Research Talks
To Learn and Cancel Quantum Noise: PEC with Sparse Pauli-Lindblad Models
Explains PEC from a one-qubit toy model, then the reasons it had been limited to one or two qubits: the full noise had to be known nearly exactly. The talk shows how Pauli twirling plus a sparse Pauli-Lindblad model makes noise learning scalable, how per-layer 'Lindblad tomograms' expose dominant errors such as those removed by dynamical decoupling, and how PEC then recovers expectation values for Trotterized Ising dynamics, along with the sampling-overhead cost.
- PEC basics: quasi-probability inverse of noise, sign-weighted sampling, overhead γ
- Why scaling PEC is hard, and how twirling diagonalizes noise into a Pauli channel
- Sparse Pauli-Lindblad model and learning it by amplifying noise over repeated layers
- Mitigated Ising-model simulations, high-weight observables, error budget and runtime scaling
Summary and key points drafted by a language model from the slides, not yet reviewed by me. The slides are the source.
Cite
@misc{minev2022learncancela,
author = {Zlatko K. Minev},
title = {{To Learn and Cancel Quantum Noise: PEC with Sparse Pauli-Lindblad Models}},
howpublished = {Talk, IBM Quantum Qiskit Seminar},
year = {2022},
url = {https://zlatko-minev.github.io/zlatko-minev-quantum-repository/items/2022-learn-and-cancel-quantum-noise-pec-qiskit-seminar/},
note = {Slides}
}Reuse
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