Unbiasing fermionic quantum Monte Carlo with a quantum computer

Abstract

Interacting many-electron 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 systems1–4. Fermionic quantum Monte Carlo (QMC) methods5,6, 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 state-of-the-art classical methods without burdensome error mitigation. Compared with the popular variational quantum eigensolver7,8, our hybrid quantum-classical 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 ground-state wavefunction.

Towards the simulation of large scale protein–ligand interactions on NISQ-era quantum computers

“…A natural extension will be to determine the second-order SAPT terms that would allow for accurate interaction energies and induction and dispersion energy components to be computed. Other interesting questions are how to increase the quantitative accuracy of the method by including correlation out of the active space 18,59 and to gather more challenging drug–protein systems potentially containing multiple metal centers.…”

“…This may be practically impossible with the current general algorithms and available hardware although we note alternatives to the VQE may help to overcome this issue. 18 …”

Recovering models of open quantum systems from data via polynomial optimization: Towards globally convergent quantum system identification

“…Quantum technologies [1,2], which rest on the ability to actively control microscopic systems and exploit their unique quantum mechanical features, have the potential for high impact in a number of different fields; from precision sensing [3][4][5] and communication [6,7], to encryption [8] and very prominently quantum computing [1,9,10]. The extreme sensitivity of quantum effects to their immediate environment, combined with the exquisite control required to exploit these effects, necessitates accurate dynamical modeling.…”

Artificial Neural Network Encoding of Molecular Wavefunction for Quantum Computing

“…Various wave function ansatze for the state preparation have been developed and cannot be all cited, but the most extensively studied is the unitary coupled-cluster (UCC) framework 46 and many others. 47,48 In this work, we present the development of an algorithm to build the trained ANN object on a QC as a materialized representation of the theoretical molecular many-electron wavefunction. It is classified as a VQE type.…”