FACES is a new protocol for simultaneous self-consistent learning of averaged error rates across many FLO gates with rigorously shown efficient sampling complexity via Kravchuk transformations.
Error mitigation by training with fermionic linear optics,
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Presents unbiased uncertainty quantification for post-processing error mitigation and applies it to optimize hyperparameters in Zero Noise Extrapolation and Clifford Data Regression under finite-shot noise.
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Fermionic Averaged Circuit Eigenvalue Sampling
FACES is a new protocol for simultaneous self-consistent learning of averaged error rates across many FLO gates with rigorously shown efficient sampling complexity via Kravchuk transformations.
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Robust design under uncertainty in quantum error mitigation
Presents unbiased uncertainty quantification for post-processing error mitigation and applies it to optimize hyperparameters in Zero Noise Extrapolation and Clifford Data Regression under finite-shot noise.
- Enabling Lie-Algebraic Classical Simulation beyond Free Fermions