Quantum computational advantage in random-circuit sampling on IBM superconducting quantum computers
Abstract
We report forward random-circuit sampling (RCS) on the 120-qubit Nighthawk r2 superconducting processor (ibm_phoenix) with square-lattice connectivity, using 61 qubits, native CZ gates, and the standard cloud execution stack with no benchmark-specific calibration. Two independent fidelity estimators---mirror benchmarking and three- and four-patch cross-entropy benchmarking (XEB)---agree with each other at every measured depth, the mirror from 4 to 40 cycles and the patched estimators from 20 to 40 cycles, across more than two orders of magnitude of fidelity decay, and exceed the first-generation Nighthawk r1 device by more than an order of magnitude at fixed depth. The 36-cycle circuits sit at the depth where tensor-network contraction cost saturates at system size: a contraction-cost estimator validated against the published Sycamore and Zuchongzhi networks places the single-amplitude cost at ∼1022 complex operations. At FXEB(36)=2.3×10−3 under favorable memory assumptions this implies 1.2×1027 machine operations within the bounded-fidelity rejection-sampling model --- more than a century of runtime on the Frontier supercomputer --- to collect a 106-sample ensemble, which takes only 19\,s on Nighthawk r2. To our knowledge, this is the first demonstration of quantum advantage for a vanilla random-circuit sampling on a commercially and broadly accessible quantum processor that most non-expert quantum computer users can easily replicate.
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BibTeX
@misc{sedrakyan2026quantum,
title = {{Quantum computational advantage in random-circuit sampling on IBM superconducting quantum computers}},
author = {Tigran Sedrakyan and Yuxuan Zhang and Hovnatan Karapetyan and Joshua D. Baktay and Hrant Gharibyan and Hayk Tepanyan},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.28657},
eprint = {2609.28657},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.28657},
}
RIS
TY - UNPB
TI - Quantum computational advantage in random-circuit sampling on IBM superconducting quantum computers
AU - Tigran Sedrakyan
AU - Yuxuan Zhang
AU - Hovnatan Karapetyan
AU - Joshua D. Baktay
AU - Hrant Gharibyan
AU - Hayk Tepanyan
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2609.28657
UR - https://arxiv.org/abs/2609.28657
AB - We report forward random-circuit sampling (RCS) on the 120-qubit Nighthawk r2 superconducting processor (ibm_phoenix) with square-lattice connectivity, using 61 qubits, native CZ gates, and the standard cloud execution stack with no benchmark-specific calibration. Two independent fidelity estimators---mirror benchmarking and three- and four-patch cross-entropy benchmarking (XEB)---agree with each other at every measured depth, the mirror from 4 to 40 cycles and the patched estimators from 20 to 40 cycles, across more than two orders of magnitude of fidelity decay, and exceed the first-generation Nighthawk r1 device by more than an order of magnitude at fixed depth. The 36-cycle circuits sit at the depth where tensor-network contraction cost saturates at system size: a contraction-cost estimator validated against the published Sycamore and Zuchongzhi networks places the single-amplitude cost at ∼10^22 complex operations. At F_XEB(36)=2.3×10^−3 under favorable memory assumptions this implies 1.2×10^27 machine operations within the bounded-fidelity rejection-sampling model --- more than a century of runtime on the Frontier supercomputer --- to collect a 10^6-sample ensemble, which takes only 19\,s on Nighthawk r2. To our knowledge, this is the first demonstration of quantum advantage for a vanilla random-circuit sampling on a commercially and broadly accessible quantum processor that most non-expert quantum computer users can easily replicate.
ER -