Efficient Mutation Testing of Quantum Machine Learning Models

2026-04-30

AI Summary

This work addresses the challenge of effectively detecting implementation faults in quantum neural networks by proposing an efficient mutation testing approach. The method introduces novel quantum circuit mutation operators and a targeted mutation generation strategy, which collectively reduce redundant mutants while enhancing the precision of fault injection. By generating mutants with greater diversity and representativeness, the approach significantly strengthens the ability of test suites to validate quantum neural network implementations, uncovering defects that are often missed by conventional testing techniques. Experimental results demonstrate the effectiveness of the proposed method in improving the reliability of quantum machine learning models.

Abstract

Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer parameters than their classical counterparts. Due to the increasing complexity of quantum machine learning models, it is necessary to verify that the implementation of these models satisfy the design specification and be free of bugs and faults. Mutation testing is a promising avenue to identify faulty quantum circuits that do not meet design specifications or contain defects by intentionally inserting faults into the quantum circuit. It is necessary to define mutation operations to inject faults into quantum circuits to ensure that a test suite is robust enough to evaluate an implementation against its design specification. In this paper, we extend mutation testing to quantum machine learning applications, primarily quantum neural network models. Specifically, this paper makes two important contributions. We define new mutation operations for efficient fault insertion compared to state-of-the-art approaches. We also present a directed mutation generation technique to reduce redundant mutant circuits. Extensive experimental evaluation demonstrates that our approach generates a more diverse and representative set of mutants, effectively addressing faults that traditional techniques fail to expose.

Problem

• quantum machine learning
• mutation testing
• quantum neural networks
• fault detection
• quantum circuit verification

Innovation

• mutation testing
• quantum machine learning
• quantum neural networks
• fault injection
• mutant reduction