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

TL;DR

This paper reviews the intersection of quantum algorithms and Monte Carlo methods in physics and chemistry, highlighting recent developments, challenges, and opportunities for integrating quantum computing with classical sampling techniques.

Contribution

It provides a comprehensive overview of recent advances in quantum Monte Carlo methods and their potential to enhance classical sampling in physics, chemistry, and related fields.

Findings

Recent quantum algorithms improve energy estimation accuracy.

Quantum hardware can accelerate sampling in classical models.

Rapid growth in research indicates high potential for quantum-Monte Carlo integration.

Abstract

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 in quantum algorithms. These include refined energy estimators, parameter optimization, real and imaginary-time dynamics, and variational circuits. Conversely, we will review new ideas in 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.