Quantum computing for chemistry and physics applications from a Monte Carlo perspective.

This Perspective focuses on the several overlaps between quantum algorithms and Monte Carlo methods in the domains of physics and chemistry. We will analyze the challenges and possibilities of integrating established quantum Monte Carlo solutions into quantum algorithms. These include refined energy estimators, parameter optimization, real and imaginary-time dynamics, and variational circuits. Conversely, we will review new ideas for utilizing quantum hardware to accelerate the sampling in statistical classical models, with applications in physics, chemistry, optimization, and machine learning. This review aims to be accessible to both communities and intends to foster further algorithmic developments at the intersection of quantum computing and Monte Carlo methods. Most of the works discussed in this Perspective have emerged within the last two years, indicating a rapidly growing interest in this promising area of research.

Quantum-enhanced Markov chain Monte Carlo.

Quantum computers promise to solve certain computational problems much faster than classical computers. However, current quantum processors are limited by their modest size and appreciable error rates. Recent efforts to demonstrate quantum speedups have therefore focused on problems that are both classically hard and naturally suited to current quantum hardware, such as sampling from complicated-although not explicitly useful-probability distributions1-3. Here we introduce and experimentally demonstrate a quantum algorithm that is similarly well suited to current hardware, but which samples from complicated distributions arising in several applications. The algorithm performs Markov chain Monte Carlo (MCMC), a prominent iterative technique4, to sample from the Boltzmann distribution of classical Ising models. Unlike most near-term quantum algorithms, ours provably converges to the correct distribution, despite being hard to simulate classically. But like most MCMC algorithms, its convergence rate is difficult to establish theoretically, so we instead analysed it through both experiments and simulations. In experiments, our quantum algorithm converged in fewer iterations than common classical MCMC alternatives, suggesting unusual robustness to noise. In simulations, we observed a polynomial speedup between cubic and quartic over such alternatives. This empirical speedup, should it persist to larger scales, could ease computational bottlenecks posed by this sampling problem in machine learning5, statistical physics6 and optimization7. This algorithm therefore opens a new path for quantum computers to solve useful-not merely difficult-sampling problems.

Improved Accuracy on Noisy Devices by Nonunitary Variational Quantum Eigensolver for Chemistry Applications.

We propose a modification of the Variational Quantum Eigensolver algorithm for electronic structure optimization using quantum computers, named nonunitary Variational Quantum Eigensolver (nu-VQE), in which a nonunitary operator is combined with the original system Hamiltonian leading to a new variational problem with a simplified wave function ansatz. In the present work, as nonunitary operator, we use the Jastrow factor, inspired from classical Quantum Monte Carlo techniques for simulation of strongly correlated electrons. The method is applied to prototypical molecular Hamiltonians for which we obtain accurate ground-state energies with shallower circuits, at the cost of an increased number of measurements. Finally, we also show that this method achieves an important error mitigation effect that drastically improves the quality of the results for VQE optimizations on today's noisy quantum computers. The absolute error in the calculated energy within our scheme is 1 order of magnitude smaller than the corresponding result using traditional VQE methods, with the same circuit depth.

Evidence for supercritical behaviour of high-pressure liquid hydrogen.

Hydrogen, the simplest and most abundant element in the Universe, develops a remarkably complex behaviour upon compression1. Since Wigner predicted the dissociation and metallization of solid hydrogen at megabar pressures almost a century ago2, several efforts have been made to explain the many unusual properties of dense hydrogen, including a rich and poorly understood solid polymorphism1,3-5, an anomalous melting line6 and the possible transition to a superconducting state7. Experiments at such extreme conditions are challenging and often lead to hard-to-interpret and controversial observations, whereas theoretical investigations are constrained by the huge computational cost of sufficiently accurate quantum mechanical calculations. Here we present a theoretical study of the phase diagram of dense hydrogen that uses machine learning to 'learn' potential-energy surfaces and interatomic forces from reference calculations and then predict them at low computational cost, overcoming length- and timescale limitations. We reproduce both the re-entrant melting behaviour and the polymorphism of the solid phase. Simulations using our machine-learning-based potentials provide evidence for a continuous molecular-to-atomic transition in the liquid, with no first-order transition observed above the melting line. This suggests a smooth transition between insulating and metallic layers in giant gas planets, and reconciles existing discrepancies between experiments as a manifestation of supercritical behaviour.

TurboRVB: A many-body toolkit for ab initio electronic simulations by quantum Monte Carlo.

TurboRVB is a computational package for ab initio Quantum Monte Carlo (QMC) simulations of both molecular and bulk electronic systems. The code implements two types of well established QMC algorithms: Variational Monte Carlo (VMC) and diffusion Monte Carlo in its robust and efficient lattice regularized variant. A key feature of the code is the possibility of using strongly correlated many-body wave functions (WFs), capable of describing several materials with very high accuracy, even when standard mean-field approaches [e.g., density functional theory (DFT)] fail. The electronic WF is obtained by applying a Jastrow factor, which takes into account dynamical correlations, to the most general mean-field ground state, written either as an antisymmetrized geminal power with spin-singlet pairing or as a Pfaffian, including both singlet and triplet correlations. This WF can be viewed as an efficient implementation of the so-called resonating valence bond (RVB) Ansatz, first proposed by Pauling and Anderson in quantum chemistry [L. Pauling, The Nature of the Chemical Bond (Cornell University Press, 1960)] and condensed matter physics [P.W. Anderson, Mat. Res. Bull 8, 153 (1973)], respectively. The RVB Ansatz implemented in TurboRVB has a large variational freedom, including the Jastrow correlated Slater determinant as its simplest, but nontrivial case. Moreover, it has the remarkable advantage of remaining with an affordable computational cost, proportional to the one spent for the evaluation of a single Slater determinant. Therefore, its application to large systems is computationally feasible. The WF is expanded in a localized basis set. Several basis set functions are implemented, such as Gaussian, Slater, and mixed types, with no restriction on the choice of their contraction. The code implements the adjoint algorithmic differentiation that enables a very efficient evaluation of energy derivatives, comprising the ionic forces. Thus, one can perform structural optimizations and molecular dynamics in the canonical NVT ensemble at the VMC level. For the electronic part, a full WF optimization (Jastrow and antisymmetric parts together) is made possible, thanks to state-of-the-art stochastic algorithms for energy minimization. In the optimization procedure, the first guess can be obtained at the mean-field level by a built-in DFT driver. The code has been efficiently parallelized by using a hybrid MPI-OpenMP protocol, which is also an ideal environment for exploiting the computational power of modern Graphics Processing Unit accelerators.

Nonadiabatic Molecular Quantum Dynamics with Quantum Computers.

The theoretical investigation of nonadiabatic processes is hampered by the complexity of the coupled electron-nuclear dynamics beyond the Born-Oppenheimer approximation. Classically, the simulation of such reactions is limited by the unfavorable scaling of the computational resources as a function of the system size. While quantum computing exhibits proven quantum advantage for the simulation of real-time dynamics, the study of quantum algorithms for the description of nonadiabatic phenomena is still unexplored. In this Letter, we propose a quantum algorithm for the simulation of fast nonadiabatic chemical processes together with an initialization scheme for quantum hardware calculations. In particular, we introduce a first-quantization method for the time evolution of a wave packet on two coupled harmonic potential energy surfaces (Marcus model). In our approach, the computational resources scale polynomially in the system dimensions, opening up new avenues for the study of photophysical processes that are classically intractable.

Nonunitary Operations for Ground-State Calculations in Near-Term Quantum Computers.

We introduce a quantum Monte Carlo inspired reweighting scheme to accurately compute energies from optimally short quantum circuits. This effectively hybrid quantum-classical approach features both entanglement provided by a short quantum circuit, and the presence of an effective nonunitary operator at the same time. The functional form of this projector is borrowed from classical computation and is able to filter out high-energy components generated by a suboptimal variational quantum heuristic Ansatz. The accuracy of this approach is demonstrated numerically in finding energies of entangled ground states of many-body lattice models. We demonstrate a practical implementation on IBM quantum hardware up to an 8-qubit circuit.

Phase Diagram of Hydrogen and a Hydrogen-Helium Mixture at Planetary Conditions by Quantum Monte Carlo Simulations.

Understanding planetary interiors is directly linked to our ability of simulating exotic quantum mechanical systems such as hydrogen (H) and hydrogen-helium (H-He) mixtures at high pressures and temperatures. Equation of state (EOS) tables based on density functional theory are commonly used by planetary scientists, although this method allows only for a qualitative description of the phase diagram. Here we report quantum Monte Carlo (QMC) molecular dynamics simulations of pure H and H-He mixture. We calculate the first QMC EOS at 6000 K for a H-He mixture of a protosolar composition, and show the crucial influence of He on the H metallization pressure. Our results can be used to calibrate other EOS calculations and are very timely given the accurate determination of Jupiter's gravitational field from the NASA Juno mission and the effort to determine its structure.

Accelerating ab initio Molecular Dynamics and Probing the Weak Dispersive Forces in Dense Liquid Hydrogen.

We propose an ab initio molecular dynamics method, capable of dramatically reducing the autocorrelation time required for the simulation of classical and quantum particles at finite temperatures. The method is based on an efficient implementation of a first order Langevin dynamics modified by means of a suitable, position dependent acceleration matrix S. Here, we apply this technique to both Lennard-Jones models, to demonstrate the accuracy and speeding-up of the sampling, and within a quantum Monte Carlo based wave function approach, for determining the phase diagram of high-pressure hydrogen with simulations much longer than the autocorrelation time. With the proposed method, we are able to equilibrate in a few hundred steps even close to the liquid-liquid phase transition (LLT). Within our approach, we find that the LLT transition is consistent with recent density functionals predicting a much larger transition pressure when the long range dispersive forces are taken into account.

Understanding Quantum Tunneling through Quantum Monte Carlo Simulations.

The tunneling between the two ground states of an Ising ferromagnet is a typical example of many-body tunneling processes between two local minima, as they occur during quantum annealing. Performing quantum Monte Carlo (QMC) simulations we find that the QMC tunneling rate displays the same scaling with system size, as the rate of incoherent tunneling. The scaling in both cases is O(Δ^{2}), where Δ is the tunneling splitting (or equivalently the minimum spectral gap). An important consequence is that QMC simulations can be used to predict the performance of a quantum annealer for tunneling through a barrier. Furthermore, by using open instead of periodic boundary conditions in imaginary time, equivalent to a projector QMC algorithm, we obtain a quadratic speedup for QMC simulations, and achieve linear scaling in Δ. We provide a physical understanding of these results and their range of applicability based on an instanton picture.

Ab initio molecular dynamics simulation of liquid water by quantum Monte Carlo.

Although liquid water is ubiquitous in chemical reactions at roots of life and climate on the earth, the prediction of its properties by high-level ab initio molecular dynamics simulations still represents a formidable task for quantum chemistry. In this article, we present a room temperature simulation of liquid water based on the potential energy surface obtained by a many-body wave function through quantum Monte Carlo (QMC) methods. The simulated properties are in good agreement with recent neutron scattering and X-ray experiments, particularly concerning the position of the oxygen-oxygen peak in the radial distribution function, at variance of previous density functional theory attempts. Given the excellent performances of QMC on large scale supercomputers, this work opens new perspectives for predictive and reliable ab initio simulations of complex chemical systems.

Distinct metallization and atomization transitions in dense liquid hydrogen.

We perform molecular dynamics simulations driven by accurate quantum Monte Carlo forces on dense liquid hydrogen. There is a recent report of a complete atomization transition between a mixed molecular-atomic liquid and a completely dissociated fluid in an almost unaccessible pressure range [Nat. Commun. 5, 3487 (2014)]. Here, instead, we identify a different transition between the fully molecular liquid and the mixed-atomic fluid at ∼400 GPa, i.e., in a much more interesting pressure range. We provide numerical evidence supporting the metallic behavior of this intermediate phase. Therefore, we predict that the metallization at finite temperature occurs in this partially dissociated molecular fluid, well before the complete atomization of the liquid. At high temperature this first-order transition becomes a crossover, in very good agreement with the experimental observation. Several systematic tests supporting the quality of our large scale calculations are also reported.

Unexpectedly high pressure for molecular dissociation in liquid hydrogen by electronic simulation.

The study of the high pressure phase diagram of hydrogen has continued with renewed effort for about one century as it remains a fundamental challenge for experimental and theoretical techniques. Here we employ an efficient molecular dynamics based on the quantum Monte Carlo method, which can describe accurately the electronic correlation and treat a large number of hydrogen atoms, allowing a realistic and reliable prediction of thermodynamic properties. We find that the molecular liquid phase is unexpectedly stable, and the transition towards a fully atomic liquid phase occurs at much higher pressure than previously believed. The old standing problem of low-temperature atomization is, therefore, still far from experimental reach.

Finite-temperature electronic simulations without the Born-Oppenheimer constraint.

The adiabatic approximation, typically assumed when performing standard Born-Oppenheimer (BO) molecular dynamics, can become unreliable at finite temperature, and specifically when the temperature is larger than the electronic energy gap between the ground state and the low-lying excited states. In this regime, relevant for many important chemical processes, the non-adiabatic couplings between the electronic energy states can produce finite temperature effects in several molecular properties, such as the geometry, the vibrational frequencies, the binding energy, and several chemical reactions. In this work, we introduce a novel finite-temperature non-adiabatic molecular dynamics based on a novel covariant formulation of the electronic partition function. In this framework, the nuclei are not constrained to move in a specific electronic potential energy surface. Then, by using a rigorous variational upper bound to the free energy, we are led to an approximate partition function that can be evaluated numerically. The method can be applied to any technique capable to provide an energy value over a given wave function ansatz depending on several variational parameters and atomic positions. In this work, we have applied the proposed method within a quantum Monte Carlo (QMC) scheme. In particular, we consider in this first application only classical ions, but we explicitly include an electronic correlation (Jastrow) term in the wave function, by extending in this way the standard variational QMC method, from ground state to finite temperature properties. We show that our approximation reduces correctly to the standard ground-state Born-Oppenheimer (gsBO) at zero temperature and to the correct high temperature limit. Moreover, at temperatures large enough, this method improves the upper bound of the free energy obtained with a single BO energy surface, since within our approach it is possible to estimate the electron entropy of a correlated ansatz in an efficient way. We test this new method on the simple hydrogen molecule, where at low temperature we recover the correct gsBO low temperature limit. Moreover, we show that the dissociation of the molecule is possible at a temperature much smaller than the one corresponding to the gsBO energy surface, in good agreement with experimental evidence. Several extensions of the proposed technique are also discussed, as for instance the inclusion of quantum effects for ions and the calculation of critical (magnetic, superconducting) temperatures.