The complexity of NISQ
Abstract
The recent proliferation of NISQ devices has made it imperative to understand their power. In this work, we define and study the complexity class , which encapsulates problems that can be efficiently solved by a classical computer with access to noisy quantum circuits. We establish super-polynomial separations in the complexity among classical computation, , and fault-tolerant quantum computation to solve some problems based on modifications of Simon's problems. We then consider the power of for three well-studied problems. For unstructured search, we prove that cannot achieve a Grover-like quadratic speedup over classical computers. For the Bernstein-Vazirani problem, we show that only needs a number of queries logarithmic in what is required for classical computers. Finally, for a quantum state learning problem, we prove that is exponentially weaker than classical computers with access to noiseless constant-depth quantum circuits.
Copyright and License
© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Acknowledgement
We would like to thank Isaac Chuang for valuable conversations on complexity classes for learning theory, John Preskill for inspiring discussions on NISQ and its complexity class formulation, and John Wright for bringing76 and related works to our attention. S.C. is supported by NSF Award 2103300. J.C. is supported by a Junior Fellowship from the Harvard Society of Fellows, as well as in part by the Department of Energy under grant DE-SC0007870. H.H. is supported by a Google Ph.D. fellowship.
Contributions
S.C., J.C., H.H., and J.L. contributed equally to this work. All authors developed the theoretical aspects of this work and wrote the manuscript together.
Conflict of Interest
The authors declare no competing interests.
Errata
A Publisher Correction to this article was published on 12 February 2024
Files
Name | Size | Download all |
---|---|---|
md5:31a094efc20685a6e70a51cf91cd75e8
|
921.5 kB | Preview Download |
md5:e7e39ab99186c4ff00ec6fa72de4600b
|
757.8 kB | Preview Download |
Additional details
- National Science Foundation
- DMS-2103300
- Harvard University
- United States Department of Energy
- DE-SC0007870
- Google PhD Fellowship
- Accepted
-
2023-08-25Accepted paper
- Available
-
2023-09-23Published online
- Caltech groups
- Institute for Quantum Information and Matter