Research Talks
Machine Learning for Practical Quantum Error Mitigation (ML-QEM)
Introduces the trade-offs among error mitigation methods (PEC is unbiased but costly, ZNE is cheaper but biased) and proposes front-loading the cost into training a model that mitigates expectation values at runtime without extra circuits. Compares linear regression, random forest, multi-layer perceptron and graph neural network models on random and Trotterized Ising circuits in noisy simulation and on hardware, including mimicking ZNE on a 100-qubit experiment.
- Three-step ML-QEM workflow: generate training data, train, mitigate at runtime
- Comparison with Clifford data regression and why linear models are not enough in general
- Four model families compared; random forest consistently performs best
- Benchmarks on random 4-qubit circuits and Trotterized 1D transverse-field Ising circuits versus digital ZNE
Summary and key points drafted by a language model from the slides, not yet reviewed by me. The slides are the source.
Cite
@misc{liao2023machinelearning,
author = {Haoran Liao},
title = {{Machine Learning for Practical Quantum Error Mitigation (ML-QEM)}},
howpublished = {Talk, Qiskit Seminar},
year = {2023},
url = {https://zlatko-minev.github.io/zlatko-minev-quantum-repository/items/2023-machine-learning-for-practical-quantum-error/},
note = {Slides}
}Reuse
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