Scalable Mitigation of Measurement Errors on Quantum Computers

Abstract

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.