Artificial intelligence for quantum computing

Yuri Alexeev, Marwa H. Farag, Taylor L. Patti, Mark E. Wolf (), Natalia Ares, Alán Aspuru-Guzik, Simon C. Benjamin, Zhenyu Cai, Shuxiang Cao, Christopher Chamberland, Zohim Chandani, Federico Fedele, Ikko Hamamura, Nicholas Harrigan, Jin-Sung Kim, Elica Kyoseva, Justin G. Lietz, Tom Lubowe, Alexander McCaskey, Roger G. Melko, Kouhei Nakaji, Alberto Peruzzo, Pooja Rao, Bruno Schmitt, Sam Stanwyck, Norm M. Tubman, Hanrui Wang and Timothy Costa
Additional contact information
Yuri Alexeev: NVIDIA Corporation
Marwa H. Farag: NVIDIA Corporation
Taylor L. Patti: NVIDIA Corporation
Mark E. Wolf: NVIDIA Corporation
Natalia Ares: University of Oxford, Department of Engineering Science
Alán Aspuru-Guzik: NVIDIA Corporation
Simon C. Benjamin: Quantum Motion
Zhenyu Cai: University of Oxford, Department of Engineering Science
Shuxiang Cao: NVIDIA Corporation
Christopher Chamberland: NVIDIA Corporation
Zohim Chandani: NVIDIA Corporation
Federico Fedele: University of Oxford, Department of Engineering Science
Ikko Hamamura: NVIDIA Corporation
Nicholas Harrigan: NVIDIA Corporation
Jin-Sung Kim: NVIDIA Corporation
Elica Kyoseva: NVIDIA Corporation
Justin G. Lietz: NVIDIA Corporation
Tom Lubowe: NVIDIA Corporation
Alexander McCaskey: NVIDIA Corporation
Roger G. Melko: University of Waterloo, Department of Physics and Astronomy
Kouhei Nakaji: NVIDIA Corporation
Alberto Peruzzo: Qubit Pharmaceuticals
Pooja Rao: NVIDIA Corporation
Bruno Schmitt: NVIDIA Corporation
Sam Stanwyck: NVIDIA Corporation
Norm M. Tubman: NASA Ames Research Center
Hanrui Wang: University of California Los Angeles, Computer Science Department
Timothy Costa: NVIDIA Corporation

Nature Communications, 2025, vol. 16, issue 1, 1-19

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-65836-3

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