Shaked Regev is a Research Scientist at Oak Ridge National Lab whose research interests include Quantum Error Correction, Numerical Linear Algebra, Mathematical Optimization, GPU Programming, and Super Computing.
He received his PhD in Computational and Mathematical Engineering from Stanford University for his thesis on “Preconditioning Techniques for Sparse Linear Systems”.
Presentation Title:
Closed from logical error rate approximations for surface codes
Presentation Abstract:
We propose a novel method to calculate logical error rates in surface codes, assuming independent and identically distributed physical errors. We show how to use our method to analyze hypothetical quantum computers with various configurations and select designs with lower error rates. Currently, this requires expensive classical simulations of quantum decoders for various distances and physical error rates or inaccurate extrapolation from minimal experimental data. Instead, we use the symmetry of the problem to count the configurations that result in a logical error with our novel software. Given a physical error rate, we can deduce the probability of a logical error, to provably good accuracy. We include an analysis of measurement errors to allow a more complete comparison of different surface code implementations.