Distinguishing Quantum Software Bugs from Hardware Noise: A Statistical Approach

TL;DR

This paper introduces a statistical method to distinguish between quantum software bugs and hardware noise in NISQ devices, aiding developers in debugging quantum programs effectively.

Contribution

It presents a novel probabilistic approach tailored for the stochastic nature of quantum computation, validated through empirical experiments on key quantum algorithms.

Findings

Effective differentiation between bugs and noise demonstrated

Method shows high reliability in practical quantum software debugging

Applicable to various quantum algorithms and hardware setups

Abstract

Quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era presents significant challenges in differentiating quantum software bugs from hardware noise. Traditional debugging techniques from classical software engineering cannot directly resolve this issue due to the inherently stochastic nature of quantum computation mixed with noises from NISQ computers. To address this gap, we propose a statistical approach leveraging probabilistic metrics to differentiate between quantum software bugs and hardware noise. We evaluate our methodology empirically using well-known quantum algorithms, including Grover's algorithm, Deutsch-Jozsa algorithm, and Simon's algorithm. Experimental results demonstrate the efficacy and practical applicability of our approach, providing quantum software developers with a reliable analytical tool to identify and classify unexpected behavior in quantum programs.