A proposed machine-learning update to quantum readout error matrices claims 6.6-29.9% metric improvements in simulation, but its least-squares core is degenerate because each qubit's probability vector sums to one.
Mitigation of correlated readout errors without randomized measurements
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abstract
Quantum simulation, the study of strongly correlated quantum matter using synthetic quantum systems, has been the most successful application of quantum computers to date. It often requires determining observables with high precision, for example when studying critical phenomena near quantum phase transitions. Thus, readout errors must be carefully characterized and mitigated in data postprocessing, using scalable and noise-model agnostic protocols. We present a readout error-mitigation protocol that uses only single-qubit Pauli measurements and avoids experimentally challenging randomized measurements. The proposed approach captures a very broad class of correlated noise models and is scalable to large qubit systems. It is based on a complete and efficient characterization of few-qubit correlated positive operator-valued measures, using overlapping detector tomography. To assess the effectiveness of the protocol, observables are extracted from simulations involving up to 100 qubits employing readout errors obtained from experiments with superconducting qubits.
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quant-ph 1years
2025 1verdicts
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Personalized Improvement of Standard Readout Error Mitigation using Low-Depth Circuits and Machine Learning
A proposed machine-learning update to quantum readout error matrices claims 6.6-29.9% metric improvements in simulation, but its least-squares core is degenerate because each qubit's probability vector sums to one.