Efficient Mutation Testing of Quantum Machine Learning Models

TL;DR

This paper extends mutation testing to quantum machine learning models, introducing new mutation operations and a directed mutation technique to improve fault detection and reduce redundancy.

Contribution

It defines novel mutation operations for quantum neural networks and proposes a directed mutation generation method to enhance testing efficiency.

Findings

Our approach produces a more diverse set of mutants.

It effectively exposes faults that traditional methods miss.

The method reduces redundant mutant circuits.

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.