论文标题
用于量子控制的软件工具:通过噪声和错误抑制来改善量子计算机性能
Software tools for quantum control: Improving quantum computer performance through noise and error suppression
论文作者
论文摘要
在设备不完美的设备和控制系统的存在下操纵量子计算硬件是实现有用的量子计算机的核心挑战。噪声的敏感性限制了嘈杂的中间量子量子(NISQ)设备的性能和能力,以及任何未来的量子计算技术。幸运的是,量子控制能够有效执行具有内置鲁棒性的量子逻辑操作和算法,而无需复杂的逻辑编码。在此手稿中,我们介绍了用于在量子计算研究中应用和集成量子控制的软件工具,以满足硬件研发团队,算法开发人员和最终用户的需求。我们提供了一组基于Python的经典软件工具,用于在量子计算软件堆栈的各个层中创建和部署优化的量子控制解决方案。我们描述了一种软件体系结构,利用高性能分布式云计算和本地自定义集成到硬件系统中,并解释关键功能如何与其他软件包和量子编程语言集成。我们的演示文稿包括详细的中央产品功能的数学概述,包括灵活的优化工具包,用于分析高维希尔伯特空间中噪声易感性的过滤功能以及噪声和硬件表征的新方法。提出伪代码是为了阐明这些任务的通用编程工作流程,并报告了用于数值密集任务的性能基准测试,从而突出了所选云计算体系结构的好处。最后,我们提出了一系列案例研究,该案例研究证明了使用这些工具在实际实验设置中用于陷阱离子和超导量子计算机硬件的应用。
Manipulating quantum computing hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers. Susceptibility to noise limits the performance and capabilities of noisy intermediate-scale quantum (NISQ) devices, as well as any future quantum computing technologies. Fortunately quantum control enables efficient execution of quantum logic operations and algorithms with built-in robustness to errors, without the need for complex logical encoding. In this manuscript we introduce software tools for the application and integration of quantum control in quantum computing research, serving the needs of hardware R&D teams, algorithm developers, and end users. We provide an overview of a set of python-based classical software tools for creating and deploying optimized quantum control solutions at various layers of the quantum computing software stack. We describe a software architecture leveraging both high-performance distributed cloud computation and local custom integration into hardware systems, and explain how key functionality is integrable with other software packages and quantum programming languages. Our presentation includes a detailed mathematical overview of central product features including a flexible optimization toolkit, filter functions for analyzing noise susceptibility in high-dimensional Hilbert spaces, and new approaches to noise and hardware characterization. Pseudocode is presented in order to elucidate common programming workflows for these tasks, and performance benchmarking is reported for numerically intensive tasks, highlighting the benefits of the selected cloud-compute architecture. Finally, we present a series of case studies demonstrating the application of quantum control solutions using these tools in real experimental settings for both trapped-ion and superconducting quantum computer hardware.