Tong Shen, Daniel A. Lidar
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 for a broad class of noisy-circuit Liouvillians of practical and experimental interest. We apply it to a variety of quantum circuits and demonstrate significant speedups and scaling improvements over state-of-the-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.