Artificial Intelligence for Quantum Computing
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914200882774016 |
|---|---|
| author | Alexeev, Yuri Farag, Marwa H. Patti, Taylor L. Wolf, Mark E. Ares, Natalia Aspuru-Guzik, Alán Benjamin, Simon C. Cai, Zhenyu Cao, Shuxiang Chamberland, Christopher Chandani, Zohim Fedele, Federico Hamamura, Ikko Harrigan, Nicholas Kim, Jin-Sung Kyoseva, Elica Lietz, Justin G. Lubowe, Tom McCaskey, Alexander Melko, Roger G. Nakaji, Kouhei Peruzzo, Alberto Rao, Pooja Schmitt, Bruno Stanwyck, Sam Tubman, Norm M. Wang, Hanrui Costa, Timothy |
| author_facet | Alexeev, Yuri Farag, Marwa H. Patti, Taylor L. Wolf, Mark E. Ares, Natalia Aspuru-Guzik, Alán Benjamin, Simon C. Cai, Zhenyu Cao, Shuxiang Chamberland, Christopher Chandani, Zohim Fedele, Federico Hamamura, Ikko Harrigan, Nicholas Kim, Jin-Sung Kyoseva, Elica Lietz, Justin G. Lubowe, Tom McCaskey, Alexander Melko, Roger G. Nakaji, Kouhei Peruzzo, Alberto Rao, Pooja Schmitt, Bruno Stanwyck, Sam Tubman, Norm M. Wang, Hanrui Costa, Timothy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09131 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence for Quantum Computing Alexeev, Yuri Farag, Marwa H. Patti, Taylor L. Wolf, Mark E. Ares, Natalia Aspuru-Guzik, Alán Benjamin, Simon C. Cai, Zhenyu Cao, Shuxiang Chamberland, Christopher Chandani, Zohim Fedele, Federico Hamamura, Ikko Harrigan, Nicholas Kim, Jin-Sung Kyoseva, Elica Lietz, Justin G. Lubowe, Tom McCaskey, Alexander Melko, Roger G. Nakaji, Kouhei Peruzzo, Alberto Rao, Pooja Schmitt, Bruno Stanwyck, Sam Tubman, Norm M. Wang, Hanrui Costa, Timothy Quantum Physics 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. |
| title | Artificial Intelligence for Quantum Computing |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2411.09131 |