A differentiable tensor-network framework learns CPTP noise channels from single-circuit measurement data on IBM hardware and generalizes the model to unrelated circuits.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 3roles
background 1polarities
background 1representative citing papers
QESEM is a characterization-based error mitigation technique that achieves unbiased estimates with substantially reduced runtime cost compared to probabilistic error cancellation while outperforming zero-noise extrapolation on utility-scale circuits.
Depolarizing noise doubles the number of non-analytic points in the Loschmidt echo at dynamical phase transition times in the transverse-field Ising model, inducing an inherent error that zero-noise extrapolation cannot mitigate.
citing papers explorer
-
Quantum hardware noise learning via differentiable Kraus representation on tensor networks
A differentiable tensor-network framework learns CPTP noise channels from single-circuit measurement data on IBM hardware and generalizes the model to unrelated circuits.
-
Reliable high-accuracy error mitigation for utility-scale quantum circuits
QESEM is a characterization-based error mitigation technique that achieves unbiased estimates with substantially reduced runtime cost compared to probabilistic error cancellation while outperforming zero-noise extrapolation on utility-scale circuits.
-
Quantum simulation of dynamical phase transitions in noisy quantum devices
Depolarizing noise doubles the number of non-analytic points in the Loschmidt echo at dynamical phase transition times in the transverse-field Ising model, inducing an inherent error that zero-noise extrapolation cannot mitigate.