Effectively manipulating quantum computing (QC) hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers. Susceptibility to noise critically limits the performance and capabilities of today’s so-called noisy intermediate-scale quantum devices, as well as any future QC technologies. Fortunately, quantum control enables efficient execution of quantum logic operations and quantum algorithms with built-in robustness to errors, and without the need for complex logical encoding. In this manuscript we introduce software tools for the application and integration of quantum control in QC 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 QC 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 key features including a flexible optimization toolkit, engineering-inspired 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 derived from these tools in real experimental settings using both trapped-ion and superconducting quantum computer hardware.

在存在不完善的设备和控制系统的情况下有效地操纵量子计算 (QC) 硬件是实现有用的量子计算机的核心挑战。对噪声的敏感性严重限制了当今所谓的噪声中等规模量子设备以及任何未来 QC 技术的性能和能力。幸运的是，量子控制能够高效执行量子逻辑运算和量子算法，并具有内置的错误鲁棒性，并且不需要复杂的逻辑编码。在这份手稿中，我们介绍了量子控制在 QC 研究中的应用和集成的软件工具，以满足硬件研发团队、算法开发人员和最终用户的需求。我们概述了一组基于 Python 的经典软件工具，用于在 QC 软件堆栈的各个层创建和部署优化的量子控制解决方案。我们描述了一种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的软件架构，并解释了关键功能如何与其他软件包和量子编程语言集成。我们的演讲包括关键特性的详细数学概述，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敏感性的工程启发滤波器函数，以及噪声和硬件表征的新方法。提供伪代码是为了阐明这些任务的常见编程工作流程，并且针对数值密集型任务报告了性能基准测试，突出了所选云计算架构的优势。最后，我们展示了一系列案例研究，展示了从这些工具派生的量子控制解决方案在使用俘获离子和超导量子计算机硬件的实际实验环境中的应用。