AI 中文总结
本文以带误差缓解的量子储层计算为例，深入探讨量子软件开发中实验跟踪细节，解释其基本概念及量子计算对跟踪的要求，展示详细开发过程及实验跟踪方法，并将相关知识推广到更广泛的量子开发过程。
AI 中文摘要
量子计算机比以往任何时候都更广泛可用，使该领域更容易进入且更广泛传播。从业者来自广泛领域，使用量子计算方法针对各种问题进行实验和研究。当前文献表明，开发者在量子软件开发中遵循特定方法，通常还会配备一套匹配的工具。然而，在这种新范式下，实践和工具中仍存在未解决的领域。在本文中，我们深入探讨量子软件开发中实验跟踪的细节。我们解释实验跟踪的基本概念，并详细说明量子计算在本质上对跟踪实践的要求。鉴于硬件的实验状态和不断发展的软件，必须监控量子执行，汇总边际收益以获得最佳结果，并检测错误源。在我们以带误差缓解的混沌时间序列数据预测的量子储层计算为例的案例研究中，我们展示了详细的量子软件开发过程，并描述了在整个开发过程中如何跟踪实验。然后，我们将这些知识推广到更广泛的量子开发过程中。
英文摘要
Quantum computers are more widely available than ever, making the field more accessible and widespread. Practitioners are coming from a wide range of domains, conducting experiments and research using quantum computing approaches across a variety of problems. The current literature suggests that developers follow certain methodologies in quantum software development, often with a matching set of tools provided. Yet with the novel paradigm, there are areas that remain unaddressed in practices and tools. In this article, we go into the details of experiment tracking in quantum software development. We explain the basic concept of experiment tracking and detail how, in essence, quantum computing sets demands on tracking practices. Given the experimental state of hardware and the constantly evolving software, quantum execution must be monitored, marginal gains aggregated for the best outcome, and error sources detected. In our case study, quantum reservoir computing for chaotic time series data prediction with error mitigation, we present a detailed quantum software development process and describe how experiments can be tracked throughout development. We then generalize this knowledge into the broader quantum development process.
Comments12 pages, 7 figures, 5 tables, presented at QCE26
