Direct Randomized Benchmarking for Multiqubit Devices
Open accessAbstract
Benchmarking methods that can be adapted to multiqubit systems are essential for assessing the overall or "holistic" performance of nascent quantum processors. The current industry standard is Clifford randomized benchmarking (RB), which measures a single error rate that quantifies overall performance. But, scaling Clifford RB to many qubits is surprisingly hard. It has only been performed on one, two, and three qubits as of this writing. This reflects a fundamental inefficiency in Clifford RB: the n-qubit Clifford gates at its core have to be compiled into large circuits over the one- and two-qubit gates native to a device. As n grows, the quality of these Clifford gates quickly degrades, making Clifford RB impractical at relatively low n. In this Letter, we propose a direct RB protocol that mostly avoids compiling. Instead, it uses random circuits over the native gates in a device, which are seeded by an initial layer of Clifford-like randomization. We demonstrate this protocol experimentally on two to five qubits using the publicly available ibmqx5. We believe this to be the greatest number of qubits holistically benchmarked, and this was achieved on a freely available device without any special tuning up. Our protocol retains the simplicity and convenient properties of Clifford RB: it estimates an error rate from an exponential decay. But, it can be extended to processors with more qubits-we present simulations on 10+ qubits-and it reports a more directly informative and flexible error rate than the one reported by Clifford RB. We show how to use this flexibility to measure separate error rates for distinct sets of gates, and we use this method to estimate the average error rate of a set of cnot gates.
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Existing works have already proposed to adopt randomly generated benchmarks for evaluating the error rates of different gate operations regarding a particular NISQ device.
“Existing works have already proposed to adopt randomly generated benchmarks for evaluating the error rates of diferent gate operations regarding a particular NISQ device [83, 84, 106].”
From QASMBench: A Low-Level Quantum Benchmark Suite for NISQ Evaluation and Simulation (2023) · cites this paper as [106]
Detailed modeling predicts that the SPAM error is dominated by measurement error for 171Yb+.
“however, detailed modeling predicts that the SPAM error is dominated by measurement error for 171 Yb þ [47,48].”
From A Race-Track Trapped-Ion Quantum Processor (2023) · cites this paper as [48]
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Self-citationProbing Context-Dependent Errors in Quantum Processors
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Self-citationScalable Randomized Benchmarking of Quantum Computers Using Mirror Circuits
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Fast Estimation of Sparse Quantum Noise
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Experimental Characterization of Crosstalk Errors with Simultaneous Gate Set Tomography
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Learning Correlated Noise in a 39-Qubit Quantum Processor
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Practical Introduction to Benchmarking and Characterization of Quantum Computers
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Shadow estimation of gate-set properties from random sequences
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Demonstrating Scalable Randomized Benchmarking of Universal Gate Sets
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Quantum algorithms for scientific computing
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Performance Optimization for Drift-Robust Fidelity Improvement of Two-Qubit Gates
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Fast Bayesian Tomography of a Two-Qubit Gate Set in Silicon
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Modular software for real-time quantum control systems
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Randomized benchmarking in the analogue setting
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Practical verification protocols for analog quantum simulators
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Fully Scalable Randomized Benchmarking Without Motion Reversal
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Self-citationOperational, gauge-free quantum tomography
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Self-citationQuantum reservoir computing for photonic entanglement witnessing
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Position Papers for the ASCR Workshop on Basic Research Needs in Quantum Computing and Networking
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Measuring the Capabilities of Quantum Computers
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Self-citationLow-temperature environments for quantum computation and quantum simulation*
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Scalable Evaluation of Quantum-Circuit Error Loss Using Clifford Sampling
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Randomized Benchmarking beyond Groups
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Self-citationWildcard error: Quantifying unmodeled errors in quantum processorsPreprint
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Self-citationCalibrating quantum gates up to 52 qubits in a superconducting processor
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Functional simulation of real-time quantum control software
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Measuring error rates of mid-circuit measurements
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Self-citationHands-on introduction to randomized benchmarking
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Estimation of correlations and nonseparability in quantum channels via unitarity benchmarking
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SSQEM: Semi-Supervised Quantum Error Mitigation
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A Theory of Direct Randomized Benchmarking
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Self-citationBenchmarking quantum processors with a single qubit
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Shadow estimation of gate-set properties from random sequences
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Machine Learning for Quantum Hardware Performance Assessment
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Extremum Seeking Control of Quantum Gates
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Scalable Full-Stack Benchmarks for Quantum Computers
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Modular Architecture for Classical Simulation of Quantum Circuits
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Partial randomized benchmarking
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Model validation and error attribution for a drifting qubit
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- Authors
- Proctor, T. J., Carignan-Dugas, A., Rudinger, K., Nielsen, E., Blume-Kohout, R., & Young, K.
- Affiliations
- Sandia National Laboratories California (US)
- University of Waterloo (CA)
- Sandia National Laboratories (US)
- ORCID
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2019-07-19
- Volume
- 123
- Issue
- 3
- Pages
- 030503-030503
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2884175209
- Cited by
- 118
- References
- 71
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 9 · 2023: 19 · 2022: 21 · 2021: 24 · 2020: 22 · 2019: 4
- FWCI
- 8.10
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Physical Review Letters (publishedVersion)
- arXiv (Cornell University) (publishedVersion)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- U.S. Department of Energy (DE-NA0003525)
- Office of the Director of National Intelligence
- Intelligence Advanced Research Projects Activity
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- randomized benchmarking, gate error rates, IBM Quantum Experience, quantum hardware characterization, exponential decay
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