Fully Scalable Randomized Benchmarking Without Motion Reversal

We introduce , a protocol that streamlines traditional RB by using circuits consisting almost entirely of independent identically distributed (IID) layers of gates. BiRB reliably and efficiently extracts the average error rate of a Clifford gate set by sending tensor-product eigenstates of random Pauli operators through random circuits with IID layers. Unlike existing RB methods, BiRB does not use motion reversal circuits—i.e., circuits that implement the identity (or a Pauli) operator—which simplifies both the method and the theory proving its reliability. Furthermore, this simplicity enables scaling BiRB to many more qubits than the most widely used RB methods. Published by the American Physical Society 2024

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Publication Details

Journal
PRX Quantum
Published
2024-08-15
DOI
https://doi.org/10.1103/prxquantum.5.030334
Citations
10
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
3.47

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article

Fully Scalable Randomized Benchmarking Without Motion Reversal

Daniel Hothem, Robin Blume-Kohout, Jordan Hines, K. Birgitta Whaley et al.
10 citations
PRX Quantum
Quantum Computing Algorithms and Architecture
3.47
article

Fully Scalable Randomized Benchmarking Without Motion Reversal

Daniel Hothem, Robin Blume-Kohout, Jordan Hines, K. Birgitta Whaley, Timothy Proctor
article en
10 citations

Abstract

We introduce , a protocol that streamlines traditional RB by using circuits consisting almost entirely of independent identically distributed (IID) layers of gates. BiRB reliably and efficiently extracts the average error rate of a Clifford gate set by sending tensor-product eigenstates of random Pauli operators through random circuits with IID layers. Unlike existing RB methods, BiRB does not use motion reversal circuits—i.e., circuits that implement the identity (or a Pauli) operator—which simplifies both the method and the theory proving its reliability. Furthermore, this simplicity enables scaling BiRB to many more qubits than the most widely used RB methods. Published by the American Physical Society 2024

PRX QuantumVol. 5(3)
Sandia National Laboratories California (US), University of California System (US), Sandia National Laboratories (US), University of California, Berkeley (US)
U.S. Department of Energy, Office of the Director of National Intelligence, Office of Science, Intelligence Advanced Research Projects Activity, Advanced Scientific Computing Research, Laboratory Directed Research and Development, Sandia National Laboratories
Openalex Percentile: Top 7%
Quantum Computing Algorithms and Architecture
3.47
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