Artificial intelligence for quantum computing
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.
Article Details
Authors (28)
Yuri Alexeev
Marwa H. Farag
Taylor L. Patti
Mark E. Wolf
Natalia Ares
Alán Aspuru-Guzik
Chemical Physics Theory Group, Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada
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
Applied Physics Group, NASA Ames Research Center
Hanrui Wang
Timothy Costa