Failure-Guided Fuzzing for Hybrid Quantum-Classical Programs

2026-05-13

AI Summary

This work addresses the challenge of efficiently uncovering non-convergent or faulty configurations in hybrid quantum-classical (HQC) programs under limited testing budgets, particularly within the joint search space of classical optimizer hyperparameters and quantum circuit parameters. We propose a two-stage failure-guided fuzzing approach that first identifies non-convergent seed configurations and then perturbs quantum parameters in their neighborhoods to enhance fault detection efficiency. As the first study to introduce failure-guided mechanisms into HQC testing, we demonstrate that reusing failure information significantly improves fuzzing effectiveness and reveal that seed generation strategies exhibit workload-dependent performance. Experiments on Qiskit with VQE and QAOA MaxCut show that our method substantially outperforms random testing; concolic seeds yield additional gains for VQE but exhibit reduced stability in QAOA.

Abstract

Hybrid quantum-classical (HQC) algorithms, such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA), are central to near-term quantum computing but remain challenging to test. Sampling-based fuzzing can expose faulty or non-convergent configurations, but under realistic execution budgets, it may miss failure-prone regions in the joint space of classical optimizer settings and quantum circuit parameters. This paper studies failure-guided fuzzing for HQC programs. It models a hybrid input as a pair of classical optimizer hyperparameters and quantum circuit parameters, and evaluates a two-phase strategy that first searches for non-convergent seeds and then locally fuzzes circuit parameters around those seeds. To understand where the gains come from, five budgeted strategies are compared: random hybrid testing, classical enumeration without fuzzing, random-seed local fuzzing, enumeration-seed local fuzzing, and concolic-seed local fuzzing. The study is implemented on a VQE instance and a QAOA MaxCut instance in Qiskit. The results show that failure-guided local fuzzing is the main driver of improvement over random testing, while concolic seed discovery provides additional benefits on VQE but is less stable on QAOA. These findings suggest that reusing failure information is a promising direction for HQC testing, but that the value of concolic seed discovery is workload-dependent.

Problem

• hybrid quantum-classical programs
• fuzzing
• failure detection
• parameter space
• testing

Innovation

• failure-guided fuzzing
• hybrid quantum-classical programs
• local fuzzing
• concolic testing
• non-convergent seeds