Unfolding Quantum Computer Readout Noise

TL;DR

This paper explores unfolding techniques, especially iterative Bayesian unfolding, to correct readout noise in NISQ quantum computers, bridging quantum information science and high energy physics for more accurate quantum results.

Contribution

It introduces the application of unfolding methods, particularly iterative Bayesian unfolding, to mitigate readout errors in quantum computing, offering a novel approach inspired by high energy physics techniques.

Findings

Iterative Bayesian unfolding avoids common matrix inversion pathologies.

Unfolding methods improve accuracy of quantum readout error correction.

Bridges quantum computing and high energy physics methodologies.

Abstract

In the current era of noisy intermediate-scale quantum (NISQ) computers, noisy qubits can result in biased results for early quantum algorithm applications. This is a significant challenge for interpreting results from quantum computer simulations for quantum chemistry, nuclear physics, high energy physics, and other emerging scientific applications. An important class of qubit errors are readout errors. The most basic method to correct readout errors is matrix inversion, using a response matrix built from simple operations to probe the rate of transitions from known initial quantum states to readout outcomes. One challenge with inverting matrices with large off-diagonal components is that the results are sensitive to statistical fluctuations. This challenge is familiar to high energy physics, where prior-independent regularized matrix inversion techniques (`unfolding') have been developed for years to correct for acceptance and detector effects when performing differential cross section measurements. We study various unfolding methods in the context of universal gate-based quantum computers with the goal of connecting the fields of quantum information science and high energy physics and providing a reference for future work. The method known as iterative Bayesian unfolding is shown to avoid pathologies from commonly used matrix inversion and least squares methods.