Lei Zhang, University of Maryland, and colleagues investigate a new approach to testing hybrid quantum-classical algorithms, which are key for near-term quantum computing but notoriously difficult to verify. Failure-guided fuzzing identifies problematic configurations by first locating non-convergent starting points and then refining quantum circuit parameters around them. Implementation on Variational Quantum Eigensolver and Quantum Approximate Optimisation Algorithm instances within Qiskit reveals that using failure information sharply enhances testing effectiveness compared to random approaches, with concolic seed discovery offering further advantages for specific workloads. These findings highlight a promising pathway towards more strong and reliable hybrid quantum-classical program testing. Failure-guided fuzzing substantially improves quantum circuit error detection A five-fold increase in detected crashes resulted from using failure-guided local fuzzing compared to random hybrid testing, overcoming a key barrier to robust verification. Hybrid quantum-classical (HQC) algorithms, such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimisation Algorithm (QAOA), delegate computationally intensive tasks between a quantum processor and a classical computer. This division of labour, while promising for near-term quantum devices with limited qubit counts, introduces a complex interplay of classical optimisation and quantum evaluation, creating numerous potential failure points. The expected number of crashes drops below one for even moderately sized quantum circuits, previously hindering error identification; this is because the search space for problematic configurations grows exponentially with the number of qubits and classical optimiser settings. Reusing information from previous failures significantly enhances the efficiency of hybrid quantum-classical program testing, especially when combined with targeted fuzzing around problematic configurations. This is important because the probability of finding errors diminishes exponentially with the number of qubits, making exhaustive testing impractical. The core principle behind failure-guided fuzzing is to leverage the knowledge gained from previous test runs to intelligently guide the search for new errors, rather than relying on purely random