Scalable Mitigation of Measurement Errors on Quantum Computers
Paul D. Nation, Hwajung Kang, Neereja Sundaresan, Jay Gambetta · PRX Quantum · 2021
We present a method for mitigating measurement errors on quantum computing platforms that does not form the full assignment matrix, or its inverse, and works in a subspace defined by the noisy input bit strings.This method accommodates both uncorrelated and correlated errors and allows for the computation of accurate error bounds.Additionally, we detail a matrix-free preconditioned iterative-solution method that converges in O(1) steps that is performant and uses orders of magnitude less memory than direct factorization.We demonstrate the validity of our method and mitigate errors in a few seconds on numbers of qubits that would otherwise be impractical.