Measuring the capabilities of quantum computers

Quantum computers can now run interesting programs, but each processor’s capability—the set of programs that it can run successfully—is limited by hardware errors. These errors can be complicated, making it difficult to accurately predict a processor’s capability. Benchmarks can be used to measure c...

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Bibliographic Details
Published in:Nature physics Vol. 18; no. 1; pp. 75 - 79
Main Authors: Proctor, Timothy, Rudinger, Kenneth, Young, Kevin, Nielsen, Erik, Blume-Kohout, Robin
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 01.01.2022
Nature Publishing Group
Nature Publishing Group (NPG)
Subjects:
639/705/1042
639/766/259
639/766/483
Algorithms
Atomic
Benchmarks
Circuits
Classical and Continuum Physics
Complex Systems
Computational science
Condensed Matter Physics
Hardware
Information theory and computation
Mathematical and Computational Physics
MATHEMATICS AND COMPUTING
Microprocessors
Molecular
Optical and Plasma Physics
Physics
Physics and Astronomy
Processors
Quantum computers
Quantum computing
Quantum physics
Qubits (quantum computing)
Standard error
Theoretical
ISSN:1745-2473, 1745-2481
Online Access:Get full text
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Summary:Quantum computers can now run interesting programs, but each processor’s capability—the set of programs that it can run successfully—is limited by hardware errors. These errors can be complicated, making it difficult to accurately predict a processor’s capability. Benchmarks can be used to measure capability directly, but current benchmarks have limited flexibility and scale poorly to many-qubit processors. We show how to construct scalable, efficiently verifiable benchmarks based on any program by using a technique that we call circuit mirroring. With it, we construct two flexible, scalable volumetric benchmarks based on randomized and periodically ordered programs. We use these benchmarks to map out the capabilities of twelve publicly available processors, and to measure the impact of program structure on each one. We find that standard error metrics are poor predictors of whether a program will run successfully on today’s hardware, and that current processors vary widely in their sensitivity to program structure. Evaluations of quantum computers across architectures need reliable benchmarks. A class of benchmarks that can directly reflect the structure of any algorithm shows that different quantum computers have considerable variations in performance.
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NA0003525
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
SAND-2021-12259J
ISSN:1745-2473
1745-2481
DOI:10.1038/s41567-021-01409-7