Abstract
We argue that a complete description of quantum annealing implemented with continuous variables must take into account the nonadiabatic AharonovAnandan geometric phase that arises when the system Hamiltonian changes during the anneal. We show that this geometric effect leads to the appearance of nonstoquasticity in the effective quantum Ising Hamiltonians that are typically used to describe quantum annealing with flux qubits. We explicitly demonstrate the effect of this geometric nonstoquasticity when quantum annealing is performed with a system of one and two coupled flux qubits. The realization of nonstoquastic Hamiltonians has important implications from a computational complexity perspective, since it is believed that in many cases quantum annealing with stoquastic Hamiltonians can be efficiently simulated via classical algorithms such as Quantum Monte Carlo. It is well known that the direct implementation of nonstoquastic Hamiltonians with flux qubits is particularly challenging. Our results suggest an alternative path for the implementation of nonstoquasticity via geometric phases that can be exploited for computational purposes.
Introduction
It is well known that the solution of computational problems can be encoded into the ground state of a timedependent quantum Hamiltonian. This approach is known as adiabatic quantum computation (AQC),^{1,2,3} and is universal for quantum computing^{4} (for a review of AQC, see ref. 5). Quantum annealing (QA) is a framework that incorporates algorithms^{6,7,8} and hardware^{9,10,11,12,13} designed to solve computational problems via quantum evolution toward the ground states of final Hamiltonians that encode classical optimization problems, without necessarily insisting on universality or adiabaticity.
QA thus inhabits a regime that is intermediate between the idealized assumptions of universal AQC and unavoidable experimental compromises. Perhaps the most significant of these compromises has been the design of stoquastic quantum annealers. A Hamiltonian H is stoquastic with respect to a given basis if H has only real nonpositive offdiagonal matrix elements in that basis. This means that its ground state can be expressed as a classical probability distribution,^{14, 15} and that the standard quantumtoclassical mapping^{16} used in quantum Monte Carlo algorithms does not result in a “sign problem” (a partition function with Boltzmann weights taking positive as well as unphysical negative, or even complex values).^{14, 15, 17,18,19,20,21} The computational power of stoquastic Hamiltonians has been carefully scrutinized, and is suspected to be limited in the groundstate AQC setting.^{5} For example, it is unlikely that groundstate stoquastic AQC is universal.^{22} In many cases groundstate stoquastic AQC can be efficiently simulated by classical algorithms, but certain exceptions are known.^{23, 24}
One is thus naturally motivated to consider a departure from the stoquastic setting. Indeed, this is the setting of proofs of the universality of AQC and of various specific results that use nonstoquasticity to improve upon the performance of a stoquastic Hamiltonian.^{25,26,27,28,29,30,31} For example, it is known that nonstoquastic interactions that are turned on temporarily during QA can modify the annealing path so that it encounters a polynomially small gap rather than an exponentially small one, by replacing a firstorder quantum phase transition by a secondorder one.^{31} Note that (non)stoquasticity is a global property of the entire Hamiltonian (since once can always change the stoquasticity of a local term by a local change of basis), and henceforth when we—with slight abuse of language—refer to (non)stoquastic interactions (or terms) we have in mind a fixed basis. Motivated by experimental considerations, we shall focus on the computational basis, i.e., the basis in which the final Hamiltonian is diagonal.
Introducing nonstoquastic interactions is especially important in the physical implementation of QA devices, in order to allow them to escape the trap of efficient classical simulability. The DWave devices,^{10,11,12} e.g., can only implement the Hamiltonian of the quantum transverse field Ising model, which is stoquastic. To remedy this would require additional couplings between the flux qubits, to realize, e.g., σ ^{x} ⊗ σ ^{x} interactions, in addition to the existing σ ^{z} ⊗ σ ^{z} interactions. However, the implementation of a scalable architecture with such interactions is highly demanding, at least with superconducting flux qubits.
Rather than attempt to introduce nonstoquasticity via new components, here we revisit the assumptions that lead to the derivation of the Hamiltonian generating the effective time evolution of a continuousvariable system, such as inductively coupled flux qubits. We show that nonstoquastic terms arise naturally as a nonadiabatic geometric phase, due to the AharonovAnandan effect,^{32, 33} when QA is performed in systems of inductively coupled flux qubits. The geometric effect discussed here is distinct from earlier work on holonomic quantum computation,^{34,35,36} and nonadiabatic geometric phases in quantum computation using Josephson devices.^{37, 38}
We study these geometric terms in detail, and show that the geometric effect is amplified in proportion to the inverse gap of the flux Hamiltonian, appearing and then disappearing during the anneal. The geometric effect can thus be considered as a type of nonstoquastic catalyst,^{5} with the potential to lead to quantum speedups.^{25,26,27,28,29,30,31} From a more general experimental perspective, the presence of the geometric terms studied in this paper has clear signatures that must be taken into account in the validation and characterization of current and future QA devices.
Results
General formalism and the geometric term
For concreteness, we assume that QA is performed by implementing a timedependent Hamiltonian that can be generically expressed as follows:
Such Hamiltonians can be realized with superconducting flux qubits,^{39} in which case the continuous variables \(\phi = \left\{ {{\phi _i}} \right\}_{i = 1}^n\) are the magnetic fluxes trapped by the n flux qubits, and E _{C} represents a charging energy. The term P(ϕ, t) is a timedependent potential that controls both the anneal and the interactions between qubits. We assume that the lowest 2^{n} energy levels of the Hamiltonian (1) are separated from the rest of the spectrum by an energy gap. For a slowly variable potential P(ϕ, t), the system of Eq. (1) is then confined to an N = 2^{n} dimensional Hilbert space \({{\cal H}_N}(t)\).
To proceed, we follow the approach introduced by Anandan in the discussion of nonadiabatic nonAbelian geometric phases.^{33} We first consider the timeevolved Ndimensional subspace \({{\cal H}_N}(t)\), which is defined by the map
where \(U(t) = {\cal T}\,{\rm{exp}}\left( {  i{\int}_0^t {H\left( {t'} \right)dt'} } \right)\) is the unitary timeevolution operator (\({\cal T}\) denotes time ordering) and H(t) is the Hamiltonian (1). Let \(\left\{ {\left {{\psi _a}(0)} \right\rangle } \right\}_{a = 1}^N\) denote an orthonormal basis for \({{\cal H}_N}(0)\). Thus, \({{\cal H}_N}(t)\) has a basis whose orthonormal elements obey the Schrödinger equation:
We now define another (arbitrary) orthonormal basis \(\left {\psi _a^\prime (t)} \right\rangle \) for \({{\cal H}_N}(t)\), such that \(\left {{\psi _a}(0)} \right\rangle = \left {\psi _a^\prime (0)} \right\rangle \). Then there exists an N × N unitary transformation W(t) between the two bases such that \(\left {{\psi _b}(t)} \right\rangle = \mathop {\sum}\nolimits_{a = 1}^N {W_{ab}}(t)\left {\psi _a^\prime (t)} \right\rangle \). An arbitrary state \(\left {\omega (0)} \right\rangle = \mathop {\sum}\nolimits_a {\omega _a}(0)\left {{\psi _a}(0)} \right\rangle \in {{\cal H}_N}(0)\) thus evolves according to: \(\left {\omega (t)} \right\rangle = \mathop {\sum}\nolimits_a {\omega _a}(0)\left {{\psi _a}(t)} \right\rangle = \mathop {\sum}\nolimits_a {\omega _a}(t)\left {\psi _a^\prime (t)} \right\rangle \), where \({\omega _a}(t) \equiv \mathop {\sum}\nolimits_b {W_{ab}}(t){\omega _b}(0)\). The unitary W(t) can thus be considered as describing the effective evolution of the state \(\left {\omega (t)} \right\rangle \) inside the subspace \({{\cal H}_N}(t)\) in the frame rotating with the basis \(\left {\psi _a^\prime (t)} \right\rangle \) (see SM, section A, for more details). By substituting the basis transformation into Eq. (3) one easily finds:
where \(\tilde H(t)\) and G(t) are the N × N matrices defined by the following matrix elements, respectively:
Let us now assume that H(t) depends on t only via the invertible and differentiable “annealing schedule” s ≡ κ ^{−1}(τ), where τ ≡ t/t _{ f }, and t _{ f } denotes the final time. Then it follows directly from Eq. (5b) that G(t)dt = G(s)ds (see SM, section B), which shows that G(s) is a geometric term, i.e., it depends only on the schedule s (and not on its parametrization).^{40, 41} Consequently, \(W\left( {{t_f}} \right) = {\cal T}\,{\rm{exp}}\left[ {  i{\int}_0^1 {{H^{{\rm{eff}}}}(s)ds} } \right]\) with the dimensionless effective Hamiltonian
where from hereon a dot denotes d/ds, and we set s ≡ τ for simplicity. Note that the geometric term is negligible only in the adiabatic limit t _{ f }→∞.
Application to QA with superconducting flux qubits
Let \(\left\{ {\left {a(s)} \right\rangle } \right\}\), with a = 1, …, N, denote the N lowest instantaneous energy eigenstates of the original Hamiltonian (1), i.e., \(H(s)\left {a(s)} \right\rangle = {E_a}(s)\left {a(s)} \right\rangle \). We identify the earlier \(\left\{ {\left {\psi _a^\prime (s)} \right\rangle } \right\}\) with these eigenstates, so that W(s) describes the unitary evolution in the subspace rotating with the instantaneous eigenbasis of H(s). In this basis, the QA process is described by the dimensionless effective Hamiltonian (6) with:
in which \(\left {b(s)} \right\rangle \), with b = 1, …, N, also labels the N lowest instantaneous energy eigenstates. In QA applications, the Hamiltonian (1) is designed such that its N = 2^{n} lowest energy levels can be put into 1to1 correspondence with the energy levels of a transverse field Ising model with n qubits. Thus, there exists a unitary V(s) such that, up to a term proportional to the identity matrix^{42}:
where the profile functions A(s) and B(s) are particular to the qubit Hamiltonian (see SM, section C), \({\tilde H^X} =  \mathop {\sum}\nolimits_{i = 1}^n \sigma _i^x\) is the usual transverse field driver, and
is the problem Hamiltonian whose ground state encodes the answer to the optimization problem of interest. The unitary V(s) is the transformation between the energy eigenbasis and the computational basis \(\left {\psi _a^{\rm{C}}(s)} \right\rangle \). Note that the geometric term transforms as a geometric connection^{33} (see SM, section D):
QA of a system of n flux qubits controlled by the Hamiltonian (1) is then described by the following effective Hamiltonian in the computational basis:
The first term, proportional to t _{ f }, is the usual Hamiltonian discussed in literature in the context of QA. The second term has a geometric origin and is nonvanishing for finite annealing times t _{ f }. The geometric term is nonstoquastic. To see this, note that the original Hamiltonian (1) is real, and thus there is a basis choice in which the energy eigenbasis states \(\left {a(s)} \right\rangle \) have only real amplitudes. Consequently, the geometric term G(s) in Eq. (7) is then a purely imaginary Hermitian matrix. The transformed geometric term G ^{C}(s) (Eq. (10)) is also purely imaginary in the computational basis, since the basis change of Eq. (8) can be performed with a real unitary (i.e., orthogonal) matrix V(s). Therefore, including only interactions up to twobody terms, the geometric term can be written in the most general form as follows:
QA with geometric terms: one qubit
We now apply the results obtained so far to QA with one qubit. For concreteness we focus on the Cshunt flux qubit with three Josephson junctions. This qubit can be described by the following Hamiltonian^{43,44,45} (see SM, section E):
where ϕ is the flux (in units of Φ_{0}/2π) trapped in the superconducting ring, E _{S} is the charging energy of the shunting capacitor, and \(\phi _{{\rm{CJJ}}}^x(s)\) and ϕ ^{x}(s) are external fluxes used to control the anneal.
We next construct the effective Hamiltonian of Eq. (11). We start by numerically computing the ground state \(\left {0(s)} \right\rangle \) and first excited state \(\left {1(s)} \right\rangle \) of the flux qubit Hamiltonian Eq. (13) (examples are given in the SM, Fig. 5b, d), from which we numerically obtain the effective Hamiltonian (Eq. (6)) in the instantaneous energy eigenbasis:
(we have ignored a term proportional to the identity matrix) where Δ(s) ≡ E _{1}(s) − E _{0}(s) is the gap (plotted in the left panel of Fig. 1) and \(g(s) \equiv \left\langle {1(s)} \right{{\it{\partial }}_s}\left {0(s)} \right\rangle \). We now transform to the computational basis using the unitary \(V(s) = {\rm{exp}}\left[ {\frac{i}{2}{\rm{arctan}}\left( {\frac{{A(s)}}{{B(s)}}} \right){\sigma ^y}} \right]\), after which the Hamiltonian of Eq. (14) becomes
This has the form of the most general singlequbit Hamiltonian: \(H_1^{{\rm{eff,C}}}(s) = \mathop {\sum}\nolimits_{\alpha \in x,y,z} {c_\alpha }(s){\sigma ^\alpha }\). It can be checked easily that (see SM, section F):
It is interesting to note that the second term in Eq. (16b) is similar to, but different from, the counterdiabatic term needed to obtain a “shortcut to adiabaticity”^{46, 47} when QA is modeled directly in terms of a spinsystem Hamiltonian with a transverse field.
By defining the computational basis as the basis of states with welldefined persistent current, the annealing schedule functions A(s) and B(s) (shown in the left panel of Fig. 1) can be determined in terms of the external fluxes \(\phi _{{\rm{CJJ}}}^x\) and ϕ ^{x} of Eq. (13b) (see the SM, section C). Thus, Eq. (16b) shows that these flux parameters in turn control the properties of the geometric term g ^{y}. The profile function g ^{y}(s) for the geometric term, also shown in the left panel of Fig. 1, is nonvanishing toward the middle of the adiabatic evolution, when the gap closes. The effects of the geometric term are shown in the right panel of Fig. 1, obtained by numerically solving for the corresponding unitary evolution. The effects become significant when the gap is small. There is also a significant effect on the final groundstate population.
QA with geometric terms: two qubits
To study the interacting case, we consider two inductively coupled compound Josephson junction flux qubits^{39} (see SM, section E):
where the flux qubit Hamiltonian is given by
and the interaction potential is explicitly written as:
Proceeding as in the singlequbit case, we start from the Hamiltonian (17) to numerically compute \(\tilde H\) and G appearing in Eq. (7), and construct the effective Hamiltonian in the instantaneous energy eigenbasis (Eq. (6)). Once again, the effective Hamiltonian can be expressed in the computational basis by (numerically) finding a unitary V such that the final Hamiltonian reads:
This is the usual transverse field Ising model, plus an additional geometric term, whose general form is given in Eq. (12). The left panel of Fig. 2 shows the gaps Δ_{ k,0} = E _{k} − E _{0}, where \(\left\{ {{E_k}} \right\}_{k = 0}^3\) are the ordered eigenvalues of \(H_2^{{\rm{eff,C}}}\), and the components of the geometric term G ^{C}(s, h _{1}, h _{2}, J _{12}) in the computational basis as defined in Eq. (12). The left panel of Fig. 2 shows that, in general, the geometric functions \(g_i^y(s),g_{ij}^{xy}(s),g_{ij}^{zy}(s)\) are all nonvanishing, and their magnitude grows as the groundstate gap shrinks. In particular, as in the singlequbit case, geometric effects introduce a nonstoquastic contribution to the driver term which is nonvanishing toward the middle of the anneal. As in the singlequbit case, the right panel of Fig. 2 shows that ignoring the geometric terms results in consistently different final populations in the computational basis.
Dependence on the annealing time
The contribution of the geometric terms is inherently a nonadiabatic effect, arising when diabatic transitions populate the excited states. In Fig. 3 we show the fidelity \({\left {\left\langle {\left. {\psi _{1,{\rm{G}}}^{\rm{C}}(s)} \right\psi _{1,{\rm{no}}  {\rm{G}}}^{\rm{C}}(s)} \right\rangle } \right^2}\) between the timeevolved states with and without geometric terms, as a function of the total annealing time. As expected, the effect of the geometric terms increases with decreasing annealing time and it is reflected in the decreasing fidelity. Figure 3 shows that the total annealing time over which this contribution is significant is on the order of a few nanoseconds, for parameters relevant for current QA devices. While this is significantly shorter than the typical microsecond timescale of current QA experiments using the DWave devices, this is the case for the one and twoqubit cases which we have analyzed here. Since, as is clear from Eq. (16b) and from Fig. 2, the magnitude of the geometric term grows in inverse proportion to the gap, we expect it to become more significant for multiqubit problems whose gap dependence can be inverse polynomial or even exponential. This effect is already visible in Fig. 3, which shows that the fidelity for the twoqubit system tends to be smaller than the onequbit system for larger annealing times.
Discussion
We have shown that even the simplest implementation of QA with flux qubits induces effective Hamiltonians with nonstoquastic interactions arising from a geometric phase. The appearance of such interactions is ubiquitous when the Hamiltonian of a continuousvariable system is changed over time, and the evolution is nonadiabatic, due to the appearance of the AharonovAnandan effect. Since arbitrarily small gaps are inevitable in QA for hard optimization problems, nonadiabatic evolutions are ultimately inescapable. We thus argue that, similarly, the geometric effects studied here are unavoidable and relevant in practical applications of QA. It is possible that inclusion of nonadiabatic excitations is enough to prevent the existence of efficient classical simulations of stoquastic Hamiltonians: strikingly, an example of an exponential quantum speedup is known in this case for the “glued trees” problem,^{48} and stoquastic AQC is universal when the computation is crafted to carefully exploit excited states.^{49} The inclusion of geometric effects provides an alternative and desirable mechanism to include nonstoquastic interactions in the effective Hamiltonian of QA realized with continuousvariables systems. It is likely that, in order to exploit geometric terms for computational purposes, a certain level of controllability will be required. For example, the relative strength of the geometric term can be directly controlled by modifying the annealing schedule (e.g., the function \(\dot \kappa (s)\) in Eq. (6)) while keeping the total annealing time t _{ f } constant. We leave to future work the important question of whether new designs of flux qubits might enable a higher level of controllability of the geometric terms discussed here.
Methods
For more details on the experimental and numerical methodologies employed, see Supplementary Information.
Data availability
The authors declare that the main data supporting the finding of this study are available within the article and its Supplementary Information.
References
 1.
Farhi, E., Goldstone, J., Gutmann, S. & Sipser, M. Quantum computation by adiabatic evolution. Preprint at http://arxiv.org/pdf/quantph/0001106 (2000).
 2.
Farhi, E. et al. A quantum adiabatic evolution algorithm applied to random instances of an NPcomplete problem. Science 292, 472–475 (2001).
 3.
van Dam, W., Mosca, M. & Vazirani, U. How powerful is adiabatic quantum computation? Proceedings of the 42nd IEEE Symposium on Foundations of Computer Science, 279–287 (2001).
 4.
Aharonov, D. et al. Adiabatic quantum computation is equivalent to standard quantum computation. SIAM J. Comput. 37, 166–194 (2007).
 5.
Albash, T. & Lidar, D. A. Adiabatic quantum computing. Preprint at http://arxiv.org/pdf/quantph/1611.04471 (2016).
 6.
Kadowaki, T. & Nishimori, H. Quantum annealing in the transverse Ising model. Phys. Rev. E 58, 5355 (1998).
 7.
Santoro, G. E., Martoňák, R., Tosatti, E. & Car, R. Theory of quantum annealing of an Ising spin glass. Science 295, 2427–2430 (2002).
 8.
Das, A. & Chakrabarti, B. K. Colloquium: quantum annealing and analog quantum computation. Rev. Mod. Phys. 80, 1061–1081 (2008).
 9.
Brooke, J., Rosenbaum, T. F. & Aeppli, G. Tunable quantum tunnelling of magnetic domain walls. Nature 413, 610–613 (2001).
 10.
Johnson, M. W. et al. Quantum annealing with manufactured spins. Nature 473, 194–198 (2011).
 11.
Johnson, M. W. et al. A scalable control system for a superconducting adiabatic quantum optimization processor. Supercond. Sci. Technol. 23, 065004 (2010).
 12.
Harris, R. et al. Experimental investigation of an eightqubit unit cell in a superconducting optimization processor. Phys. Rev. B 82, 024511 (2010).
 13.
McMahon, P. L. et al. A fullyprogrammable 100spin coherent Ising machine with alltoall connections. Science 354, 614–617 (2016).
 14.
Bravyi, S., DiVincenzo, D. P., Oliveira, R. I. & Terhal, B. M. The complexity of stoquastic local Hamiltonian problems. Quantum Inf. Comput. 8, 0361 (2008).
 15.
Bravyi, S. & Terhal, B. Complexity of stoquastic frustrationfree Hamiltonians. SIAM J. Comput. 39, 1462–1485 (2009).
 16.
Suzuki, M., Miyashita, S. & Kuroda, A. Monte Carlo simulation of quantum spin systems. I. Prog. Theor. Phys. 58, 1377 (1977).
 17.
Troyer, M. & Wiese, U.J. Computational complexity and fundamental limitations to fermionic quantum Monte Carlo simulations. Phys. Rev. Lett. 94, 170201 (2005).
 18.
Bravyi, S. Monte Carlo simulation of stoquastic Hamiltonians. Preprint at http://arxiv.org/pdf/quantph/1402.2295 (2014).
 19.
Heim, B., Rønnow, T. F., Isakov, S. V. & Troyer, M. Quantum versus classical annealing of Ising spin glasses. Science 348, 215–217 (2015).
 20.
Bravyi, S. & Gosset, D. Polynomialtime classical simulation of quantum ferromagnets. Preprint at http://arxiv.org/pdf/quantph/1612.05602 (2016).
 21.
Ohzeki, M. Quantum Monte Carlo simulation of a particular class of nonstoquastic Hamiltonians in quantum annealing. Sci. Rep. 7, 41186 (2017).
 22.
Farhi, E. & Harrow, A. W. Quantum supremacy through the quantum approximate optimization algorithm. Preprint at http://arxiv.org/pdf/quantph/1602.07674 (2016).
 23.
Hastings, M. B. & Freedman, M. H. Obstructions to classically simulating the quantum adiabatic algorithm. Preprint at http://arxiv.org/pdf/quantph/1302.5733 (2013).
 24.
Jarret, M., Jordan, S. P. & Lackey, B. Adiabatic optimization versus diffusion Monte Carlo methods. Phys. Rev. A 94, 042318 (2016).
 25.
Farhi, E. et al. Quantum adiabatic algorithms, small gaps, and different paths. Quantum Inf. Comput. 11, 181–214 (2011).
 26.
Seoane, B. & Nishimori, H. Manybody transverse interactions in the quantum annealing of the pspin ferromagnet. J. Phys. A 45, 435301 (2012).
 27.
Crosson, E., Farhi, E., Lin, C. Y.Y., Lin, H.H. & Shor, P. Different strategies for optimization using the quantum adiabatic algorithm. Preprint at http://arxiv.org/pdf/quantph/1401.7320 (2014).
 28.
Seki, Y. & Nishimori, H. Quantum annealing with antiferromagnetic transverse interactions for the Hopfield model. J. Phys. A 48, 335301 (2015).
 29.
Zeng, L., Zhang, J. & Sarovar, M. Schedule path optimization for adiabatic quantum computing and optimization. J. Phys. A 49, 165305 (2016).
 30.
Hormozi, L., Brown, E. W., Carleo, G. & Troyer, M. Nonstoquastic Hamiltonians and quantum annealing of Ising spin glass. Preprint at http://arxiv.org/pdf/quantph/1609.06558 (2016).
 31.
Nishimori, H. & Takada, K. Exponential enhancement of the efficiency of quantum annealing by nonstochastic Hamiltonians. Preprint at http://arxiv.org/pdf/quantph/1609.03785 (2016).
 32.
Aharonov, Y. & Anandan, J. Phase change during a cyclic quantum evolution. Phys. Rev. Lett. 58, 1593 (1987).
 33.
Anandan, J. Nonadiabatic nonabelian geometric phase. Phys. Lett. A 133, 171–175 (1988).
 34.
Zanardi, P. & Rasetti, M. Holonomic quantum computation. Phys. Lett. A 264, 94–99 (1999).
 35.
Sjöqvist, E. et al. Nonadiabatic holonomic quantum computation. New J. Phys. 14, 103035 (2012).
 36.
Zu, C. et al. Experimental realization of universal geometric quantum gates with solidstate spins. Nature 514, 72–75 (2014).
 37.
Pirkkalainen, J. M., Solinas, P., Pekola, J. P. & Möttönen, M. Nonabelian geometric phases in groundstate Josephson devices. Phys. Rev. B 81, 174506 (2010).
 38.
Kamleitner, I., Solinas, P., Müller, C., Shnirman, A. & Möttönen, M. Geometric quantum gates with superconducting qubits. Phys. Rev. B 83, 214518 (2011).
 39.
Makhlin, Y., Schön, G. & Shnirman, A. Quantumstate engineering with Josephsonjunction devices. Rev. Mod. Phys. 73, 357–400 (2001).
 40.
Berry, M. V. Quantal phase factors accompanying adiabatic changes. Proc. R. Soc. Lond. A Math. Phys. Sci. 392, 45 (1984).
 41.
Wilczek, F. & Zee, A. Appearance of gauge structure in simple dynamical systems. Phys. Rev. Lett. 52, 2111–2114 (1984).
 42.
Boixo, S. et al. Computational multiqubit tunnelling in programmable quantum annealers. Nat. Commun. 7, 10327 (2016).
 43.
Yan, F. et al. The flux qubit revisited to enhance coherence and reproducibility. Nat. Commun. 7, 12964 (2016).
 44.
Orlando, T. P. et al. Superconducting persistentcurrent qubit. Phys. Rev. B 60, 15398–15413 (1999).
 45.
Weber, S. J. et al. Coherent coupled qubits for quantum annealing. Preprint at http://arxiv.org/pdf/quantph/1701.06544 (2017).
 46.
del Campo, A. Shortcuts to adiabaticity by counterdiabatic driving. Phys. Rev. Lett. 111, 100502 (2013).
 47.
Takahashi, K. Shortcuts to adiabaticity for quantum annealing. Phys. Rev. A 95, 012309 (2017).
 48.
Somma, R. D., Nagaj, D. & Kieferová, M. Quantum speedup by quantum annealing. Phys. Rev. Lett. 109, 050501 (2012).
 49.
Jordan, S. P., Gosset, D. & Love, P. J. QuantumMerlinArthur—complete problems for stoquastic Hamiltonians and Markov matrices. Phys. Rev. A 81, 032331 (2010).
Acknowledgements
We are grateful to Dr. Andrew Kerman for useful discussions. This work was supported under ARO grant number W911NF1210523, ARO MURI Grant Nos. W911NF1110268 and W911NF1510582, and NSF grant number INSPIRE1551064. The research is based on work partially supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via the U.S. Army Research Office contract W911NF17C0050. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.
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Vinci, W., Lidar, D.A. Nonstoquastic Hamiltonians in quantum annealing via geometric phases. npj Quantum Inf 3, 38 (2017). https://doi.org/10.1038/s415340170037z
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