Software tools for quantum control: improving quantum computer performance through noise and error suppression. (30th September 2021)

• Record Type: Journal Article
• Authors: Ball, Harrison; Biercuk, Michael J; Carvalho, Andre R R; Chen, Jiayin; Hush, Michael; De Castro, Leonardo A; Li, Li; Liebermann, Per J; Slatyer, Harry J; Edmunds, Claire; Frey, Virginia; Hempel, Cornelius; Milne, Alistair
• Is Part Of: Quantum science and technology. Volume 6:Number 4(2021)
• Publication Date: 2021-09-30
• Subjects: quantum control -- quantum software -- quantum technology -- optimized quantum gates -- error suppression; Quantum theory -- Periodicals; Quantum theory; Periodicals; 530
• DOI: 10.1088/2058-9565/abdca6
• Languages: English
• ISSNs: 2058-9565

Abstract

Abstract: 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.