Artificial Intelligence for Quantum Computing

TL;DR

This paper reviews how cutting-edge AI techniques are being applied to address hardware and software challenges in quantum computing, highlighting current progress and future opportunities in this interdisciplinary field.

Contribution

It provides a comprehensive overview of AI's role in advancing quantum computing hardware and software, fostering cross-disciplinary collaboration.

Findings

AI techniques are improving quantum device design

AI is aiding in quantum error correction and optimization

Future AI applications could accelerate quantum algorithm development

Abstract

Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI's data-driven learning capabilities, and in fact, many of QC's biggest scaling challenges may ultimately rest on developments in AI. However, bringing leading techniques from AI to QC requires drawing on disparate expertise from arguably two of the most advanced and esoteric areas of computer science. Here we aim to encourage this cross-pollination by reviewing how state-of-the-art AI techniques are already advancing challenges across the hardware and software stack needed to develop useful QC - from device design to applications. We then close by examining its future opportunities and obstacles in this space.