A quantum computing approach to fixed-node Monte Carlo using classical shadows

TL;DR

This paper explores a quantum computing approach to fixed-node Monte Carlo methods using classical shadows, aiming to improve electronic structure calculations, but finds high sampling costs and noise sensitivity limit near-term advantages.

Contribution

It introduces a fixed-node Monte Carlo method leveraging classical shadows to avoid exponential post-processing, applied to molecular systems with analysis of noise effects.

Findings

Fixed-node approach reduces exponential scaling in overlap estimation.

AFQMC is more robust to noise than fixed-node Monte Carlo.

High sampling costs limit practical accuracy for small active spaces.

Abstract

Quantum Monte Carlo (QMC) methods are powerful approaches for solving electronic structure problems. Although they often provide high-accuracy solutions, the precision of most QMC methods is ultimately limited by a trial wave function that must be used. Recently, an approach has been demonstrated to allow the use of trial wave functions prepared on a quantum computer [Nature 603, 416 (2022)] in the auxiliary-field QMC (AFQMC) method, using classical shadows to estimate the required overlaps. However, this approach has an exponential post-processing step to construct these overlaps, when performing classical shadows obtained using random Clifford circuits. Here, we study an approach to avoid this exponentially scaling step by using a fixed-node Monte Carlo method, based on full configuration interaction quantum Monte Carlo (FCIQMC). This method is applied to the local unitary cluster Jastrow (LUCJ) ansatz. We consider H$_4$, ferrocene and benzene molecules using up to 12 qubits as examples. Circuits are compiled to native gates for typical near-term architectures, and we assess the impact of circuit-level depolarizing noise on the method. We also provide a comparison of AFQMC and fixed-node approaches, demonstrating that AFQMC is more robust to errors, although extrapolations of the fixed-node energy reduce this discrepancy. Although the method can be used to reach chemical accuracy, the sampling cost to achieve this is high even for small active spaces, suggesting caution for the prospect of outperforming conventional QMC approaches.