Abstract
The last decade has witnessed remarkable progress in the development of quantum technologies. Although fault-tolerant devices likely remain years away, the noisy intermediate-scale quantum devices of today may be leveraged for other purposes. Leading candidates are variational quantum algorithms (VQAs), which have been developed for applications including chemistry, optimization, and machine learning, but whose implementations on quantum devices have yet to demonstrate improvements over classical capabilities. In this Perspective, we propose a variety of ways that the performance of VQAs could be informed by quantum optimal control theory. A major theme throughout is the need for sufficient control resources in VQA implementations; we discuss different ways this need can manifest, outline a variety of open questions, and look to the future.Received 11 September 2020DOI:https://doi.org/10.1103/PRXQuantum.2.010101Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum controlQuantum information processingQuantum Information
Highlights
The development of large-scale, fault-tolerant quantum computers would enable diverse and disruptive applications, such as the ability to break modern encryption protocols using Shor’s factoring algorithm [1] and to efficiently simulate the dynamics of complex quantum systems [2]
The reach of technology and the focus of the community have broadened “from pulses to circuits.”. In this Perspective, we explore how the development of variational quantum algorithms (VQAs) can be informed by going from the circuit level “back again” to the pulse level to strengthen ties to quantum optimal control (QOC) and leverage results and tools from this well-developed field
V we present a hierarchy of abstractions in variational optimization that serves to provide a common framework for VQA and QOC experiments
Summary
The development of large-scale, fault-tolerant quantum computers would enable diverse and disruptive applications, such as the ability to break modern encryption protocols using Shor’s factoring algorithm [1] and to efficiently simulate the dynamics of complex quantum systems [2]. Hardware implementations of VQAs have not yet demonstrated improvements over the capabilities of classical computers, and the aim of this Perspective is to examine how progress can be made towards meeting this milestone in the future This Perspective considers VQAs, their associated challenges, and potential paths forward, through the lens of quantum optimal control. This is followed by an in-depth discussion of QOC-motivated future research directions aimed at addressing four important challenges associated with VQAs: ansatz selection, optimization landscapes, noise, and robustness In each of these cases, we emphasize the need for appropriate control resources to enhance the performance of VQAs. we conclude in Sec. VI with a look ahead
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