Artificial Intelligence for Quantum Computing
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.
Cite
@article{arxiv.2411.09131,
title = {Artificial Intelligence for Quantum Computing},
author = {Yuri Alexeev and Marwa H. Farag and Taylor L. Patti and Mark E. Wolf and Natalia Ares and Alán Aspuru-Guzik and Simon C. Benjamin and Zhenyu Cai and Shuxiang Cao and Christopher Chamberland and Zohim Chandani and Federico Fedele and Ikko Hamamura and Nicholas Harrigan and Jin-Sung Kim and Elica Kyoseva and Justin G. Lietz and Tom Lubowe and Alexander McCaskey and Roger G. Melko and Kouhei Nakaji and Alberto Peruzzo and Pooja Rao and Bruno Schmitt and Sam Stanwyck and Norm M. Tubman and Hanrui Wang and Timothy Costa},
journal= {arXiv preprint arXiv:2411.09131},
year = {2025}
}
Comments
42 pages, 7 figures, 2 tables