive coupling between the top nodes of two transmon qubits. Circuit quantization in the limit of yields
where the expressions in brackets are the two Hamiltonians of the individual qubits, [see Eq. (16)], and we take in Eq. (26). From Eq. (27), we see that the coupling energy depends on the coupling capacitance as well as the matrix elements of the voltage operators. The dependencies are bilinear in the perturbative limit ().
To implement the coupling capacitance, one only need bring the edges of the capacitor pads into close proximity, as has been demonstrated in-state-of-the-art planar designs.85 The coupling capacitance is determined by the planar capacitor geometry as well as the surrounding environment, such as the dielectric constant of the substrate and the ground plane proximity.
In the case of inductive coupling, a mutual inductance shared by two loops is the coupling mechanism, yielding an interaction Hamiltonian of the form
where M12 denotes the mutual inductance and is the current-operator of the loop current. A typical example is two closely positioned (rf-SQUID type) flux qubits, as illustrated in Fig. 3(c). The system Hamiltonian can be expressed as
where the individual qubit Hamiltonians are identical to that of the fluxonium in Eq. (24), and the current operators, with , is the familiar DC-Josephson relation for each junction, see Eq. (15). In this case, the strength of the inductive coupling energy depends on the mutual inductance as well as the matrix element of the current operators.
To realize a mutual inductance, two looped circuits are brought into close proximity to one another, or, to make them stronger, overlapping with each other,86 and even may share the same wire or Josephson junction inductor.87–90 In the case of a Josephson junction, and for certain metals, the inductance is dominated by “kinetic inductance” contributions, rather than solely geometric inductance.91,92 Kinetic inductance arises from the mechanical, inertial mass of the charge carriers, but is only practically witnessed in very high-conductance materials like superconductors. A primary feature of kinetic inductance is that its values can vastly exceed those of conventional geometric inductances, which are generally limited by electromagnetic considerations.79
2. Coupling axis: Transverse and longitudinal
Regardless of its physical realization, the effect of a coupling on system dynamics is determined by its form as represented in the eigenbasis of the individual systems. That is, how Hint appears in the representation spanned by the eigenbasis of H1 ⊗ H2.
where the expression within brackets represent the Duffing oscillator Hamiltonian for the qubits and g is the coupling energy. Since we define , and consequently , the original -term becomes what is shown in Eq. (30). Such a coupling is called “transverse,” because the coupling Hamiltonian has nonzero matrix elements only at off-diagonal positions with respect to both oscillators, i.e., for any integer k and for , and in this case, .
If we can ignore higher energy levels () either because of sufficient anharmonicity or through careful control protocols that ensure these levels never have influence, we may truncate the Hamiltonian in Eq. (30) to
This is a Hamiltonian of two spins, coupled by an exchange interaction. As we will see in Sec. IV D 1, such a Hamiltonian is most commonly used in contemporary implementations and can generate various types of two-qubit entangling gates. Note that, more often, we see that the interaction term is expressed in instead of . The choice in the context here is arbitrary and does not change the dynamics. However, when both capacitive and inductive couplings are present in the system, both and may be needed. In this case, the voltage operator (reduced to σy after two-level approximation in the lab frame) is transversal to the current operator (reduced to σx) and both of them may be transverse to the qubit. A similar example is demonstrated between a qubit and a resonator by Lu et al.93
Transverse coupling can be engineered between a qubit and a harmonic oscillator, see Fig. 3(b). In this case, the Hamiltonian becomes
where ωq and ωr denote the qubit and resonator frequencies, and and describes the processes of exciting and de-exciting the qubit, respectively. Here, we have assumed that the coupling is in the dispersive limit, i.e., , hence ignoring the double (de)excitation terms proportional to and , which under typical operation regimes oscillate sufficiently fast to average to zero. The Hamiltonian in Eq. (32), is the standard model used for describing how a two-level atom interacts with a resonant cavity that houses it. Such a structure is also known as cavity quantum electrodynamics (cQED), and it is extended to the circuit version here. It has many useful applications in superconducting quantum information architectures, such as high-fidelity readout,94 see Sec. V, cavity buses,95 quantum memory,96,97 quantum computation with cat states,98–100 etc.
Here, we briefly mention the use of a cavity or resonator to mediate coupling between two qubits, which may be physically well-separated (≈1 cm). Since most superconducting resonators are in the GHz frequency range, they can be made much longer than any dimension of a qubit circuit (≈1 mm). One can use such a resonator to mediate coupling between two or more otherwise noninteracting qubits. An example is shown in Fig. 3(b), where two transmon qubits are both capacitively coupled to the center resonator. The two-level system Hamiltonian is:
It can be shown that in the dispersive limit, i.e., , the resonator can—after proper transformation and approximation—be treated as an isolated system, and the composite system simplified to two transversely coupled qubits, see Eq. (31).
We now turn to the previous example of two inductively coupled flux qubits, see Fig. 3(c). Assume that the double-well potential [Fig. 2(g)] has a relatively high interwell barrier, which leads to an exponentially small qubit transition frequency at the energy degeneracy point, (Φe = π). Around this degeneracy point, the off-diagonal matrix element of is zero, i.e., the ground and excited states are localized in different wells and . We can then rewrite the Hamiltonian in Eq. (29) as
Now, the coupling axis is the same as the qubit quantization axes and therefore termed “longitudinal coupling.” Note, however, that the physical and couplings can change in the qubit frame.
Longitudinal coupling is an important type of interaction, because it can generate entanglement without energy exchange. Moreover, it is found a necessary ingredient in the application of quantum annealing, where certain hard combinatorial optimization problems can be modeled by the Ising Hamiltonian in Eq. (34) and finding its ground state would solve this problem.
An intermediate qubit mode may also be used as a coupler in the longitudinal case. In Fig. 3(d), an additional rf-SQUID is used to mediate the coupling. The coupling strength can be tuned by the flux bias of the coupler SQUID.101 Note that a tunable coupler may also be realized in a structure with capacitive couplings.63 A tunable coupler is useful because it provides a wide range of coupling strengths,102 a high on-off ratio103 for reducing gate error-rates, and more ways of achieving high-fidelity entangling gates.67,104–106 The trade-off is an additional control line.
In addition to the pure transversal and longitudinal qubit-qubit interactions, there are also examples of mixed types of interaction Hamiltonians107
which are longitudinal with respect to a qubit, but transverse with respect to a harmonic oscillator in a qubit-resonator system. Such a model is called longitudinal but one should note that it is only longitudinal to one participating system. It is hard to engineer physically longitudinal coupling with respect to a harmonic oscillator, since either the E-field (V) or the B-field (I) is transverse with respect to the eigen field of the harmonic oscillator. Note, however, that a transversal model such as in Eq. (32) may be transformed into a longitudinal one in certain operating regimes, see Sec. V.
In some applications, such as for quantum annealing, both longitudinal and transverse couplings are desired (σzσz coupling for mapping the problem and σxσx coupling for enhancing the annealing performance) and require independent control.
III. NOISE, DECOHERENCE, AND ERROR MITIGATION
Random, uncontrollable physical processes in the qubit control and measurement equipment or in the local environment surrounding the quantum processor are sources of noise that lead to decoherence and reduce the operational fidelity of the qubits. In this section, we introduce the basics of noise leading to decoherence in superconducting circuits, and we discuss coherent control methods to mitigate certain types of noise.
A. Types of noise
In a closed system, the dynamical evolution of a qubit state is deterministic. That is, if we know the starting state of the qubit and its Hamiltonian, then we can predict the state of the qubit at any time in the future. However, in open systems, the situation changes. The qubit now interacts with uncontrolled degrees of freedom in its environment, which we refer to as fluctuations or noise. In the presence of noise, as time progresses, the qubit state looks less and less like the state we would have predicted and, eventually, the state is lost. There are many different sources of noise that affect quantum systems, and they can be categorized into two primary types: systematic noise and stochastic noise.
1. Systematic noise
Systematic noise arises from a process that is traceable to a fixed control or readout error. For example, we apply a microwave pulse to the qubit that we believe will impart a 180° rotation. However, the control field is not tuned properly and, rather than rotating the qubit 180°, the pulse slightly over-rotates or under-rotates the qubit by a fixed amount. The underlying error is “systematic,” and it therefore leads to the same rotation error each time it is applied. However, when such erroneous pulses are used in practice in a variety of control sequences, the observed results may appear to be influenced by random noise. This is because the pulse is generally not applied in the same way for each experiment: it could be applied a different number of times, interspersed with different pulses in different orders, and therefore generally differs from experiment to experiment. However, once systematic errors are identified, they can generally be corrected through proper calibration or the use of improved hardware.
2. Stochastic noise
The second type of noise is stochastic noise, arising from random fluctuations of parameters that are coupled to our qubit.108 For example, thermal noise of a 50 Ω resistor in the control lines leading to the qubit will have voltage and current fluctuations—Johnson noise—with a noise power that is proportional to both temperature and bandwidth. Or, the oscillator that provides the carrier for a qubit control pulse may have amplitude or phase fluctuations. Additionally, randomly fluctuating electric and magnetic fields in the local qubit environment—e.g., on the metal surface, on the substrate surface, at the metal-substrate interface, or inside the substrate—can couple to the qubit. This creates unknown and uncontrolled fluctuations of one or more qubit parameters, and this leads to qubit decoherence.
3. Noise strength and qubit susceptibility
The degree to which a qubit is affected by noise is related to the amount of noise impinging on the qubit, and the qubit's susceptibility to that noise. The former is often a question of materials science and fabrication; that is, can we make devices with lower levels of noise. Or, it may be related to the quality of the control electronics and cryogenic engineering to limit the levels of noise on the control lines that necessarily connect to the qubits to control them. The latter—qubit susceptibility—is a question of qubit design. Qubits can be designed to trade-off sensitivity to one type of noise at the expense of increased sensitivity to other types of noise. Thus, materials science, fabrication engineering, electronics design, cryogenic engineering, and qubit design all play a role in creating devices with high coherence. In general, one should strive to eliminate the sources of noise, and then design qubits that are insensitive to the residual noise.
The qubit response to noise depends on how the noise couples to it—either through a longitudinal or a transverse coupling as referenced to the qubit quantization axis. This can be visualized using a Bloch Sphere picture of the qubit state, as illustrated in Fig. 4 and discussed in detail in Sec. III B.
B. Modeling noise and decoherence
1. Bloch sphere representation
The “Bloch sphere” is a unit sphere used to represent the quantum state of a two-level system (qubit). Figure 4(a) shows a Bloch sphere with a “Bloch vector” representing the state . If we visualize the Bloch sphere as the planet Earth, then by convention, the north pole represents state and the south pole state . For pure quantum states such as , the Bloch vector is of unit length, , connecting the center of the sphere to any point on its surface.
The z-axis connects the north and south poles. It is called the “longitudinal axis,” since it represents the “qubit quantization axis” for the states and in the qubit eigenbasis. In turn, the x–y plane is the “transverse plane” with “transverse axes” x and y. In this Cartesian coordinate system, the unit Bloch vector is represented using the polar angle and the azimuthal angle , as illustrated in Fig. 4(a). Following our convention, state at the north pole is associated with +1, and state (the south pole) with –1. We can similarly represent the quantum state using the angles θ and ϕ,
The Bloch vector is stationary on the Bloch sphere in the “rotating frame picture.” If state has a higher energy than state (as it generally does in superconducting qubits), then in a stationary frame, the Bloch vector would precess around the z-axis at the qubit frequency . Without loss of generality (and much easier to visualize), we instead “choose” to view the Bloch sphere in a reference frame where the x and y-axes also rotate around the z-axis at the qubit frequency. In this “rotating frame,” the Bloch vector appears stationary as written in Eq. (36). The rotating frame will be described in detail in Sec. IV D 1 in the context of single-qubit gates.
For completeness, we note that the density matrix for a pure state is equivalently
where I is the identity matrix, and is a vector of Pauli matrices. If the Bloch vector is a unit vector, then ρ represents a pure state ψ and Tr(ρ2) = 1. More generally, the Bloch sphere can be used to represent “mixed states,” for which ; in this case, the Bloch vector terminates at points “inside” the unit sphere, and . To summarize, the surface of the unit sphere represents pure states, and its interior represents mixed states.6
2. Bloch-Redfield model of decoherence
Within the standard Bloch-Redfield109–111 picture of two-level system dynamics, noise sources weakly coupled to the qubits have short correlation times with respect to the system dynamics. In this case, the relaxation processes are characterized by two rates (see Fig. 4),
which contains the pure dephasing rate Γφ. We note that the definition of Γ2 as a sum of rates presumes that the individual decay functions are exponential, which occurs for Lorentzian noise spectra (centered at ω = 0) such as white noise (short correlation times) with a high-frequency cutoff.
The impact of noise on the qubit can be visualized on the Bloch sphere in Fig. 4(a). For an initial state (t = 0)
the Bloch-Redfield density matrix ρBR for the qubit is written112,113
First, we have introduced the “longitudinal decay function” , which accounts for longitudinal relaxation of the qubit.
Second, we introduced the “transverse decay function” , which accounts for transverse decay of the qubit.
Third, we have introduced an explicit phase accrual , where , which generalizes the Bloch sphere picture to account for cases where the qubit frequency ωq differs from the rotating-frame frequency ωd, as we will see later when discussing measurements of T2 using Ramsey interferometry,114,115 and in Sec. IV D 1, in the context of single-qubit gates.
Fourth, we have constructed the matrix such that for , the upper-left matrix element will approach a unit value, indicating that all populations relax to the ground state, while the other three matrix elements decay to zero. This is related to the assumption that the environmental temperature is low enough that thermal excitations of the qubit from the ground to the excited state rarely occur.
a. Longitudinal relaxation
The longitudinal relaxation rate Γ1 describes depolarization along the qubit quantization axis, often referred to as “energy decay” or “energy relaxation.” In this language, a qubit with polarization p = 1 is entirely in the ground state at the north pole, p = –1 is entirely in the excited state at the south pole, and p = 0 is a completely depolarized mixed state at the center of the Bloch sphere.
As illustrated in Fig. 4(b), longitudinal relaxation is caused by “transverse noise,” via the x- or y-axis, with the intuition that off-diagonal elements of an interaction Hamiltonian are needed to connect and drive transitions between states and .
Depolarization occurs due to energy exchange with an environment, generally leading to both an “up transition rate” (excitation from to ), and a “down transition rate” (relaxation from to ). Together, these form the longitudinal relaxation rate Γ1
T1 is the 1/e decay time in the exponential decay function in Eq. (43), and it is the characteristic time scale over which the qubit population will relax to its steady-state value. For superconducting qubits, this steady-state value is generally the ground state, due to Boltzmann statistics and typical operating conditions. Boltzmann equilibrium statistics lead to the “detailed balance” relationship , where T is the temperature and is the Boltzmann constant, with an equilibrium qubit polarization approaching . Typical qubits are designed at frequency GHz and are operated at dilution refrigerator temperatures T ≈ 20 mK. In this limit, the up-rate is exponentially suppressed by the Boltzmann factor , and so only the down-rate contributes significantly, relaxing the population to the ground state. Thus, qubits generally spontaneously lose energy to their cold environment, but the environment rarely introduces a qubit excitation. As a result, the equilibrium polarization approaches unity [see Eq. (43)].116,117
Only noise at the qubit frequency mediates qubit transitions, whether absorption or emission, and this noise is generally “well behaved” (short correlation time, many modes weakly coupled to qubit, no divergences) around the qubit frequency for superconducting qubits. The intuition is that qubit-transition linewidths are relatively narrow in frequency, and so the noise generally does not vary much over this narrow frequency range. Although there are a few notable exceptions, for example, qubit decay in the presence of hot quasiparticles,118–120 which can lead to nonexponential decay functions, longitudinal depolarization measurements generally present exponential decay functions consistent with the Bloch-Redfield picture.
An example of a T1 measurement is shown in Fig. 5(a). The qubit is prepared in its excited state using an -pulse, and then left to spontaneously decay to the ground state for a time τ, after which the qubit is measured. A single measurement will project the quantum state into either state or state , with probabilities that correspond to the qubit polarization. To make an estimate of this polarization, one needs to identically prepare the qubit and repeat the experiment many times. This is analogous to flipping a coin: any single flip will yield heads or tails, but the probability of obtaining a heads or tails can be estimated by flipping the coin many times and taking the ensemble average. The resulting exponential decay has a characteristic time T1 = 85 μs.
b. Pure dephasing
The “pure dephasing” rate Γϕ describes depolarization in the x–y plane of the Bloch sphere. It is referred to as “pure dephasing,” to distinguish it from other phase-breaking processes such as energy excitation or relaxation.
As illustrated in Fig. 4(c), pure dephasing is caused by “longitudinal noise” that couples to the qubit via the z-axis. Such longitudinal noise causes the qubit frequency ωq to fluctuate, such that it is no longer equal to the rotating frame frequency ωd, and causes the Bloch vector to precess forward or backward in the rotating frame. Intuitively, we can imagine identically preparing several instances of the Bloch vector along the x-axis. For each instance, the stochastic fluctuations of qubit frequency will result in a different precession frequency, resulting in a net fanout of the Bloch vector in the x–y plane. This eventually leads to a complete depolarization of the azimuthal angle ϕ. Note that this stochastic effect will be captured in the transverse relaxation rate Γ2 (Sec. III B 2 c); it is “not” the deterministic term that appears in Eq. (43), which represents intentional detuning of the qubit reference frame.
There are a few important distinctions between pure dephasing and energy relaxation. First, in contrast to energy relaxation, pure dephasing is not a resonant phenomenon; noise at any frequency can modify the qubit frequency and cause dephasing. Thus, qubit dephasing is subject to broadband noise. Second, since pure dephasing is elastic (there is no energy exchange with the environment), it is in principle “reversible.” That is, the dephasing can be “undone”—with quantum information being preserved—through the application of unitary operations, e.g., dynamical decoupling pulses,78 see Sec. III D 2.
The degree to which the quantum information can be retained depends on many factors, including the bandwidth of the noise, the rate of dephasing, the rate at which unitary operations can be performed, etc. This should be contrasted with spontaneous energy relaxation, which is an “irreversible” process. Intuitively, once the qubit emits energy to the environment and its myriad uncontrollable modes, the quantum information is essentially lost with no hope for its recovery and reconstitution back into the qubit.
c. Transverse relaxation
The transverse relaxation rate describes the loss of coherence of a superposition state, for example , pointed along the x-axis on the equator of the Bloch sphere as illustrated in Fig. 4(d). Decoherence is caused in part by longitudinal noise, which fluctuates the qubit frequency and leads to pure dephasing Γφ (red). It is also caused by transverse noise, which leads to energy relaxation of the excited-state component of the superposition state at a rate Γ1 (blue). Such a relaxation event is also a phase-breaking process, because once it occurs, the Bloch vector points to the north pole, , and there is no longer any knowledge of which direction the Bloch vector had been pointing along the equator; the relative phase of the superposition state is lost.
Transverse relaxation T2 can be measured using Ramsey interferometry, as shown and described in Fig. 5(b). The protocol positions the Bloch vector on the equator using a -pulse. Typically, the carrier frequency of this pulse is slightly detuned from the qubit frequency by an amount δω. As a result, the Bloch vector will precess around the z-axis at a rate δω. This is done for convenience sake, so that the resulting Ramsey measurement will oscillate, making it easier to analyze. After precessing for a time τ, a second -pulse projects the Bloch vector back on to the z-axis. Repeated measurements are made to take an ensemble averaged estimate of the qubit polarization, as a function of τ. The resulting oscillations in Fig. 5(b) feature an approximately exponential decay function with time . The “*” indicates that the Ramsey experiment is sensitive to “inhomogeneous broadening.” That is, it is highly sensitive to quasistatic, low-frequency fluctuations that are constant within one experimental trial, but vary from trial to trial, e.g., due to 1/f-type noise. This sensitivity to quasistatic noise is related to the corresponding N = 0 noise filter function shown in Fig. 5(d) being centered at zero-frequency, as described in more detail in Sec. III D 2.
The Hahn echo shown in Fig. 5(c) is an experiment that is less sensitive to quasistatic noise. By placing a Y π pulse at the center of a Ramsey interferometry experiment, the quasistatic contributions to dephasing can be “refocused,” leaving an estimate T2E that is less sensitive to inhomogeneous broadening mechanisms. The pulses are generally chosen to be resonant with the qubit transition for a Hahn echo, since any frequency detuning would be nominally refocused anyway. The resulting decay function in Fig. 5(c) is essentially exponential with time .
With the known T1 and T2 times, one can infer the pure dephasing time Tφ from Eq. (41), provided the decay functions are exponential. In superconducting qubits, however, the broadband dephasing noise (e.g., flux noise, charge noise, critical-current noise, …) tends to exhibit a 1/f-like power spectrum. Such noise is singular near ω = 0, has long correlation times, and generally does not fall within the Bloch-Redfield description. The decay function of the off-diagonal terms in Eq. (43) is generally nonexponential, and for such cases, the simple expression in Eq. (41) is not applicable.
3. Modification due to 1/f-type noise
If we assume that the qubit is coupled to many independent fluctuators, then, regardless of their individual statistics, they will in concert generate noise with a Gaussian distribution due to the central limit theorem. We therefore say that the longitudinal fluctuations exhibit Gaussian-distributed 1/f noise. For 1/f noise spectra, the phase decay function is itself a Gaussian , where we write to distinguish it from Tφ used in Eq. (41). Furthermore, this function is separable from the T1-type exponential decay, because the T1-noise remains regular at the qubit frequency. The density matrix in Eq. (43) becomes, following Refs. 78 and 112,
where the decay function contains the “coherence function” , which generalizes pure dephasing to include nonexponential decay functions. As we shall see later, the subscript N labeling the decay function refers to the number of π-pulses used to refocus the low-frequency noise, which impacts the form of the decay function. Because the function is no longer purely exponential, we cannot formally write the transverse relaxation decay function as . However, an exponential decay remains a practically reasonable approximation for . We also note that the energy decay component of the transverse relaxation is , and so T2 can never be larger than 2T1. In the absence of pure dephasing, the maximum T2 = 2T1 is reached.
As an example, consider the Ramsey interferometry data in Fig. 5(b). Since the dephasing is relatively weak, the transverse relaxation function as is a reasonable fit and yields T2 = 95 μs. However, using the value T1 = 85 μs from Fig. 5(a) and dividing out from the data in Fig. 5(b), the remaining pure dephasing decay function is shown in Fig. 5(d) and assumes a Gaussian envelope , with . The Hahn echo data in Fig. 5(c) may be treated similarly.
For completeness, in addition to 1/f dephasing mechanisms, we note that there are also “white” pure dephasing mechanisms, which give rise to an exponential decay function for the dephasing component of T2. One common example is dephasing due to the shot noise of residual photons in the readout resonator coupled to superconducting qubits, as we discuss in Sec. III C 3.
4. Noise power spectral density (PSD)
The frequency distribution of the noise power for a stationary noise source λ is characterized by its PSD
The Wiener-Khintchine theorem states that the PSD is the Fourier transform of the autocorrelation function of the noise source λ. Since the integration limits are , this is the bilateral PSD. Symmetrizing the PSD allows one to consider only positive frequencies, which is termed a unilateral PSD. Both unilateral and bilateral PSDs are used, often with the same notation, and so one needs to know how the PSD is defined, to keep track of the factors 2 and π, and also be aware of the implications for quantum systems.
For classical systems, the noise power spectral density is symmetric. This is because the autocorrelation function of real signals is itself a real function, and the Fourier transform of a real temporal function is symmetric in the frequency domain. Dephasing noise is caused by real, fluctuating fields, and so its PSD is generally symmetric. Examples of such classical noise include thermal (Johnson) noise and 1/f noise122 (see Fig. 6).
In turn, the inverse Fourier transform of the PSD will yield the autocorrelation function
This implies that integrating the noise power spectral density with τ = 0 yields the second moment of the noise, or, for zero-mean fluctuations, the variance.
However, the autocorrelation function for a quantum system may be complex-valued due to the fact that quantum operators generally do not commute at different times. This means that time-ordering of the operators matters, and the PSD need not be symmetric in frequency. This is generally the case for transverse noise causing longitudinal energy relaxation. Noise at a positive frequency corresponds to energy transfer from the qubit to the environment, including both stimulated and spontaneous emission, associated with the down-rate . Noise at a negative frequency corresponds to energy transfer to the qubit from the environment, associated with the up-rate . This energy transfer becomes exponentially suppressed when the qubit frequency is larger than thermal energy (kB T), as shown in Fig. 6. For a detailed discussion, see Refs. 123 and 124. Spontaneous emission to a cold environment or electromagnetic vacuum, represented by Nyquist noise in Fig. 6, is an example of an asymmetric noise PSD.121
C. Common examples of noise
There are many sources of stochastic noise in superconducting qubits, and we refer the reader to Ref. 40 for a review. Here, we briefly present several of the most common types of noise, their effect on coherence, and refer the reader to the references for a more detailed discussion.
1. Charge noise
“Charge noise” is ubiquitous in solid-state devices. It arises from charged fluctuators present in the defects or charge traps that reside in interfacial dielectrics, the junction tunnel barrier, and in the substrate itself. These are often modeled as an ensemble of fluctuating two-level systems or as bulk dielectric loss.130,131 For example, in the case of a transmon qubit, the electric field between the capacitor plates traverses and couples to dielectric defects residing on the metal surfaces of the plates (for lateral-plate-type capacitors) or the capacitor dielectric between the plates (for parallel-plate-type capacitors). The electric field variable is transverse with respect to the quantization axis of the transmon qubit, which means that this noise is mainly responsible for energy relaxation (T1). Additionally, if the EJ/EC ratio of the transmon is not made sufficiently large (smaller than around 60), the qubit frequency itself will also be sensitive to broadband charge fluctuations. In this case, low-frequency charge noise couples longitudinally to the transmon and causes pure dephasing (Tφ).
Charge noise is modeled primarily as a combination of inverse-frequency noise and Nyquist noise, also referred to as “ohmic” noise. At lower frequencies, the spectral density takes the form
with quasiuniversal values at 1 Hz, and . In addition to large 1/f fluctuations, early charge qubits often witnessed discrete, charge offsets reminiscent of random telegraph noise. Together, these two mechanisms severely limited the utility of charge qubits, and served as a strong motivation to move to capacitively shunted charge qubits (transmons), which greatly reduced the qubit longitudinal sensitivity to charge noise. At higher frequencies, the power spectrum takes the form , where the noise strength at 1 Hz can assume a range of values depending on the level of dissipation in the system. Likewise, the cross-over from 1/f-like behavior to f-like behavior generally occurs at around 1 GHz, but will vary higher or lower between samples depending on the degree of dissipation.62,132
2. Magnetic flux noise
Another commonly observed noise in solid-state devices is magnetic “flux noise.” The origin of this noise is understood to arise from the stochastic flipping of spins (magnetic dipoles) that reside on the surfaces of the superconducting metals comprising the qubit,133 resulting in random fluctuations of the effective magnetic field that biases flux-tunable qubits.
For example, in the case of the split transmon, the external magnetic field threading the loop couples longitudinally to the qubit and modulates the transition frequency via the Josephson energy EJ (except at , where the qubit is first-order insensitive to magnetic-field fluctuations). Because the flux noise is longitudinal to the transmon, it contributes to pure dephasing (Tφ). However, in the case of the flux qubit, and depending on the flux-bias point, the flux noise may be either longitudinal—causing dephasing Tφ—or it may couple transversely and thus contribute to T1 relaxation.62,78 The noise power spectrum of these fluctuations generally exhibits a “quasiuniversal” dependence
with and , and has been shown to extend from less than millihertz to beyond gigahertz frequencies.78,127,128,134,135
The large, low-frequency weighting of the 1/f power distribution enables the use of engineered error mitigation techniques—such as dynamical decoupling—to achieve better coherence78,136,137 and for improving single and two-qubit gate fidelity.138 It was recently demonstrated that 1/f flux noise is also a T1-mechanism when extended out to the qubit frequency,62 and one similarly expects a crossover to ohmic flux noise at high enough frequencies.139
Although much is known about the statistics and number of the defects presumed responsible for flux noise, their precise physical manifestation remains uncertain.133,140 The fact that the 1/f noise is quasiuniversal and largely independent of device, strongly suggests a common origin for the noise. Recent studies suggest that adsorbed molecular oxygen may be responsible for flux-noise.140,141
3. Photon number fluctuations
In the circuit QED architecture, resonator “photon number fluctuation” is another major decoherence source.142 Residual microwave fields in the cavity have photon-number fluctuations that in the dispersive regime impact the qubit through an interaction term , see Sec. II C 2, leading to a frequency shift , where is the average photon number, and effectively scales the photon population seen by the qubit due to the interplay between the qubit-induced dispersive shift of the resonator frequency (χ) and the resonator decay rate (κ).
In the dispersive limit, the noise is longitudinally coupled to the qubit and leads to pure dephasing at a rate
The fluctuations originate from residual photons in the resonator, typically due to radiation from higher temperature stages in the dilution refrigerator.106,143 The corresponding noise spectral density is of Lorentzian type
which exhibits an essentially white noise spectrum up to a 3 dB cutoff frequency ω = κ set by the resonator decay rate κ, see Ref. 62.
“Quasiparticles,” i.e., unpaired electrons, are another important noise source for superconducting devices.119 The tunneling of quasiparticles through a qubit junction may lead to both T1 relaxation and pure dephasing Tφ, depending on the type of qubit, the bias point, and the junction through which the tunneling event occurs.118,120
Quasiparticles are naturally excited due to thermodynamics, and the quasiparticle density in equilibrium superconductors should be exponentially suppressed as the temperature decreases. However, below about 150 mK, the quasiparticle density observed in superconducting devices—generally in the range 10–8–10–6 per Cooper pair—is much higher than what the BCS theory would predict for a superconductor in equilibrium with its cryogenic environment at 10 mK. The reason for this excess quasiparticle population is unclear, but it is very likely related to the presence of additional, nonthermal mechanisms that increase the generation rates, “bottleneck effects” that occur at millikelvin temperatures to reduce recombination rates, or a combination of both.
It has been shown that the observed T1 and excess excited-state population measured in today's state-of-the-art high-coherence transmon are self-consistent with excess “hot” nonequilibrium quasiparticles at the quasiuniversal density of around 10–7–10–6 per Cooper pair.144,145 Although this quasiparticle generation mechanism is not yet well understood, it has been shown that quasiparticles can be transiently pumped away, improving T1 times and reducing T1 temporal variation.120
D. Operator form of qubit-environment interaction
Similar to the way two qubits are coupled, a qubit may couple and interact with uncontrolled degrees of freedom (DOF) in its environment (the noise sources). The interaction Hamiltonian between the qubit DOF () and those of the noise source () may be expressed in a general form
where ν denotes the coupling strength—which is related to the sensitivity of the qubit to environmental fluctuations —and we assume that is a qubit operator within the qubit Hamiltonian . The noisy environment represented by the operator produces fluctuations δλ. Note that we retained the hats in this section to remind us that these are quantum operators.
1. Connecting T1 to S(ω)
If the coupling is transverse to the qubit, e.g., is of the type σx or —see the related case of qubit-qubit coupling treated in Sec. II C—then noise at the qubit frequency can cause transitions between the qubit eigenstates. Since this is a stochastic process, the ensemble-average manifests itself as a decay (usually exponential) of the qubit population toward a certain equilibrium value (usually the qubit ground state for ). Again, this process is equivalently referred to as “T1 relaxation,” “energy relaxation,” or “longitudinal relaxation.” As stated above, T1 is the characteristic time scale of the decay. Its inverse, Γ1 = 1/T1 is called the relaxation rate and depends on the power spectral density of the noise S(ω) at the transition frequency of the qubit ω = ωq
where is the qubit transverse susceptibility to fluctuations δλ, such that is the ensemble average value of the environmental noise sources as seen by the qubit. Equation (53) is equivalent to Fermi's Golden Rule, in which the qubit's transverse susceptibility to noise is driven by the noise power spectral density. The qubit transverse susceptibility can be used to calculate the prefactors; for example, for fluctuations δλ = δn, the relevant term in the transmon Hamiltonian in Eq. (16) is , where we allow for an offset charge ng, and the susceptibility is given by . We refer the reader to Refs. 40, 146, and 147 for more details.
2. Connecting Tφ to S(ω)
If the coupling to the qubit is instead longitudinal, e.g., is of the type σz or , the noise will stochastically modulate the transition frequency of the qubit and thereby introduce a stochastic phase evolution of a qubit superposition state. This gradually leads to a loss of phase information, and it is therefore called pure dephasing (time constant Tφ). Unlike T1 relaxation, which is generally an irreversible (incoherent) error, pure dephasing Tφ is in principle reversible (a coherent error). The degree of pure dephasing depends on the control pulse sequence applied while the qubit is subject to the noise process.
Consider the relative phase φ of a superposition state undergoing free evolution in the presence of noise. The superposition state's accumulated phase
diffuses due to adiabatic fluctuations of the transition frequency,
where is the qubit's longitudinal sensitivity to λ-noise. For noise generated by a large number of fluctuators that are weakly coupled to the qubit, its statistics are Gaussian. Ensemble averaging over all realizations of the Gaussian-distributed stochastic process , the dephasing is
leading to a coherence decay function,
where is a dimensionless weighting function.
The function can be viewed as a frequency-domain filter of the noise [see Fig. 7(a)]. In general, its filter properties depend on the number N and distribution of applied pulses. For example, considering sequences of π-pulses,78,148–152
where is the normalized position of the center of the jth π-pulse between the two π/2-pulses, τ is the total free-induction time, and τπ is the length of each π-pulse,151,152 yielding a total sequence length . As the number of pulses increases for a fixed τ, the filter function's peak shifts to higher frequencies, leading to a reduction in the net integrated noise for 1/fα-type noise spectra with α > 0. Similarly, for a fixed N, the filter function will shift in frequency with τ. Additionally, for a fixed time separation (valid for ), the filter sharpens and asymptotically peaks at as more pulses are added. is thus called the “filter function,”78,150 and it depends on the pulse sequences being applied. From Eq. (57), the pure dephasing decay arises from a noise spectral density that is “shaped” or “filtered” by the sequence-specific filter function. By choosing the number of pulses, their rotation axes, and their arrangement in time, we can design filter functions that minimize the net noise power for a given noise spectral density within the experimental constraints of the experiment (e.g., pulse-modulation bandwidth of the electronics used to control the qubits).
To give a standard example, we compare the coherence integral for two cases: a Ramsey pulse sequence and a Hahn echo pulse sequence. Both sequences involve two π/2 pulses separated by a time τ, during which free evolution of the qubit occurs in the presence of low-frequency dephasing noise. The distinction is that the Hahn echo will place a single π pulse (N = 1) in the middle of the free-evolution period, whereas the Ramsey does not use any additional pulses (N = 0). The resulting filter functions are
where the subscripts N = 0 and N = 1 indicate the number of π-pulses applied for the Ramsey and Hahn echo experiments, respectively. The filter function for the Ramsey case is a sinc-function centered at ω = 0. For noise that decreases with frequency, e.g., 1/f flux noise in superconducting qubits, the Ramsey experiment windows through the noise in S(ω) where it has its highest value. This is the worst choice of filter function for 1/f noise. In contrast, the Hahn echo filter function has a centroid that is peaked at a higher frequency, away from ω = 0. In fact, it has zero value at ω = 0. For noise that decreases with frequency, such as 1/f noise, this is advantageous. This concept extends to larger numbers N of π pulses, and is called a Carr-Purcell-Meiboom-Gill (CPMG) sequence.153,154 In Fig. 7(b), the T2 time of a qubit under the influence of strong dephasing noise is increased toward the 2T1 limit using a CPMG dynamical error-suppression pulse sequence with an increasing number of pulses, N. We refer the reader to Refs. 78, 155, and 156, where these experiments were performed with superconducting qubits.
3. Noise spectroscopy
The qubit is highly sensitive to its noisy environment, and this feature can be used to map out the noise power spectral density. In general, one can map the noise PSD during “free evolution”—periods of time for which no control is applied to the qubit, except for very short dynamical decoupling pulses—and during “driven evolution”—periods of time during which the control fields are applied to the qubit. Both free-evolution and driven-evolution noise is important to characterize, as the noise PSD may differ for these two types of evolution, and both are utilized in the context of universal quantum computation. We refer the reader to Ref. 128 for a summary of noise spectroscopy during both types of evolution.
The Ramsey frequency itself is sensitive to longitudinal noise, and monitoring its fluctuations is one means to map out the noise spectral density over the submillihertz to ∼100 Hz range.127,157
At higher frequencies, the CPMG dynamical decoupling sequence can be used to create narrow-band filters that “sample” the noise at different frequencies as a function of the free-evolution time τ and the number of pulses N. This has been used to map out the noise PSD in the range 0.1–300 MHz.78 One must be careful of the additional small peaks at higher-frequencies, which all contribute to the dephasing used to perform the noise spectroscopy.158
At even higher frequencies, measurements of T1 can be used in conjunction with Fermi's golden rule to map out the transverse noise spectrum above 1 GHz.62,78,160
The aforementioned are all examples of noise spectroscopy during free evolution. Noise spectroscopy during driven evolution was also demonstrated using a “spin-locking” technique, where a strong drive along x or y axes defines a new qubit quantization axis, whose Rabi frequency is the new qubit frequency in the spin-locking frame. The spin-locking frame is then used to infer the noise spectrum while the qubit is continually subject to a driving field. For more information, we refer the reader to Ref. 128.
E. Engineering noise mitigation
Here, we briefly review a few examples of techniques that have been developed to reduce noise or reduce its impact on decoherence (sensitivity). We stress that improving gate fidelity is a comprehensive optimization task, one that is full of trade-offs. It is thus important to identify what the limiting factors are, what price we have to pay to diminish these limiting factors, and what advantage we can achieve until reaching a better trade-off. These all require an accurate understanding the limitations on the gate fidelity, the sources of decoherence, the properties of the noise, and how it affects the system performance.
1. Materials and fabrication improvements
Numerous efforts have been undertaken to reduce noise-induced defects due to materials and fabrication.40,161 In the case of charge noise, significant efforts have been made to reduce the number of defects, such as substrate cleaning,59,162 substrate annealing,163 and trenching.41,61 In the case of flux noise, several groups have performed experiments to characterize the behavior and properties of magnetic-flux defects.133,164,165 More recently, a number of groups have tried optical surface treatments to remove these defects.140
In the context of residual quasiparticles, it has been shown that adding quasiparticle traps to the circuit design can reduce the quasiparticle number, particularly in devices that create excess quasiparticles, such as classical digital logic or operation in the presence of thermal radiation166
2. Design improvements
Another strategy is to reduce qubit sensitivity to the noise by design. A qubit can only lose energy to defects if it couples to them. It has been demonstrated that altering the capacitor geometry to increase the electric-field mode volume reduces the electric field density in the thin dielectric regions that cause loss. This effectively reduces the “participation” of the defects and makes the qubits less sensitive to these noise sources.55,62,130
In another example, the split transmons built using asymmetric junctions have lower sensitivity to flux noise than their symmetric counterparts at the expense of decreased frequency tunability.69 This is a good trade-off to make, because generally one is interested in tuning the qubit frequency over a somewhat restricted range (typically around 1 GHz) about the qubit frequency. When such asymmetric transmons are used in a gate scheme such as the adiabatic -gate,65 (see Sec. IV F) the qubit is less sensitive to flux noise, has a lower dephasing rate, and this should improve the gate fidelity in general.
3. Dynamical error suppression
As introduced in Sec. III D 2, it is advantageous to leverage the 1/ω distribution of flux noise, wherein a considerable amount of the noise power resides at low frequencies, and so the noise is “quasistatic.” The spin-echo technique,115 which disrupts the free evolution by a π-pulse, is extremely effective in mitigating the pure dephasing by refocusing the coherent phase dispersion due to low-frequency noise. The more advanced versions, such as the CPMG-sequence, use multiple π-pulses to interrupt the system more frequently, pushing the filter band to even higher frequencies—a technique known as “dynamical decoupling.”78
Returning to excess quasiparticles, it has been shown that quasiparticles can be stochastically pumped away from the qubit region, resulting in longer, and more stable T1 times.120 Although the pumping technique uses a series of π-pulses, this technique differs from dynamical error suppression of coherent errors in that pulses are stochastically applied, and that it addresses incoherent errors (T1).
4. Cryogenic engineering
In the case of photon shot-noise, in addition to applying dynamical decoupling techniques, there have been several recent studies aimed at reducing the thermal photon flux that reaches the device. This include optimizing the attenuation of the cryogenic setup,106,144,167 remaking the cryogenic attenuators with more efficient heat sinking,143 adding absorptive “black” material to absorb stray thermal photons,168,169 and adding additional cavity filters for thermalization.170
IV. QUBIT CONTROL
In this section, we will introduce how superconducting qubits are manipulated to implement quantum algorithms. Since the transmonlike variety of superconducting qubits has so far been the most widely deployed modality for implementing quantum programs, the discussion throughout this section will be focused on modern techniques for transmons. Nonetheless, the techniques introduced here are applicable to all types of superconducting qubits.
We start with a brief review of the gates used in classical computing as well as quantum computing, and the concept of universality. Subsequently we discuss the most common technique of driving single qubit gates via a capacitive coupling of a microwave line, coupled to the qubit. We introduce the notion of “virtual gates” and “DRAG” pulsing. In the latter part of this section, we review some of the most common implementations of two-qubit gates in both tunable and fixed-frequency transmon qubits. The single-qubit and two-qubit operations together form the basis of many of the medium-scale superconducting quantum processors that exist today.
Throughout this section, we write everything in the computational basis where is the + 1 eigenstate of σz and is the –1 eigenstate. We use capitalized serif-fonts to indicate the rotation operator of a qubit state, e.g., rotations around the x-axis by an angle θ is written as
and we use the shorthand notation “” for a full π rotation about the x axis (and similarly for and ).
A. Boolean logic gates used in classical computers
Universal Boolean logic can be implemented on classical computers using a small set of single-bit and two-bit gates. Several common classical logic gates are shown in Fig. 8 along with their truth tables. In classical Boolean logic, bits can take on one of two values: state 0 or state 1. The state 0 represents logical , and state 1 represents logical .
Beyond the trivial “identity operation,” which simply passes a Boolean bit unchanged, the only other possible single-bit Boolean logic gate is the gate. As shown in Fig. 8, the gate flips the bit: and . This gate is reversible, because it is trivial to determine the input bit value given the output bit values. As we will see, for two-bit gates, this is not the case.
There are several two-bit gates shown in Fig. 8. A two-bit gate takes two bits as inputs, and it passes as an output the result of a Boolean operation. One common example is the gate, for which the output is 1 if and only if both inputs are 1; otherwise, the output is 0. The gate, and the other two-bit gates shown in Fig. 8, are all examples of irreversible gates; that is, the input bit values cannot be inferred from the output values. For example, for the gate, an output of logical 1 uniquely identifies the input 11, but an output of 0 could be associated with 00, 01, or 10. Once the operation is performed, in general, it cannot be “undone” and the input information is lost. There are several variants of two-bit gates, including,
(a combination of and ) and (a combination of and );
(exclusive ) and ( ).
The gate is interesting, because it is a “parity” gate. That is, it returns a logical 0 if the two inputs are the same values (i.e., they have the same parity), and it returns a logical 1 if the two inputs have different values (i.e., different parity). Still, the and gates are not reversible, because knowledge of the output does not allow one to uniquely identify the input bit values.
The concept of “universality” refers to the ability to perform any Boolean logic algorithm using a small set of single-bit and two-bit gates. A universal gate set can in principle transform any state to any other state in the state space represented by the classical bits. The set of gates which enable universal computation is not unique, and may be represented by a small set of gates. For example, the gate and the gate together form a universal gate set. Similarly, the gate itself is universal, as is the gate. The efficiency with which one can implement arbitrary Boolean logic, of course, depends on the choice of the gate set.
B. Quantum logic gates used in quantum computers
Quantum logic can similarly be performed by a small set of single-qubit and two-qubit gates. Qubits can of course assume the classical states and , at the north pole and south pole of the Bloch sphere, but they can also assume arbitrary superpositions , corresponding to any other position on the sphere.
Single-qubit operations translate an arbitrary quantum state from one point on the Bloch sphere to another point by rotating the Bloch vector (spin) a certain angle about a particular axis. As shown in Fig. 9, there are several single-qubit operations, each represented by a matrix that describes the quantum operation in the computational basis represented by the eigenvectors of the σz operator, i.e., and .
For example, the “identity gate” performs no rotation on the state of the qubit. This is represented by a two-by-two identity matrix. The -gate performs a π rotation about the x axis. Similarly, the -gate and -gate perform a π rotation about the y axis and z axis, respectively. The -gate performs a π/2 rotation about the z axis, and the T-gate performs a rotation of π/4 about the z axis. The Hadamard gate is also a common single-qubit gate that performs a π rotation about an axis diagonal in the x–z plane, see Fig. 9.
Two-qubit quantum-logic gates are generally “conditional” gates and take two qubits as inputs. Typically, the first qubit is the “control” qubit, and the second is the “target” qubit. A unitary operator is applied to the target qubit, dependent on the state of the control qubit. The two common examples shown in Fig. 10 are the controlled NOT (-gate) and controlled phase ( or gate). The -gate flips the state of the target qubit conditioned on the control qubit being in-state . The -gate applies a gate σz to the target qubit, conditioned on the control qubit being in-state . As we will show later, the gate—another two-qubit gate—can be built from the -gate and single-qubit gates. The unitary operator of the gate can be written in a useful way, highlighting that it applies an -gate (a σx operator) depending on the state of the control qubit
and similarly for the gate
Comparing the last equality above with the unitary for the [Eq. (62)], it is clear that the two gates are closely related. Indeed, a can be generated from a by applying two Hadamard gates
since . Due to the form of Eq. (63). The gate is also denoted the CZ gate, since it implements a controlled gate (a controlled-σz operation), by analogy with CNOT (a controlled application of the -gate, i.e., the σx operation). Inspection of the definition of in Fig. 10 makes no distinction between which qubit acts as the target and which as the control and, consequently, the circuit-diagram is sometimes drawn in a symmetric fashion
The (with qubit 1 as control and qubit 2 as target) can be realized in terms of the operation and single-qubit Hadamard gates,
Some two-qubit gates such as and are also called “entangling gates,” because they can take product states as inputs and output entangled states. They are thus an indispensable component of a universal gate set for quantum logic. For example, consider two qubits A and B in the following state:
If we perform a gate, , on this state, with qubit A the control qubit, and qubit B the target qubit, the resulting state is (see the truth table in Fig. 10)
which is a state that cannot be factored into an isolated qubit-A component and a qubit-B component. This is one of the two-qubit entangled “Bell states,” a manifestly quantum mechanical state.
A universal set of single-qubit and two-qubit gates is sufficient to implement an arbitrary quantum logic. This means that this gate set can in principle reach “any” state in the multiqubit state-space. How efficiently this is done depends on the choice of quantum gates that comprise the gate set. We also note that each of the single-qubit and two-qubit gates is reversible, that is, given the output state, one can uniquely determine the input state. As we discuss further, this distinction between classical and quantum gates arises, because quantum gates are based on “unitary” operations U. If a unitary operation U is a particular gate applied to a qubit, then its Hermitian conjugate can be applied to recover the original state, since resolves an identity operation.
C. Comparing classical and quantum gates
The gate-sequences used to represent quantum algorithms have certain similarities to those used in classical computing, with a few striking differences. As an example, we consider first the classical gate (discussed previously), and the related quantum circuit version, shown in Fig. 11.
While the classic bit-flip gate inverts any input state, the quantum bit-flip does not in general produce the antipodal state (when viewed on the Bloch sphere), but rather exchange the prefactors of the wavefunction written in the computational basis. The operator is sometimes referred to as “the quantum ” (or “quantum bit-flip”), but we note that X only acts similar to the classical gate in the case of classical data stored in the quantum bit, i.e., for .
As briefly mentioned in Sec. IV B, “all” quantum gates are reversible, due to the underlying unitary nature of the operators implementing the logical operations. Certain other processes used in quantum information processing, however, are irreversible, namely, measurements (see Sec. V for detailed discussion) and energy loss to the environment (if the resulting state of the environment is not known). Here, we will not consider how these processes are modeled, but refer the interested reader to, e.g., Ref. 172, and will only consider unitary control operations throughout the rest of this section. Finally, we note that quantum circuits are written left-to-right (in order of application), while the calculation of the result of a gate-sequences, e.g., the circuit
is performed right-to-left, i.e.,
As discussed in Sec. IV A, the and gates are each individually universal gates for classical computing. Since both of these gates have no direct quantum analog (because they are not reversible), it is natural to ask which gates “are” needed to build a universal quantum computer. It turns out that the ability to rotate about arbitrary axes on the Bloch-sphere (i.e., a complete single-qubit gate set), supplemented with any entangling 2-qubit operation will suffice for universality.172,173 By using what is known as the “Krauss-Cirac decomposition,” any two-qubit gate can be decomposed into a series of operations.172,174
1. Gate sets and gate synthesis
A common universal quantum gate set is
where applies an overall phase θ to a single qubit. For completeness we mention another universal gate set which is of particular interest from a theoretical perspective, namely,
As a technical aside, we mention that the restriction to a discrete gate set still gives rise to universality. This fact relies on using the so-called Solovay-Kitaev175,176 theorem, which (roughly) states that any other single-qubit gate can be approximated to an error ϵ using only (where c > 0) single-qubit gates from . The gate-set is typically referred to as the “Clifford + T” set, where and are all Clifford gates.
Each quantum computing architecture will have certain gates that are simpler to implement at the hardware level than others (sometimes referred to as “native” gates of the architecture). These are typically the gates for which the Hamiltonian governing the gate-implementation gives rise to a unitary propagator that corresponds to the gate itself. We will show several examples of this in Secs. IV E, IV F, and IV G. Regardless of which gates are natively available, as long as one has a complete gate set, one can use the Solovay-Kitaev theorem to synthesize any other set efficiently. In general one wants to keep the overall number of time steps in which gates are applied (denoted the “depth” of a circuit) as low as possible, and one wants to use as many of the native gates as possible, to reduce the amount of time spent for the synthesis. Moreover, running a quantum algorithm also depends on the qubit connectivity of the device. The process of designing a quantum gate sequence that efficiently implements a specific algorithm, while taking into account the considerations outlined above is known as “gate synthesis” and “gate compilation,” respectively. A full discussion of this large research effort is outside the scope of this review, but the interested reader may consult, e.g., Refs. 177–179 and references therein as a starting point. As a concrete (and trivial) example of how gate identities can be used, in Eq. (73) we illustrate how the Hadamard gate from can be generated by two single-qubit gates (from ) and an overall phase gate
As we show in Sec. IV D 1, the gates and are all natively available in a superconducting quantum processor.
We now address the question of how single qubit rotations and two-qubit operations are implemented in transmon-based superconducting quantum processors.
2. Addressing superconducting qubits
The modes of addressing transmonlike superconducting qubits can roughly be split into two main categories: ( Capacitive coupling between a resonator (or a feedline) and the superconducting qubit dipole-field allows for microwave control to implement single-qubit rotations (see Sec. IV D) as well as certain two-qubit gates (see Secs. IV G and IV G 4). ( For flux-tunable qubits, the local magnetic fields can be used to tune the frequency of individual qubits. This allows the implementation of z-axis single-qubit rotation as well as multiple two-qubit gates (see Secs. IV E, IV F, and IV H).
D. Single-qubit gates
In this section, we will review the steps necessary to demonstrate that capacitive coupling of microwaves to a superconducting circuit can be used to drive single-qubit gates. To this end we consider coupling a superconducting qubit to a microwave source (sometimes referred to as a “qubit drive”) as shown in Fig. 12(a). A full circuit analysis of the circuit in Fig. 12(a) is beyond the scope of this review, so here we settle for highlighting the steps that elucidate the physics of the qubit/drive coupling. The interested reader may consult a number of lectures notes and pertinent theses (e.g., Refs. 44, 157, and 180–182). Here we follow Ref. 157.
1. Capacitive coupling for control
We start by modeling the qubit as a harmonic oscillator, for which the (classical) circuit Hamiltonian can be calculated by circuit quantization techniques, starting from Kirchoffs laws, and is given by157
where is the total capacitance to the ground and is a renormalized charge variable for the circuit. We can now promote the flux and charge variables to quantum operators and assume weak coupling to the drive-line, so that , and arrive at
where and we have kept only terms that couple to the dynamic variables. Similar to the momentum operator for a harmonic oscillator in (x, p)–space, we can express the charge variable in terms of raising and lowering operators, as done in Sec. II
where is the zero-point charge fluctuations and is the impedance of the circuit to ground. Thus, the LC oscillator capacitively coupled to a drive line can be written as
Finally, by truncating to the lowest transition of the oscillator, we can make the replacement and throughout and arrive at429
where and .
To elucidate the role of the drive, we move into a frame rotating with the qubit at frequency ωq (also denoted “the rotating frame” or the “the interaction frame”). To see the usefulness of this rotating frame, consider a state . By the time-dependent Schrödinger equation this state evolves according to
where is the propagator corresponding to H0. By calculating, e.g., , it is evident that the phase is winding with a frequency of ωq due to the σz term. By going into a frame rotating with the qubit at frequency ωq, the action of the drive can be more clearly appreciated. To this end, we define and the new state in the rotating frame is . The time-evolution in this new frame is again found from the Schrödinger equation (using the shorthand )
We can think of the term in the parentheses in Eq. (82) as the form of H0 in the rotating frame. Simple insertion shows that as expected (the rotating frame should take care of the time-dependence). However, one could also think of the term in brackets in Eq. (82) as a prescription for calculating the form of any Hamiltonian in the rotating frame given by Urf, by replacing H0 with some other H. In general, we will not find .
Returning to Eq. (78), the form of Hd in the rotating frame is found to be
We can in general assume that the time-dependent part of the voltage () has the generic form
where s(t) is a dimensionless envelope function, so that the amplitude of the drive is set by . Adopting the definitions
the driving Hamiltonian in the rotating frame takes the form
Performing the multiplication and dropping fast rotating terms that will average to zero (i.e., terms with ωq + ωd), known as the rotating wave approximation (RWA), we are left with
where . Finally, by reusing the definitions from Eq. (85), the driving Hamiltonian in the rotating frame using the RWA can be written as
Equation (90) is a powerful tool for understanding single-qubit gates in superconducting qubits. As a concrete example, assume that we apply a pulse at the qubit frequency, so that δω = 0, then
showing that an “in-phase” pulse (ϕ = 0, i.e., the I-component) corresponds to rotations around the x-axis, while an out-of-phase pulse (ϕ = π/2, i.e., the Q-component), corresponds to rotations about the y-axis. As a concrete example of an in-phase pulse, writing out the unitary operator yields
which depends only on the macroscopic design parameters of the circuit as well as the envelope of the baseband pulse s(t) and amplitude V0, which can both be controlled using arbitrary waveform generators (AWGs). Equation (92) is known as “Rabi driving” and can serve as a useful tool for engineering the circuit parameters needed for efficient gate operation (subject to the available output voltage V0). To see this, we define the shorthand
which is the angle by which a state is rotated given the capacitive couplings, the impedance of the circuit, the magnitude V0, and the waveform envelope, s(t). This means that to implement a π-pulse on the x-axis, one would solve the equation Θ(t) = π and output the signal in-phase with the qubit drive. In this language, a sequence of pulses [see Fig. 13(a)] is converted to a sequence of gates operating on a qubit as
where is an operator that ensures the pulses are generated in the time-ordered sequence corresponding to .
In Fig. 13, we outline the typical in-phase and quadrature (IQ) modulation setup used to generate the pulses used in Eq. (94). Figure 13(a) shows how a pulse at frequency ωd is generated using a low phase-noise microwave generator [typically denoted “the local oscillator (LO)”], while the pulse is shaped by combining the LO signal in an IQ mixer with pulses generated in an AWG. To allow for frequency multiplexing, the AWG signal will typically be generated with a low-frequency component, ωAWG, and the LO signal will be offset, so that . By mixing in more than one frequency it is possible to address multiple qubits (or readout resonators) simultaneously, via the superposition of individual drives.
The I (Q) input of the IQ mixer will multiply the baseband signal to the in-phase (out-of-phase) component of the LO. In Fig. 13(b), we schematically show the comparison between XY gates in a quantum circuit and the corresponding waveforms generated in the AWG (omitting for clarity the frequency ωAWG component). The inset in Fig. 13(b) shows an example of a gate on the Bloch sphere, with the indication of (I, Q) axes. More sophisticated and compact approaches exist to reduce the hardware needed for XY qubit control, relative to the setup shown in Fig. 13, see, e.g., Refs. 183–185.
2. Virtual gate
As we saw in Sec. IV D, the distinction between x– and y–rotations was merely a choice of phase on the microwave signals, and the angle to be rotated is given by Θ(t), both of which are generated using an AWG. Since the choice of phase ϕ has an arbitrary starting point, we could consider . This would lead to and . Therefore, changing the phase effectively changes rotations around x to rotations around y (and vice-versa, with a change of sign). This is reminiscent of the result of applying a rotation to x– and y–rotations, where and . This analogy between shifting a phase of an AWG-generated signal and applying rotations can be utilized to implement “virtual” gates.186 As shown by McKay et al., this intuition can be formalized via the following example: consider the case of applying a pulse with an angle θ on the I channel (i.e., a ) followed by another θ pulse on the I channel, but with a phase relative to the first pulse (denoted , where indicates we still use the I channel, but the rotation axis is now an angle ϕ0 away from the x-axis). Using Eq. (94) corresponds to a pulse sequence
from which we see that the effect of the offset phase ϕ0 is to apply . The equality above can be verified with a little trigonometric footwork. The final is due to the rotation being in the frame of reference of the qubit. However, since the readout is along the z-axis (see Sec. V), a final phase rotation about z will not change the measurement outcome. Thus, if one wants to implement the gate sequence
where Ui's are arbitrary gates, this can be done by revising the gate sequence (in the control software for the AWG) and changing the phase of subsequent pulses
which reduces the number of overall gates. Moreover, the virtual- gates are “perfect,” in the sense that no additional pulses are required, and the gate takes “zero time,” and thus the gate fidelity is nominally unity. As we show in Secs. IV E and IV F, operation of two-qubit gates can incur additional single-qubit phases. Using the virtual- strategy, these phases can be canceled out, leaving a pure two-qubit interaction.
Finally, we mention one more salient feature of the virtual- gates. As shown in Ref. 63, any single-qubit operation (up to a global phase) can be written as
for appropriate choice of angles . This means that access to a single physical combined with the virtual- gives access to a complete single qubit gate set! An explicit example of Eq. (99) in action is the Hadamard gate, which can be written as , but since the 's can be virtual, it is possible to implement Hadamards as an effective single pulse operation in superconducting qubits.
3. The DRAG scheme
In going from Eq. (77) to Eq. (78), we assumed we could ignore the higher levels of the qubit. However, for weakly anharmonic qubits, such as the transmon (see Sec. II), this may not be a justified assumption, since only differs from by the anharmonicity, , which is negative and typically around 200 to 300 MHz. This situation is sketched in Figs. 14(a)–14(c), where we illustrate how Gaussian pulses with standard deviations ns have spectral content that leads to nonzero overlaps with the frequency. This leads to two deleterious effects: (1) leakage errors which take the qubit out of the computational subspace, and (2) phase errors. Effect 1 can occur because a qubit in the state may be excited to as a π pulse is applied, or be excited directly from the , since the qubit spends some amount of time in the state during the π pulse. Effect 2 occurs because the presence of the drive results in a repulsion between the and levels, in turn changing as the pulse is applied. This leads to the accumulation of a relative phase between and .188 The so-called DRAG procedure189–191 (Derivative Reduction by Adiabatic Gate) seeks to combat these two effects by applying an extra signal in the out-of-phase component. The trick is to modify the waveform envelope s(t) according to
where λ is a dimensionless scaling parameter, and λ = 0 corresponds to no DRAG pulse and is the time derivative of s(t). The theoretically optimal choice for reducing dephasing error is λ = 0.5 and an optimal choice for reducing leakage error is λ = 1.190,192 Interchanging I and Q in Eq. (100) corresponds to DRAG pulsing for the Q component.
In practice, there can be a deviation from these two optimal values, often due to pulse distortions in the lines leading to the qubits. Typically, randomized benchmarking experiments combined with single-shot measurements (see Sec. V) of the state are used to determine the optimal value of λ. The trade-off was demonstrated explicitly in Refs. 186 and 193. However, by extending the original DRAG pulse implementation,194,195 it is possible to reduce “both” errors “simultaneously.” By introducing a frequency detuning parameter δf to the waveform190 (defined such that δf = 0 corresponds to the qubit frequency), i.e.,
and choosing λ to minimize leakage errors, then phase errors can be reduced simultaneously.193 Similarly, by a judicious use of the virtual- gate, it is also possible to reduce phase errors in combination with DRAG pulsing to reduce leakage.186 Modern single-qubit gates using DRAG pulsing now routinely reach fidelities .65,67,193,196–199 Other techniques also exist for operating single-qubit gates in a spectrally crowded device.200,201
E. The two-qubit gate in tunable qubits
As briefly mentioned in Sec. IV C, single-qubit gates supplemented with an entangling two-qubit gate can form the gate set required for universal quantum computation. The two-qubit gates available in the transmonlike superconducting qubit architecture can roughly be split into two broad families as outlined previously: one group requiring local magnetic fields to tune the transition frequency of qubits and one group consisting of all-microwave control. There exist several hybrid schemes that combine various aspects of these two categories and, in particular, the notions of tunable coupling and parametric driving are proving to be important ingredients in modern superconducting qubit processors.63,67,89,103,105,106,202–207 In this section, however, we start by introducing the gate, and then review the (controlled-phase) in Sec. IV F and the (cross-resonance) in Sec. IV G. We briefly review a few other two-qubit gates and discuss their merits in Secs. IV G 4 and IV H.
1. Deriving the unitary
where g is the coupling strength and ⊗ is used to emphasize the tensor product. If the capacitive coupling is mediated through a bus resonator, then208,209
where gi is the resonator coupling to qubit i (dependent on the qubit-resonator coupling capacitance ) and is the detuning of qubit i to the resonator. In the simpler case where the qubits are directly coupled210
where is the qubit-qubit coupling capacitance and Ci is the capacitance of qubit i. Throughout this section, we will assume a direct capacitive coupling between qubits of the flux-tunable transmon type, so that and . For simplicity, we suppress the explicit flux dependence of the 's and simply refer to the coupling as g. Equation (102) can be rewritten as
and then using the rotating wave approximation again (i.e., dropping fast rotating terms) we arrive at
where we have introduced the notation and suppressed the explicit tensor product between qubit subspaces. If we now change the flux of qubit 1 to bring it into resonance with qubit 2 (), then
The first part of Eq. (107) shows that a capacitive interaction leads to a swapping of excitations between the two qubits, giving rise to the “swap” in . Moreover, due to the last part of Eq. (107), this capacitive coupling is also sometimes said to give rise to an “XY” interaction.211 The unitary corresponding to a XY (swap) interaction is
Since the qubits are tunable in frequency, we can now consider the effect of tuning the qubits into resonance for a time
From this result, we see that a capacitive coupling between qubits turned on for a time (inversely related to the coupling strength in units of radial frequency) leads to implementing a so called “” gate,209,210,212–215 which acts to swap an excitation between the two qubits, and add a phase of . For completeness, we note that for , the resulting unitary
is typically referred to as the “squareroot-” gate. The gate can be used to generate Bell-like superposition states, e.g., .
To elucidate the operating principle behind an implementation, we show the spectrum of a flux-tunable qubit using typical transmonlike parameters in Fig. 15(a). The is performed at the avoided crossing, where . By preparing QB1 in-state , moving into the avoided crossing, waiting there for a time τ [see pulse-sequence in inset in Fig. 15(b)], the excitation is swapped back and forth between the two qubits, as shown in Fig. 15(b). In Fig. 15(c), we plot the linecuts of (b) at , showing the excitation oscillating back and forth between and with the predicted time . In turn, the frequency of the oscillation can be used to extract the strength of the coupling, .
So far, we have ignored the role of the single-qubit phases acquired by tuning the qubit frequency. Referring to the pulse-sequence shown in the top panel of Fig. 15(a), we see that each qubit will acquire a phase given by
This phase can be conveniently removed either by subsequent application of virtual- gates to all following pulses,186 or by shaping the waveform of the excursion such that single-qubit phases are exactly canceled.216
Equations (104) and (108) together present a useful result from a quantum processor design perspective: The operating regime, frequency and time τ of the can be calculated (typically simulated) to a high precision, before any processor fabrication is undertaken. The only “quantum” parts that enter (and ) are the qubit frequencies, and . If the Josephson energies of the qubits are known (which they typically are, from fabrication parameters), then by simulating the capacitances in or , the time τ and the pulseshape needed to implement an can be estimated to high precision. Typical values of the coupling strength, g/(2π), for architectures using the gate are 5–40 MHz and are often very close to expectations from EM simulations.213,215–217
2. Applications of the gate
The cannot generate a gate by itself. Rather, to implement a gate requires stringing together two s and several single qubit gates211
As evident from Eq. (112), the gate in general needs to be used twice to generate a single , leading to a significant overhead when compiling –dense circuits from gates. However, depending on the context, the can be used efficiently (i.e., without any two-qubit gate overhead) to mimic the behavior of a . Typically such circuits will not be completely equivalent, but will share certain salient features for specified input states. As an example of this procedure, Neeley et al.214 demonstrated the generation of a 3-qubit Greenberger-Horne-Zeilinger (GHZ) state (which requires two subsequent s in the simplest construction), by using only two s in a circuit that correctly generates the 3-qubit GHZ state on the input. Moreover, the XY–interaction is a powerful tool for certain types of quantum simulation algorithms.218 If one is interested in digital quantum simulation of spinlike systems, then the XY–interaction can natively simulate, e.g., a Heisenberg interaction
This approach to the XY interaction was demonstrated by Salathé et al.,216 where repeated application of the gate interspersed with single-qubit rotations was used to generate successive XY, XZ and YZ interactions that lead to an aggregate Hamiltonian. State-of-the-art operation of the gate has also been used to demonstrate a ten-qubit GHZ state.219
F. The two-qubit gate in tunable qubits
In our discussion of the gates, we assumed that the higher energy levels of the superconducting qubit do not play a role. As we show below, it turns out that for the case of transmon qubits (with negative anharmonicity), the higher levels can in fact be utilized to generate a the gate directly.64,220
Recall from Sec. IV C that the gate implements the following unitary:
Our goal for the remainder of this section is to show that the unitary operator of the gate appears naturally for capacitively coupled transmon superconducting qubits and review a few of the modern applications of this gate. We have chosen to include a considerable amount of details for the implementation of this gate, as a means to review some of the issues one has to resolve, to engineer high quality two-qubit gates.
The structure of the matrix in Eq. (63) indicates that we need to apply a phase () to the qubits whenever both are in the excited state . Considering the nature of the XY interaction, which couples and leads to the gate (see Sec. IV E), we expect avoided level crossings to exist between higher levels, e.g., and . The flux-tunable implementation of the gate relies on this higher-level avoided crossing.
To motivate this intuition, we plot the spectrum for two coupled transmon qubits, in Fig. 16(a), including levels with two excitations, as the local magnetic flux in qubit 1 is being tuned. The Hamiltonian for this spectrum, written in the -basis, is approximately given by
where and is the flux-dependent energy of the i-th level of a transmon,52 and the transitions are scaled by a factor due to the higher photon number. In Fig. 16, we plot the frequencies calculated from Eq. (115), using standard, symmetric, transmonlike parameters, as the local magnetic field of qubit 1 is increased.
The result of the higher levels on the computational basis can be understood by considering a concrete example. By preparing the combined qubit state and moving slowly toward the avoided crossing between and at , waiting for some time τ and moving back [see black line with arrows in Fig. 16(b)], the resulting unitary operator in the computational basis is given by
is the phase acquired by the state along the trajectory ℓ in (Φ, t)-space during time τ. The movement should be sufficiently slow on a time scale set by g that the moving state never populates the state, i.e., the movement should be adiabatic. In terms of applied flux, the avoided crossing between the state happens before (due to the negative anharmonicity of the transmons, ) and consequently ℓ does not take the states through the operating point. As shown in Fig. 16(b), we can define a parameter (typically denoted ζ) quantifying the difference in phase acquired by the relative to the single excitation states
The parameter ζ can be thought of as the result (in the computational space) of the repulsion of due to the state. If we now choose a trajectory , designed so that , then
Inserting this expression into Eq. (116), we see that
After the adiabatic excursion, one can now apply single-qubit pulses (or use virtual- gates) to exactly cancel the single-qubit phases such that . This changes Uad to
From Eq. (121), it is evident that an adiabatic movement of , followed by single-qubit gates (virtual or real) efficiently implements a and, through Eq. (66), also efficiently implements a . The gate is one of the workhorses of modern superconducting qubit processors with gate fidelities .65,221
One is, of course, free to choose an arbitrary trajectory that implements the phase on the state. Assuming that the single-qubit phases are properly canceled, one sees that the arbitrary phase version of the gate (typically denoted ) can be written as
Because of the form of Eq. (122), one can think of the avoided crossing with the higher levels outside the computational subspace as giving rise to an effective coupling within the computational subspace.220
An alternative to the adiabatic approach outlined above to realize is to make a sudden excursion to the operating point, after waiting a time , the state will have completed a single Larmor-type rotation from to and back again to , but in the process, acquired an overall π phase, similar to the gate, but in the subspace.54 In fact, such excursions near or through avoided crossings leading to adiabatic and nonadiabatic transitions have been studied extensively in the context of interferometry, cooling, spectroscopy, and quantum control.117,222–231
The remainder of this subsection is devoted to an overview of some of the recent advances and demonstrations using the gate since its first demonstration in 2009 where it was used to generate Bell-states and demonstrate two-qubit algorithms.64
1. Trajectory design for the gate
The (adiabatic) implementation of outlined above assumed that the trajectory was completely adiabatic and that the state never left the computational subspace. Since the fidelity of gates is bounded from above by the coherence times of the qubits, short gate times are desirable.232 This presents a tension for optimally operating the gate—fast operation in conjunction with the need for adiabatic operation. A relevant question is then: what is the “optimal” trajectory that implements the necessary phase as fast as possible, with as little leakage as possible, for a given size of the avoided crossing between and ? Given a typical coupling rate MHz (as discussed in Sec. IV E), one expects a heuristic lower time limit to be ns (stronger coupling of course leads to shorter gate times, but will limit the on/off ratio of the gate). Traditional optimal control of adiabatic movement assumes the movement is “through” the avoided crossing (see, e.g., Ref. 233), but the trajectory moves close to and then back from the avoided crossing. This modification to the adiabatic movement protocol was addressed by Martinis and Geller,234 specifically in the context of errors for a gate implementation. The authors show that nonadiabatic errors can be minimal for gate times only slightly longer than using an optimal waveform (based on a Slepian waveform235) to parametrize the trajectory .
2. The gate for quantum error correction
Using the approach of Martinis and Geller, Barends et al. were able to demonstrate a two-qubit gate fidelity (determined via a technique known as “interleaved randomized benchmarking”236–239). This implementation had a gate time τ = 43 ns and was implemented with the waveform,65 in an “xmon” device85−a transmon with a “+”-shaped capacitor. A two-qubit gate fidelity represents a significant milestone, not just from a technical and engineering perspective, but also from a foundational standpoint: The surface code (a quantum error correcting code) has a lenient fault-tolerance threshold of .240–242 This means, roughly speaking, that if the underlying operations on the qubits have fidelities , then by adding more qubits to the circuit (and correctly implementing the fault-tolerant quantum error correction protocol) the overall error-rate can be reduced, and one can in principle perform arbitrarily long quantum computations, without errors spreading uncontrollably and corrupting the calculation. Because of its relatively lenient threshold under circuit noise (compared to, e.g., Steane or Shor codes172,243,244) and its use of solely nearest-neighbor coupling, the surface code is one of the most promising quantum error correction codes for medium-to-large scale quantum computing in solid state systems.240 Therefore, surpassing the fault-tolerance threshold using represents a significant milestone for the field.245 Moreover, practical blueprints for implementing scalable subcells of the surface code using the as the fundamental two-qubit gate have also been proposed71 as well as in-situ calibration protocols for large-scale systems operating with .246 For a full review of the pros and cons of various quantum error correcting codes we refer the interested reader to, e.g., an introductory review article Ref. 247, or any of the excellent textbooks and more detailed review articles in Refs. 172, 174, 244, and 247–250.
Returning to the gate, numerical optimization of was demonstrated by Kelly et al.221 using the interleaved randomized benchmarking sequence fidelity as a cost function to push a native implementation of with a fidelity up to , surpassing the surface code fault tolerance threshold. In the same work that demonstrated , Barends et al.65 used the gate to generate GHZ states, , of up to N = 5 qubits, with a fidelity for the N = 5 state of . The protocol for generating the GHZ state with N = 2 and N = 3 from was originally demonstrated by DiCarlo et al..54,64 The textbook route to generating the N = 2 GHZ state, (a Bell state) from the all-zero input is
An equivalent circuit using and native single-qubit gates in superconducting qubits is
By repeating the operation inside the dashed box on additional qubits, an N-qubit GHZ state can be generated.65 Since the demonstration of the N = 5 GHZ state using the gate, the gate has been deployed to demonstrate several important aspects of quantum information processing using superconducting qubits. A nine-qubit implementation of the five-qubit repetition code (five data qubits + four syndrome qubits)247 was demonstrated, and the error suppression factor of a single logical quantum bit was shown to increase as the encoding was changed from three data qubits to five data qubits.66 Similarly, in a five qubit processor the three-qubit repetition code with artificially injected errors was demonstrated,251 building on earlier results utilizing a combination of and gates to perform parallelized stabilizer readout.252
3. Quantum simulation and algorithm demonstrations using
As an example of the utility of the gate, we briefly discuss a particular demonstration of a digital quantum simulation. In this context, the gate has been utilized to simulate a two-site Hubbard model with four fermionic modes, using four qubits.253 Using the Jordan-Wigner transformation,254,255 it is possible to map fermionic operators onto Pauli spin matrices.254 As shown in Ref. 253, a Hubbard model with two fermionic modes, whose Hamiltonian is given by
can be written in terms of Pauli operators as
where U is the repulsion energy and t is the hopping strength. Similar to the Heisenberg interaction discussed briefly in Sec. IV E, it is now a question of producing -type interactions, where the prefactor α can be tuned. Using the version of , a unitary can be generated via
where is used to allow for small and negative angles. Finally, for completeness, we mention an alternative approach to creating , given by42,256
which has the benefit of relying on (through the s), and the angle can be controlled using the single-qubit gates. We refer the interested reader to two reviews on quantum simulations, see, e.g., Refs. 257 and 258.
The gate has also been used in a variety of other contexts, e.g., for calculating the dissociation of diatomic hydrogen (H2) using the variational quantum eigensolver method,259 for feed-forward based teleportation experiments,260,261 as well as initial steps toward demonstrating quantum supremacy262 and a 2 × 2 implementation of the Harrow-Hassidim-Lloyd algorithm263,264 In the field of hybrid semiconducting nanowire/superconducting qubits (known as the “gatemon” approach265–267), where the qubit frequency is modified by electrostatically changing the density of carriers in a semiconducting region with proximity-induced superconductivity, the gate was also demonstrated between two nanowire qubits.268
One may worry that operating a qubit by moving its frequency can lead to overlap with frequencies already used by other qubits, in a system with multiple qubits. This issue is known as “frequency crowding.” While the use of asymmetric transmons [with two sweet spots in the range , recall that Fig. 2(c)] may help alleviate some frequency crowding issues, a more long-term strategy is needed. One way to circumvent the problem is to utilize on/off tunable coupling schemes, in which qubits can exchange energy only if a coupler activates the interaction.63,103 To address this issue in the context of the gate, Chen et al.103 demonstrated a device (named “the gmon”) where the qubit interaction can be tuned with an on/off ratio on the order of 1000, and a gate fidelity of was demonstrated.
This concludes the introduction to the physics and operation of the gate in its native form. In the remainder of this section, we will introduce a few of the microwave-only gates that have been demonstrated in an effort to sidestep the need for local tunability (and the resulting increased sensitivity to noise) as required by the and gate.
G. Two-qubit gates using only microwaves
One common (potential) drawback for the and gates is that their operation requires flux-tunable qubits. Introducing a new control knob, such as flux control, in turn also introduces a new noise channel for the system. Furthermore, the need for flux-tunability increases the sensitivity of the devices to flux noise by tuning the qubits from their “sweet spots,” increases the dephasing rate. From this perspective, one could envision using all-microwave-based gates to remedy these issues. To this end, the cross-resonance (“”) gate was developed for operating fixed-frequency superconducting qubits,269–271 which typically feature longer lifetimes and reduced sensitivity to flux noise.
1. The operational principle of the gate
To elucidate the operation of the gate, we briefly revisit the driving Hamiltonian derived in Sec. IV D. There, we considered only a single qubit. However, if one extends this formalism to two qubits, see Fig. 17(a) denoting the frequency difference by and the coupling by , and performing a Schrieffer-Wolff transformation to go to the dressed state picture, the driving Hamiltonians for qubit 1 and 2 become270,272
and is the driving for qubit i. From Eq. (130), it is evident that if we drive qubit 1 at the frequency of qubit 2, then to qubit 2, this will look like a combination of and . This means that the Rabi oscillations of qubit 2 will have a frequency given by
where , and z1 depends on the state of qubit 1. This effect is demonstrated in Fig. 17(c), where a simulated drive is applied to qubit 1 while the resulting Rabi oscillations in qubit 2 are recorded. We have used typical fixed-frequency transmon parameters from experiments, and we have included a spurious cross-talk term .239,273 In Fig. 17(d), we plot the difference in angle in the (z, y) plane acquired by qubit 2 for different initializations of qubit 1, . For this particular choice of parameters, the cross-resonance gate achieves a π-phase shift in ≈200 ns.
This strategy was first demonstrated using flux-tunable transmons in Ref. 274, where a Bell state with fidelity was achieved. Using quantum process tomography, the gate fidelity was found to be . By moving to fixed-frequency qubits with increased lifetimes, the gate fidelity was increased to (with subtraction of state initialization and measurement errors).273 For completeness, we note that due to the form of the last term in Eq. (130), the gate is also sometimes denoted the gate. The unitary matrix representation of the gate is
where , which can be used to generate a with the addition of only single-qubit gates
up to a phase .
2. Improvements to the gate and quantum error correction experiments using
Since qubit 1 is being driven off-resonance, an ac-Stark shift will add a term to the driving Hamiltonian of qubit 1. The effect of both the spurious ac Stark shift and the direct single-qubit rotations was studied in Ref. 239. By modifying the original protocol to effectively “echo away” the two unwanted contributions from the and terms, the fidelity of the gate was improved to ,239 using quantum process tomography. Using interleaved randomized benchmarking of this improved “echo-”-gate (e ), a gate fidelity of was achieved. This gate implementation was used to demonstrate two-qubit parity measurements in a three-qubit device,275 as well as detecting bit-flip and phase-flip errors in a Bell state encoded in a four-qubit device,276 with gate fidelities from interleaved randomized benchmarking in the range 0.94 to 0.96. Using a similar device, but with five qubits, weight-four parity measurement of the forms ZZZZ and XXXX were demonstrated,277 where the crosstalk to qubits not involved in the gates was studied, leading to the development of a four pulse scheme.
Based on improvements in the analysis of the Hamiltonian describing the drive, Sheldon et al.197 subsequently demonstrated a version of the CR which reduced the gate time to τ = 160 ns and added an active cancelation tone to the e previously developed. Using this “active cancelation echo ” (ace ), the fidelity was increased to , measured with interleaved randomized benchmarking. The same sequence without active cancelation on the same qubits yielded . The interested reader may consult the followup theoretical work278 with more details on the effective Hamiltonian models. Other approaches to fast, high-fidelity cross-resonance gates have also been proposed.279 This series of improvements to the original cross-resonance implementation has increased the gate fidelity to beyond the threshold for fault-tolerance in a surface code, with similar quality to the gate. Although improvements should still be made, with the advent of the gate, superconducting qubit based quantum computing platforms now offer two entangling two-qubit gates that can be used for implementing surface-code based error correction schemes.
In the initial experiments using gates, the gate times were significantly longer than the typical gate times (–60 ns and –400 ns), which to a large extent accounts for the observed gate fidelities. The time scale for operation is set by the frequency detuning, the anharmonicity, and the coupling strength, through Eq. (132). This has the unfortunate drawback that if qubits do not have the intended frequencies (due to fabrication variation), they will be immediately manifested as longer gate times, and in turn, reduced gate fidelity. As fabrication techniques are becoming more sophisticated and reliable, this problem may be of reduced importance. However, since the coupling in the scheme is always on, there is an inherent tension between well-isolated qubits for high-fidelity single-qubit operations, and coupling qubits, for fast/high-fidelity two qubit gates.
3. Quantum simulation and algorithm demonstrations with the gate
Since the form of the Hamiltonian () is not a -type interaction (leading to gate) nor is it an the effective -type (leading to gate), one could question its applicability to quantum-simulation-type experiments, which often involves terms of the form . However, by developing a variational quantum eigensolver routine that efficiently generates entangled trial states using just the interaction, Kandala et al.280 calculated the ground-state energy for H2, LiH, and BeH2. This experiment was performed on six fixed-frequency qubits, and it employed a technique for compact encoding of the Hamiltonians corresponding to each molecule.281 As of this writing, this experiment represents the largest molecule for which the ground state has been found using a purely quantum processing approach.
The gate is also the native two-qubit gate available on the IBM Quantum Experience quantum processor,282 which is accessible online. Using the IBM Quantum Experience processor, Takita et al.283 demonstrated an implementation of a two-logical-qubit (four physical qubit) error detection code.284 The implementation was inspired by the proposal of Gottesman,285 which proposed a minimal experiment to claim observation of fault-tolerant encodings,248 using a four qubit error detection code in a five qubit setup. Due to constraints on the connectivity, the work by Takita et al. demonstrated a modified version of the Gottesman encoding, in which two logical qubits are initialized, but only one of them in a fault-tolerant manner. By artificially injecting an error in the state preparation circuit, the authors demonstrate that the probability of correctly preparing a fault tolerant state is greater than the probability of correctly preparing a non-fault-tolerant qubit. This behavior is consistent with expectations for how fault-tolerant encodings work. Simultaneously, Vuillot286 also used the IBM Quantum Experience machine to study fault-tolerant schemes encoded in that connectivity.
Beyond the applications to error-correction and error-detection, the cross-resonance gate has also been employed in early demonstrations of quantum advantages in machine learning. Risté et al.287 studied the so-called “learning parity with noise” problem, in which one attempts to learn a bit-string k by querying an oracle function with a user-input bit-string . In a first implementation of this problem, the authors show that for a specific instance of the bit-string , a learner with access to quantum operations needs fewer queries to the function f. However, by extending the model of learning parity with noise, the authors demonstrated a consistent advantage of the learner with access to quantum operations.287
The gate was also used to demonstrate the implementation of a supervised learning algorithm where the feature space is encoded as quantum data on the Bloch sphere.256 In typical supervised learning, an algorithm is exposed to a training set of labeled data, and is subsequently asked to classify a new, unlabeled set of data.288 In the support vector machine (SVM) approach to such problems, the data is then mapped nonlinearly onto the so-called “feature space,” in which the trained algorithm has constructed a separating hyperplane to classify the data. While a full “quantum Support Vector Machine” proposal exists, the algorithm assumes that the data are already present in a coherent superposition.289 Instead, Havlicek et al.256 proposed, and demonstrated, that mapping the classical data nonlinearly onto the Bloch sphere can also be utilized to provide a quantum advantage. For a wider discussion of the important role of quantum data in many quantum machine learning algorithms, the reader is referred to Ref. 290.