Real-time Sign-Problem-Suppressed Quantum Monte Carlo Algorithm For Noisy Quantum Circuit Simulations

TL;DR

This paper introduces a real-time quantum Monte Carlo algorithm that effectively suppresses the sign problem, enabling faster and more accurate classical simulations of open quantum system dynamics, including complex non-Markovian regimes.

Contribution

The paper proposes a novel population dynamics-based Monte Carlo method that continuously suppresses the sign problem during simulation of open quantum systems.

Findings

Significant speedups over existing quantum trajectory methods.

Successful convergence to exact solutions in non-Markovian regimes.

Enhanced efficiency in simulating various quantum computing processes.

Abstract

We present a real-time quantum Monte Carlo algorithm that simulates the dynamics of open quantum systems by stochastically compressing and evolving the density matrix under both Markovian and non-Markovian master equations. Our algorithm uses population dynamics to continuously suppress the sign problem, preventing its accumulation throughout the evolution. We apply it to a variety of quantum circuits and demonstrate significant speedups over state-of-art quantum trajectory methods and convergence to exact solutions even in non-Markovian regimes where trajectory methods fail. Our approach improves the efficiency of classical simulation of gate-based quantum computing, quantum annealing, and general open system dynamics.