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20232026
most citedMachine Learning for Practical Quantum Error Mitigation

63 citations · 197 across the 17 of their papers we have counts for

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5 papers · 1 filter

quant-ph2026

Spacetime mitigation of logical errors

Laurin E. Fischer, Ali Javadi-Abhari, Simon Martiel +1

Error detection can suppress noise at a lower sampling cost than probabilistic error cancellation (PEC), but leaves residual logical errors. We combine the two approaches to mitiga…

quant-ph2026

Symmetries of Pauli Noise from Lindbladian Dynamics

Moein Malekakhlagh, Edward H. Chen, Luke C. G. Govia +1

Characterizing noise in quantum circuits is fundamentally limited by gauge degrees of freedom; certain parameters, such as the individual contributions of state preparation and mea…

quant-ph2026

Sampling hard circuits with verifiably high fidelity

Simon Martiel, Jay-U Chung, Alireza Seif +9

Sampling-based proposals are prominent candidates for demonstrating quantum computations beyond the reach of classical supercomputers. However, it has been difficult to combine the…

quant-ph2026

Better Pauli Channel Learning with Maximum Likelihood Estimation

Daniel Belkin, Faisal Alam, Matthew Thibodeau +3

Error mitigation in a noisy quantum device requires a very good estimate of the noise channel. The accuracy of probabilistic error cancellation is often limited by the high sample…

quant-ph2026

Learning Midcircuit Measurement Backaction from Three Repeated Measurements

Chia-Tung Chu, Su-un Lee, Han Zheng +4

Accurate modeling of midcircuit measurements (MCMs) is essential for dynamic-circuit operations such as syndrome extraction, measurement-based reset, and the separation of state-pr…