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.
Matchgate benchmarking: Scalable benchmarking of a continuous family of many-qubit gates
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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.