A Coverage-Guided Testing Framework for Quantum Neural Networks

2024-11-03

arXiv.org

AI Summary

Verifying the correctness of quantum neural networks (QNNs) faces fundamental challenges due to non-classical quantum phenomena—such as superposition and entanglement—that defy conventional testing paradigms; existing methods lack coverage metrics for quantum state evolution. To address this, we propose QCov, the first quantum-aware test coverage criterion tailored for QNNs, which formally incorporates superposition and entanglement dynamics into test adequacy assessment, systematically quantifying coverage over the quantum state space. Leveraging QCov, we design a coverage-driven fuzzing framework that significantly improves defect detection across multiple QNN benchmarks (average +32.7%) and effectively guides the generation of high-coverage inputs, thereby enhancing model robustness and reliability. QCov establishes the first formal foundation for coverage-based testing of QNNs, bridging a critical gap in quantum machine learning verification and introducing a novel paradigm for trustworthy quantum AI systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Reasoning under Uncertainty: Stochastic Optimization
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Quantum Neural Networks (QNNs) combine quantum computing and neural networks, leveraging quantum properties such as superposition and entanglement to improve machine learning models. These quantum characteristics enable QNNs to potentially outperform classical neural networks in tasks such as quantum chemistry simulations, optimization problems, and quantum-enhanced machine learning. However, they also introduce significant challenges in verifying the correctness and reliability of QNNs. To address this, we propose QCov, a set of test coverage criteria specifically designed for QNNs to systematically evaluate QNN state exploration during testing, focusing on superposition and entanglement. These criteria help detect quantum-specific defects and anomalies. Extensive experiments on benchmark datasets and QNN models validate QCov's effectiveness in identifying quantum-specific defects and guiding fuzz testing, thereby improving QNN robustness and reliability.

Problem

• Ensuring reliability and safety of Quantum Neural Networks (QNNs)
• Addressing verification challenges due to quantum mechanics' non-classical nature
• Improving QNN robustness through systematic test coverage criteria

Innovation

• QCov coverage criteria for QNN state exploration
• Fuzz testing guided by quantum test diversity
• Cost-effective sampling for practical quantum scenarios