Differentiable MLE Framework for Quantum Error Correction Noise Estimation

dMLE framework uses tensor networks & statistical mechanics to optimize QEC noise parameters via gradient descent, cutting logical error rates up to 30.6% for repetition codes & 8.1% for surface codes vs. prior correlation/RL methods.

#QuantumErrorCorrection #FaultTolerantQuantumComputing #Research #Informaq

https://iq.fp2.dev/gi1Gn