Scalable mitigation of measurement errors on quantum computers

TL;DR

This paper introduces a scalable, matrix-free method for mitigating measurement errors in quantum computers, effectively handling both correlated and uncorrelated errors with minimal memory and computational resources.

Contribution

The authors propose a novel matrix-free iterative approach that mitigates measurement errors without requiring the full assignment matrix, enabling efficient error correction on larger quantum systems.

Findings

Effective error mitigation in seconds for large qubit systems

Handles both correlated and uncorrelated measurement errors

Uses significantly less memory than traditional methods

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 computing accurate error bounds. Additionally, we detail a matrix-free preconditioned iterative solution method that converges in $\mathcal{O}(1)$ 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 intractable.