Abstract
Interacting manyelectron problems pose some of the greatest computational challenges in science, with essential applications across many fields. The solutions to these problems will offer accurate predictions of chemical reactivity and kinetics, and other properties of quantum systems^{1,2,3,4}. Fermionic quantum Monte Carlo (QMC) methods^{5,6}, which use a statistical sampling of the ground state, are among the most powerful approaches to these problems. Controlling the fermionic sign problem with constraints ensures the efficiency of QMC at the expense of potentially significant biases owing to the limited flexibility of classical computation. Here we propose an approach that combines constrained QMC with quantum computation to reduce such biases. We implement our scheme experimentally using up to 16 qubits to unbias constrained QMC calculations performed on chemical systems with as many as 120 orbitals. These experiments represent the largest chemistry simulations performed with the help of quantum computers, while achieving accuracy that is competitive with stateoftheart classical methods without burdensome error mitigation. Compared with the popular variational quantum eigensolver^{7,8}, our hybrid quantumclassical computational model offers an alternative path towards achieving a practical quantum advantage for the electronic structure problem without demanding exceedingly accurate preparation and measurement of the groundstate wavefunction.
Main
The complexity of finding an accurate solution of the Schrödinger equation seemingly grows exponentially with the number of electrons in the system. This fact has greatly hindered progress towards an efficient means of accurately calculating groundstate quantum mechanical properties of complex systems. Over the last century, substantial research effort has been devoted to the development of new algorithms for solution of this manyelectron problem. At present, all available generalpurpose methods can be grouped into two categories: (1) methods that scale exponentially with system size while yielding numerically exact answers, and (2) methods for which the cost scales polynomially with system size, but that are only approximate by construction. Approaches in this second category are the only methods that can feasibly be applied to large systems at present. The accuracy of the solutions obtained by these methods is often unsatisfactory and is almost always difficult to assess.
Quantum computing has arisen as an alternative model for the calculation of quantum properties that may complement, and potentially surpass, classical methods in terms of efficiency^{9,10}. Although the ultimate ambition of this field is to construct a universal faulttolerant quantum computer^{11}, the experimental devices of today are limited to noisy intermediatescale quantum (NISQ) computers^{12}. NISQ algorithms for the computation of ground states have largely centred around the variational quantum eigensolver (VQE) framework^{7,8}, which necessitates coping with optimization difficulties, measurement overhead and circuit noise. As an alternative, algorithms based on imaginarytime evolution have been put forward, which, in principle, avoid the optimization problem^{13,14}. However, because of the nonunitary nature of imaginarytime evolution, one must resort to heuristics to achieve reasonable scaling with system size. New strategies that avoid these limitations may help to enable the first practical quantum advantage in fermionic simulations. In this work, we propose and experimentally demonstrate a class of quantumclassical hybrid algorithms that offer a different route to addressing these challenges. We do not attempt to represent the groundstate wavefunction using our quantum processor, choosing instead to use it to guide a quantum Monte Carlo (QMC) calculation performed on a classical coprocessor. Using this approach, our experimental demonstration surpasses the scale of previous experimental work on quantum simulation in chemistry^{15,16,17}.
Theory and algorithms
QMC approaches^{5,6} target the exact groundstate wavefunction, \({\Psi }_{0}\rangle \), of a manybody Hamiltonian, \(\hat{H}\), via imaginarytime evolution of an initial state \({\Phi }_{0}\rangle \) with a nonzero overlap with \({\Psi }_{0}\rangle \):
where \(\tau \) is imaginary time and \(\Psi (\tau )\rangle \) denotes the timeevolved wavefunction from \({\Phi }_{0}\rangle \) by τ (Fig. 1a). In QMC, the imaginarytime evolution in equation (1) is implemented stochastically, which can enable a polynomial scaling algorithm to sample an estimate for the exact groundstate energy by avoiding the explicit storage of highdimensional objects, such as \(\hat{H}\) and \({\Psi }_{0}\rangle \). The groundstate energy, \({E}_{{\rm{ground}}}=E(\tau =\infty )\), is estimated from averaging a time series of \(\{{E}^{(i)}(\tau )\}\), given by a weighted average over M statistical samples,
where E^{(i)}(τ) is the ith statistical sample for the energy and w_{i}(τ) is the corresponding normalized weight for that sample at imaginary time τ. Although formally exact, such a stochastic imaginarytime evolution algorithm will generically run into the fermionic sign problem^{18}, which manifests as a result of alternating signs in the weights of each statistical sample used in equation (2). In the worst case, the fermionic sign problem causes the estimator of the energy in equation (2) to have exponentially large variance (Fig. 1b, top), necessitating that one averages exponentially many samples so as to obtain a target precision. Accordingly, exact, unbiased QMC approaches are only applicable to small systems^{19,20,21} or those lacking a sign problem^{22}.
The sign problem can be controlled to give an estimator of the groundstate energy with polynomially bound variance by imposing constraints on the imaginarytime evolution of each statistical sample represented by a trial wavefunction, \({\varphi }_{i}(\tau )\rangle \). These constraints (which include prominent examples such as the fixed node^{6,23} and phaseless approximations^{24,25}) are imposed by demanding that the overlaps of the trial wavefunction \(({\Psi }_{\text{T}}\rangle \rangle )\) (where T denotes trial) with the stochastic samples \(({\varphi }_{i}(\tau )\rangle )\) remain positive during the imaginarytime propagation. Although constrained QMC calculations are typically much more accurate than those using the bare trial wavefunction directly (Fig. 1b, bottom), the remaining bias of the constrained QMC results is wholly determined by the choice of the trial wavefunction. Imposing these constraints necessarily introduces a potentially significant bias in the final groundstate energy estimate, which can be removed in the limit that the trial wavefunction approaches the exact ground state. Alternatively, the bias can be removed by releasing the constraints during propagation, at the expense of suffering an uncontrolled sign problem^{26}.
Classically, computationally tractable options for trial wavefunctions are limited to states such as a single meanfield determinant (for example, a Hartree–Fock state), a linear combination of meanfield states, a simple form of the electron–electron pair (twobody) correlator (usually called a Jastrow factor) applied to meanfield states or some other physically motivated transformations applied to meanfield states, such as backflow approaches^{27}. On the other hand, any wavefunction that can be prepared with a quantum circuit is a candidate for a trial wavefunction on a quantum computer, including more general twobody correlators. These trial wavefunctions will be referred to as ‘quantum’ trial wavefunctions.
At present, there is no efficient classical algorithm to estimate (to additive error) the overlap between \({\varphi }_{i}(\tau )\rangle \) and various quantum trial wavefunctions \({\Psi }_{\text{T}}\rangle \), such as unitary coupledcluster with singles and doubles^{28}, qubit coupledcluster methods^{29}, wavefunctions constructed by adiabatic state preparation^{30} or the multiscale entanglement renormalization ansatz^{31}. This is true even when \({\varphi }_{i}(\tau )\rangle \) is simply a computational basis state or a Slater determinant. As quantum computers can efficiently approximate \(\langle {\Psi }_{T}{\varphi }_{i}(\tau )\rangle \), there is a potential quantum advantage in this task, as well as its particular use in QMC. This offers a different route towards quantum advantage in groundstate fermion simulations compared with VQE, which instead seeks an advantage in the variational energy evaluation. We expand on this discussion of quantum advantage in Supplementary Section F.
Our quantumclassical hybrid QMC algorithm (QCQMC) utilizes quantum trial wavefunctions while performing the majority of imaginarytime evolution on a classical computer, and is summarized in Fig. 1c. In essence, on a classical computer one performs imaginarytime evolution for each wavefunction statistical sample, \({\varphi }_{i}(\tau )\rangle \), and collects observables such as the groundstate energy estimate, \({E}^{(i)}(\tau )\). During this procedure, a constraint associated with the quantum trial wavefunction is imposed to control the sign problem. To perform the constrained time evolution, the only quantity that needs to be calculated on the quantum computer is the overlap between the trial wavefunction, \({\Psi }_{\text{T}}\rangle \), and the statistical sample of the wavefunction at imaginary time τ, \({\varphi }_{i}(\tau )\rangle \). Although our approach applies generally to any form of constrained QMC, here we discuss an experimental demonstration of the algorithm that uses an implementation of QMC known as auxiliaryfield QMC (AFQMC), which will be referred to as QCAFQMC (see Methods for more details). As a single determinant meanfield trial wavefunction is the most widely used classical form of the trial function for AFQMC owing to its efficiency, here we use ‘AFQMC’ to denote AFQMC with a meanfield trial wavefunction.
Discussion
As the first example, in Fig. 2 we illustrate the quantum primitive used to perform the experiment on an H_{4} molecule involving 8 qubits (see Methods for more details). Our eight spinorbital quantum trial wavefunction consists of a valence bond wavefunction known as a perfect pairing state^{32,33} and a hardwareefficient quantum circuit^{15} with an offline singleparticle rotation, which would be classically difficult to use as a trial wavefunction for AFQMC. The state preparation circuit in Fig. 2a shows how this trial wavefunction can be efficiently prepared on a quantum computer.
In this 8qubit experiment, we consider H_{4} in a square geometry with side lengths of 1.23 Å and its dissociation into four hydrogen atoms. This system is often used as a test bed for electron correlation methods in quantum chemistry^{34,35}. We perform our calculations using two Gaussian basis sets: the minimal (STO3G) basis set^{36} and the correlation consistent quadruplezeta (ccpVQZ) basis set^{37}. The latter basis set is of the size and accuracy required to make a direct comparison with laboratory experiments. When describing the ground state of this system, there are two equally important, degenerate meanfield states. This makes AFQMC with a single meanfield trial wavefunction highly unreliable. In addition, a method often referred to as a ‘gold standard’ classical approach (that is, coupledcluster with singles, doubles and perturbative triples, CCSD(T)^{38}) also performs poorly for this system.
In Table 1, the difficulties of AFQMC and CCSD(T) are well illustrated by comparing their atomization energies with exact values in two different basis sets. Both approaches show errors that are significantly larger than ‘chemical accuracy’ (1 kcal mol^{−1}). The variational energy of the quantum trial reconstructed from experiment has a bias that can be as large as 33 kcal mol^{−1}. The noise on our quantum device makes the quality of our quantum trial far from that of the ideal (that is, noiseless) ansatz, as shown in Fig. 2b, c, resulting in an error as large as 10 kcal mol^{−1} in the atomization energy. Nonetheless, QCAFQMC reduces this error significantly, and achieves chemical accuracy in both bases. Notably, we achieve this accuracy even in the larger basis, where the variational energy of the quantum trial in the absence of noise is far from exact.
As shown in Supplementary Section C, for the larger basis set we obtain a residual ‘virtual’ correlation energy by using the quantum resources on a smaller number of orbitals to unbias an AFQMC calculation on a larger number of orbitals, with no additional overhead to the quantum computer. This capability makes our implementation competitive with stateoftheart classical approaches. Similar virtual correlation energy strategies have been previously discussed within the framework of VQE^{39}, but, unlike our approach, those strategies come with a significant measurement overhead. To unravel the QCAFQMC results on H_{4} further, in Fig. 2b, c we illustrate the evolution of trial and QCAFQMC energies as a function of the number of measurements made on the device. Despite the presence of significant noise in approximately 10^{5} measurements, QCAFQMC achieves chemical accuracy while coping with a sizeable residual bias in the underlying quantum trial.
Next, we move to a larger example, N_{2}, which requires a total of 12 qubits in our quantum experiment. Here a simpler quantum trial is usedfor QCAFQMC by taking just the valence bond part of the wavefunction depicted in Fig. 2a. We examine the potential energy surface of N_{2} from compressed to elongated geometries, which is another common benchmark problem for classical quantum chemistry methods^{35,40}. In Fig. 3a, the QCAFQMC result is shown for the calculations performed in a triplezeta basis (ccpVTZ) set^{37}, which corresponds to a 60orbital or 120qubit Hilbert space. All examined methods, CCSD(T), AFQMC and QCAFQMC, perform well near the equilibrium geometry, but CCSD(T) and AFQMC deviate from the exact results significantly as the bond distance is stretched. As a result, the error for ‘gold standard’ CCSD(T) can be as large as 14 kcal mol^{−1}, and the error for AFQMC with a classical trial wavefunction can be as large as −8 kcal mol^{−1}. The error in the QCAFQMC computation ranges from −2 kcal mol^{−1} to 1 kcal mol^{−1} depending on the bond distance. Thus, although we do not achieve chemical accuracy with QCAFQMC, we note that, even with a simple quantum trial wavefunction, we produce energies that are competitive with stateoftheart classical approaches. Idealized (that is, noiseless) VQE experiments for the same trial wavefunction would yield similar results to our quantum trial results Fig. 3a (within 4.5 kcal mol^{−1}), which are much worse than our QCAFQMC results with an error as large as 50 kcal mol^{−1}.
Finally, we present a 16qubit experiment result for the groundstate simulation of a minimal unit cell (twoatom) model of periodic solid diamond in a doublezeta basis set (DZVPGTH^{41}; 26 orbitals). Although at this level of theory the model exhibits significant finitesize effects and does not predict the correct experimental lattice constant, we aim to illustrate the utility of our algorithm in materials science applications. We emphasize that this is the largest quantum simulation of chemistry on a quantum processor so far (detailed resource counts and comparison with prior works are available in Extended Data Tables 11, 12). We again use the simple perfect pairing state as our quantum trial wavefunction and demonstrate the improvement over a range of lattice parameters compared with classical AFQMC and CCSD(T) in Fig. 3b. There is a substantial improvement in the error going from AFQMC to QCAFQMC, showing the increased accuracy due to better trial wavefunctions. At the same time, QCAFQMC performed using the idealized quantum trial produces results comparable to our experimental energies, suggesting that the error in our QCAFQMC energies is mainly due to the use of an insufficiently accurate trial wavefunction rather than experimental error. Our accuracy is limited by the simple form of our quantum trial and yet we achieve accuracy nearly on a par with the classical gold standard method, CCSD(T).
Conclusion and outlook
In summary, we propose a scalable, noiseresilient quantumclassical hybrid algorithm that seamlessly embeds a specialpurpose quantum primitive into an accurate quantum computational manybody method, namely QMC. Our work offers a computational strategy that effectively unbiases fermionic QMC approaches by leveraging stateoftheart quantum information tools. We have realized this algorithm for a specific QMC algorithm known as AFQMC, and demonstrated its performance in experiments as large as 16 qubit on a NISQ processor, producing electronic energies that are competitive with stateoftheart classical quantum chemistry methods. Our algorithm also enables the incorporation of the electron correlation energy outside the space that is handled by the quantum computer without increasing quantum resources or measurement overheads. In Supplementary Section F, we discuss issues related to asymptotic scaling and the potential for quantum advantage in our algorithm. Although we have yet to achieve practical quantum advantage over available classical algorithms, the flexibility and scalability of our proposed approach in the construction of quantum trial functions, and its inherent noise resilience, promise a path forward for the simulation of chemistry in the NISQ era and beyond.
Note added in proof: After this work was nearly complete, a theory paper by Yang et al. appeared on arXiv^{42}, describing a quantum algorithm for assisting realtime dynamics with unconstrained QMC.
Methods
Wavefunction overlap estimation
In this work, we estimate the overlap between the trial wavefunction and the statistical samples using a technique known as shadow tomography^{43,44}. Experimentally, this entails performing randomly chosen measurements of a reference state related to \({\Psi }_{\text{T}}\rangle \) before beginning the QMC calculation, yielding the representation of \({\Psi }_{\text{T}}\rangle \) in the computational basis for subsequent overlap evaluations. In this formulation of QCQMC, there is no need for the QMC calculation to iteratively query the quantum processor, despite the fact that the details of the statistical samples are not determined in advance. By disentangling the interaction between the quantum and classical computer we avoid feedback latency, an appealing feature on early NISQ platforms that comes at the cost of requiring potentially expensive classical postprocessing (see Supplementary Section D for more details). Furthermore, our algorithm naturally achieves some degree of noise robustness, as explained in Supplementary Section D, because the quantity directly used in QCQMC is the ratio between overlap values, which is inherently resilient to the estimates of the overlaps being rescaled. We highlight the challenges posed by the need to measure wavefunction overlaps precisely and the tradeoffs involved in the use of shadow tomography (see also Supplementary Section D), while giving our perspective on the most promising paths forward.
Phaseless constraints in AFQMC
In AFQMC, the \({\varphi }_{i}(\tau )\rangle \) take the form of Slater determinants in arbitrary singleparticle bases, enabling us to express the energy estimator (presented in Supplementary equation (3)) in terms of a modest number of wavefunction overlaps that we can evaluate efficiently on the quantum processor (Supplementary Section C). The phaseless constraint is imposed to control the sign problem and, likewise, only requires calculating the overlaps between \({\Psi }_{\text{T}}\rangle \) and \({\varphi }_{i}(\tau )\rangle \), as detailed in Supplementary equation (6). AFQMC has been shown to be accurate in a number of cases even with classically available trial wavefunctions^{45,46}; however, the bias incurred from the phaseless constraint cannot be overlooked.
Quantum processor
The experiments in this work were carried out on the Google 54qubit quantum processor known as Sycamore^{47}. The circuits were compiled using hardwarenative conditional Z gates with typical error rates of ≈0.5% (ref. ^{48}).
Data availability
The datasets generated and/or analysed during the current study are available from the corresponding authors on reasonable request. Source data are provided with this paper.
Code availability
We used available packages such as QChem ^{49} and Cirq (see https://github.com/quantumlib/Cirq for details on obtaining the source code); more details are available in Supplementary Section E. Other codes used herein are available from the corresponding authors on reasonable request.
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Acknowledgements
We thank members of the Google Quantum AI theory team and F. Malone for helpful discussions. J.L. and D.R.R. acknowledge the support of NSF CHE1954791. B.O. is supported by a NASA Space Technology Research Fellowship and the NSF QLCI program through grant number OMA2016245. The quantum hardware used for this experiment was developed by the Google Quantum AI hardware team, under the direction of A. Megrant, J. Kelly and Y. Chen. Theoretical foundations for device calibrations were provided by the physics team lead by V. Smelyanskiy. Initial data collection was enabled by cloud access to these devices as part of Google Quantum AI’s Quantum Computing Service Early Access Program. P. Roushan and C. Neill from the Google team helped to execute the experiment on hardware and design figures.
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J.L. conceived the quantumclassical hybrid QMC algorithm, performed QMC calculations and, with contribution from others, drafted the manuscript. W.J.H. proposed the use of shadow tomography and designed the experiment, with contributions from others. B.O. helped with theoretical analysis and the compilation of circuits. N.C.R. helped with the presentation of figures. J.L. and R.B. managed the scientific collaboration. All authors participated in discussions, writing the manuscript and analysis of the data. J.L. and W.J.H. contributed equally to this work.
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Huggins, W.J., O’Gorman, B.A., Rubin, N.C. et al. Unbiasing fermionic quantum Monte Carlo with a quantum computer. Nature 603, 416–420 (2022). https://doi.org/10.1038/s4158602104351z
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DOI: https://doi.org/10.1038/s4158602104351z
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