Assessing Superposition-Targeted Coverage Criteria for Quantum Neural Networks

TL;DR

This paper introduces superposition-targeted coverage criteria to evaluate the thoroughness of testing in Quantum Neural Networks, addressing current gaps in testing methods and assessing their effectiveness, scalability, and robustness.

Contribution

It proposes novel coverage criteria for QNN testing and provides a comprehensive empirical evaluation of their effectiveness and limitations.

Findings

Coverage criteria effectively quantify test adequacy.

Criteria show potential for larger quantum circuits.

Robustness is challenged by quantum noise and measurement limits.

Abstract

Quantum Neural Networks (QNNs) have achieved initial success in various tasks by integrating quantum computing and neural networks. However, growing concerns about their reliability and robustness highlight the need for systematic testing. Unfortunately, current testing methods for QNNs remain underdeveloped, with limited practical utility and insufficient empirical evaluation. As an initial effort, we design a set of superposition-targeted coverage criteria to evaluate QNN state exploration embedded in test suites. To characterize the effectiveness, scalability, and robustness of the criteria, we conduct a comprehensive empirical study using benchmark datasets and QNN architectures. We first evaluate their sensitivity to input diversity under multiple data settings, and analyze their correlation with the number of injected faults. We then assess their scalability to increasing circuit scales. The robustness is further studied under practical quantum constraints including insufficient measurement and quantum noise. The results demonstrate the effectiveness of quantifying test adequacy and the potential applicability to larger-scale circuits and realistic quantum execution, while also revealing some limitations. Finally, we provide insights and recommendations for future QNN testing.