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Characterizing large-scale quantum computers via cycle benchmarking
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Quantum computers promise to solve certain problems more efficiently than their digital counterparts. A major challenge towards practically useful quantum computing is characterizing and reducing the various errors that accumulate during an algorithm running on large-scale processors. Current characterization techniques are unable to adequately account for the exponentially large set of potential errors, including cross-talk and other correlated noise sources. Here we develop cycle benchmarking, a rigorous and practically scalable protocol for characterizing local and global errors across multi-qubit quantum processors. We experimentally demonstrate its practicality by quantifying such errors in non-entangling and entangling operations on an ion-trap quantum computer with up to 10 qubits, with total process fidelities for multi-qubit entangling gates ranging from 99.6(1)% for 2 qubits to 86(2)% for 10 qubits. Furthermore, cycle benchmarking data validates that the error rate per single-qubit gate and per two-qubit coupling does not increase with increasing system size.
Forward citations
Cited by 4 Pith papers
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Three direct measurements of DC and AC flux crosstalk between tunable transmons are presented, along with a derived relation between AC crosstalk and parametric CZ gate infidelity.
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Clifford Volume and Free Fermion Volume: Complementary Scalable Benchmarks for Quantum Computers
Two new classically verifiable benchmark scores, Clifford Volume and Free Fermion Volume, are defined, simulated under noise, and Clifford Volume is measured on the Quantinuum H2-1 device as 34 qubits.
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Cross-Platform Verification of Intermediate Scale Quantum Devices
A randomized-measurement protocol estimates the overlap of two quantum states prepared on separate platforms, with a 10-qubit trapped-ion proof of principle.
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