New immanant and character-based filters reduce computational cost in bosonic randomized benchmarking while providing simple variance expressions and constant low variance for the character filter.
Benchmarking bosonic and fermionic dynamics
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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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Kostant relation in filtered randomized benchmarking for passive bosonic devices
New immanant and character-based filters reduce computational cost in bosonic randomized benchmarking while providing simple variance expressions and constant low variance for the character filter.
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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.
- Randomized Benchmarking with Synthetic Quantum Circuits