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Testing and Debugging Quantum Programs: The Road to 2030

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arxiv 2405.09178 v2 pith:DU7JZQS5 submitted 2024-05-15 cs.SE quant-ph

Neilson Carlos Leite Ramalho , Higor Amario de Souza , Marcos Lordello Chaim This is my paper
classification cs.SEquant-ph
keywords quantumdebuggingprogramstestingcomputingchallengesclassicalregarding
verification ladder T0 review T1 audit T2 compute T3 formal
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Quantum computing has existed in the theoretical realm for several decades. Recently, quantum computing has re-emerged as a promising technology to solve problems that a classical computer could take hundreds of years to solve. However, there are challenges and opportunities for academics and practitioners regarding software engineering practices for testing and debugging quantum programs. This paper presents a roadmap for addressing these challenges, pointing out the existing gaps in the literature and suggesting research directions. We discuss the limitations caused by noise, the no-cloning theorem, the lack of a standard architecture for quantum computers, among others. Regarding testing, we highlight gaps and opportunities related to transpilation, mutation analysis, input states with hybrid interfaces, program analysis, and coverage. For debugging, we present the current strategies, including classical techniques applied to quantum programs, quantum-specific assertions, and quantum-related bug patterns. We introduce a conceptual model to illustrate concepts regarding the testing and debugging of quantum programs and the relationship between them. Those concepts are used to identify and discuss research challenges to cope with quantum programs through 2030, focusing on the interfaces between classical and quantum computing and on creating testing and debugging techniques that take advantage of the unique quantum computing characteristics.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Backend-Aware Graph Learning for Denoising Outcome Distributions in Quantum Program Testing

    cs.SE 2026-07 conditional novelty 6.0 of 10

    Q-BRIDGE, a graph-transformer denoiser conditioned on backend features, reconstructs ideal outcome distributions from noisy quantum executions and markedly improves oracle-based bug detection.

  2. Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling

    cs.LG 2026-07 conditional novelty 5.0 of 10

    The paper argues that probabilistic scaling alone cannot fix the validity gap in quantum circuit generation, so quantum code assistants must build verification into generation rather than filter outputs after the fact.

  3. Taxonomy of migration scenarios for Qiskit refactoring using LLMs

    cs.SE 2025-06 conditional novelty 5.0 of 10

    LLMs can generate a structured taxonomy of Qiskit migration and refactoring scenarios that largely overlaps with an expert-built taxonomy and adds some scenarios.

  4. Distinguishing Quantum Software Bugs from Hardware Noise: A Statistical Approach

    cs.SE 2025-07 conditional novelty 4.0 of 10

    The paper claims that bugs shift the set of most probable output states away from the desired states, while sub-threshold noise only spreads the distribution without changing those top states.

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