Research Talks
To Learn and Cancel Quantum Noise: PEC with Sparse Pauli-Lindblad Models
A research talk on making probabilistic error cancellation (PEC) work at scale. It explains canceling noise with noise via quasi-probability sampling, why PEC had been limited to one or two qubits, and a three-step approach: twirl the noise to a Pauli channel, learn it per layer with a sparse Pauli-Lindblad model, and mitigate readout errors. It closes with an outlook on the runtime cost of noise-free estimators.
- PEC toy model: unraveling bit-flip noise into quantum trajectories and the sampling overhead
- Why learning full-device noise is hard: exponential parameter counts, crosstalk, correlated errors
- Pauli twirling, layer-wise noise amplification, and the degeneracy (no-go result) that keeps some Pauli fidelities of an entangling layer from being learned individually
- Sparse Pauli-Lindblad model, twirled readout-error mitigation, and a runtime scaling estimate
Summary and key points drafted by a language model from the slides, not yet reviewed by me. The slides are the source.
Cite
@misc{minev2022learncancel,
author = {Zlatko K. Minev},
title = {{To Learn and Cancel Quantum Noise: PEC with Sparse Pauli-Lindblad Models}},
howpublished = {Talk, KITP},
year = {2022},
url = {https://zlatko-minev.github.io/zlatko-minev-quantum-repository/items/2022-learn-and-cancel-quantum-noise-pec-kitp/},
note = {Slides}
}Reuse
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