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.
Noise tailoring for scalable quantum compu- tation via randomized compiling
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
quant-ph 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.