[论文解读] Understanding Bugs in Quantum Simulators: An Empirical Study

Krishna Upadhyay, Moshood Fakorede+1 more|arXiv (Cornell University)|Mar 24, 2026

Quantum Computing Algorithms and Architecture被引用 0

一句话总结

该论文分析了12个开源量子模拟器中的394个确认错误，以刻画根本原因、表现形式、起源及发现方法，揭示了以用户驱动为主的发现过程以及与经典基础设施相关的显著故障。

ABSTRACT

Quantum simulators are a foundational component of the quantum software ecosystem. They are widely used to develop and debug quantum programs, validate compiler transformations, and support empirical claims about correctness and performance. In the absence of large-scale quantum hardware, simulator outputs are often treated as ground truth for algorithm development and system evaluation. However, quantum simulators also introduce unique implementation challenges. They must faithfully emulate quantum behavior while executing on classical hardware, requiring complex representations of quantum state evolution, operator composition, and noise modeling. Yet, we still lack a large-scale and in-depth study of failures in quantum simulators. To bridge this gap, this work presents a comprehensive empirical study of bugs in widely used open-source quantum simulators. We analyze 394 confirmed bugs from 12 simulators and manually categorize them based on root causes, failure manifestations, affected components, and discovery mechanisms. Our study reveals several key findings. First, bug discovery is largely user-driven, with most crashes, exceptions, and resource-related failures not detected by automated testing and identified after deployment. Second, logical correctness failures are widespread and often silent, producing plausible but incorrect outputs without triggering crashes or explicit error signals. Third, many critical failures originate in classical simulator infrastructure, such as memory management, indexing, configuration, and dependency compatibility, rather than in core quantum execution logic. These findings provide new insights into the reliability challenges of quantum simulators and highlight opportunities to improve testing and validation practices in the quantum software ecosystem.

研究动机与目标

• Identify root causes of defects in software-based quantum simulators.
• Characterize how simulator issues manifest (outputs, crashes, silent failures).
• Localize failures to quantum execution vs. classical infrastructure components.
• Assess how issues are discovered and the effectiveness of testing practices.

提出的方法

• Collect closed issues from 12 standalone quantum simulators with PR-linked fixes.
• Manually filter to 394 confirmed issues resolved by patches.
• Annotate each issue across dimensions: category, root cause, manifestation, quantum component, software component, discovery.
• Ensure inter-annotator reliability with dual annotators and resolve disagreements.

Figure 1 . Typical architecture of quantum simulators, showing user-facing interfaces, circuit preparation, simulation backend, and supporting classical layers for acceleration and infrastructure.

实验结果

研究问题

• RQ1 RQ1: What are the root causes of issues in quantum simulators?
• RQ2 RQ2: How do issues manifest in quantum simulators?
• RQ3 RQ3: Where do critical failures originate within the simulator architecture?
• RQ4 RQ4: How are issues discovered and what does this reveal about testing effectiveness?

主要发现

• Issue discovery is largely user-driven (78.4%) while automated testing finds few issues (10.7%).
• Severe failures often escape internal testing and are found post-deployment.
• Logical correctness failures are common and often silent, producing incorrect outputs without errors.
• Many critical failures originate in classical infrastructure rather than core quantum logic.
• Ecosystem fragility, including dependencies and platform compatibility, frequently causes reliability problems.

Figure 2 . Overview of the data collection and analysis methodology, illustrating the multi-stage pipeline from initial project selection to the final taxonomic analysis of issues.