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Quantum Physics

arXiv:2411.09131 (quant-ph)
[Submitted on 14 Nov 2024]

Title:Artificial Intelligence for Quantum Computing

Authors:Yuri Alexeev, Marwa H. Farag, Taylor L. Patti, Mark E. Wolf,
Natalia Ares, Alan Aspuru-Guzik, Simon C. Benjamin, Zhenyu Cai, Zohim
Chandani, Federico Fedele, Nicholas Harrigan, Jin-Sung Kim, Elica
Kyoseva, Justin G. Lietz, Tom Lubowe, Alexander McCaskey, Roger G.
Melko, Kouhei Nakaji, Alberto Peruzzo, Sam Stanwyck, Norm M. Tubman,
Hanrui Wang, Timothy Costa
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    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.

Comments: 42 pages, 7 figures, 2 tables
Subjects: Quantum Physics (quant-ph)
Cite as:  arXiv:2411.09131 [quant-ph]
          (or arXiv:2411.09131v1 [quant-ph] for this version)
          https://doi.org/10.48550/arXiv.2411.09131
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Taylor Patti [view email]
[v1] Thu, 14 Nov 2024 02:11:16 UTC (1,346 KB)
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