Comparative Study of Sampling-Based Simulation Costs of Noisy Quantum Circuits

TL;DR

This paper compares the costs of sampling-based classical simulation methods for noisy quantum circuits, revealing how different techniques perform under varying noise levels to better understand classical-quantum computational boundaries.

Contribution

It introduces techniques to reduce simulation costs and characterizes the performance of stabilizer-state sampling and Heisenberg propagation under stochastic noise.

Findings

Stabilizer-state sampling is more efficient in low noise regimes.

Heisenberg propagation outperforms in high noise regimes.

High depolarizing noise improves the scaling of simulation methods.

Abstract

Noise in quantum operations often negates the advantage of quantum computation. However, most classical simulations of quantum computers calculate the ideal probability amplitudes either storing full state vectors or using sophisticated tensor network contractions. Here, we investigate sampling-based classical simulation methods for noisy quantum circuits. Specifically, we characterize the simulation costs of two major schemes, stabilizer-state sampling of magic states and Heisenberg propagation, for quantum circuits being subject to stochastic Pauli noise, such as depolarizing and dephasing noise. To this end, we introduce several techniques for the stabilizer-state sampling to reduce the simulation costs under such noise. It revealed that in the low noise regime, stabilizer-state sampling results in a smaller sampling cost, while Heisenberg propagation is better in the high noise regime. Furthermore, for a high depolarizing noise rate $\sim 10\%$, these methods provide better scaling compared to that given by the low-rank stabilizer decomposition. We believe that these knowledge of classical simulation costs is useful to squeeze possible quantum advantage on near-term noisy quantum devices as well as efficient classical simulation methods.