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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| Vydané v: | Nature physics Ročník 18; číslo 1; s. 75 - 79 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Nature Publishing Group UK
01.01.2022
Nature Publishing Group Nature Publishing Group (NPG) |
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| ISSN: | 1745-2473, 1745-2481 |
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| Abstract | 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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| AbstractList | 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. 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. 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. |
| Author | Rudinger, Kenneth Blume-Kohout, Robin Proctor, Timothy Young, Kevin Nielsen, Erik |
| Author_xml | – sequence: 1 givenname: Timothy orcidid: 0000-0003-0219-8930 surname: Proctor fullname: Proctor, Timothy email: tjproct@sandia.gov organization: Quantum Performance Laboratory, Sandia National Laboratories, Quantum Performance Laboratory, Sandia National Laboratories – sequence: 2 givenname: Kenneth orcidid: 0000-0002-3038-4389 surname: Rudinger fullname: Rudinger, Kenneth organization: Quantum Performance Laboratory, Sandia National Laboratories, Quantum Performance Laboratory, Sandia National Laboratories – sequence: 3 givenname: Kevin orcidid: 0000-0002-4679-4542 surname: Young fullname: Young, Kevin organization: Quantum Performance Laboratory, Sandia National Laboratories, Quantum Performance Laboratory, Sandia National Laboratories – sequence: 4 givenname: Erik surname: Nielsen fullname: Nielsen, Erik organization: Quantum Performance Laboratory, Sandia National Laboratories, Quantum Performance Laboratory, Sandia National Laboratories – sequence: 5 givenname: Robin orcidid: 0000-0001-8134-948X surname: Blume-Kohout fullname: Blume-Kohout, Robin organization: Quantum Performance Laboratory, Sandia National Laboratories, Quantum Performance Laboratory, Sandia National Laboratories |
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| Snippet | 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... |
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| SubjectTerms | 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 |
| Title | Measuring the capabilities of quantum computers |
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