Disambiguating Pauli noise in quantum computers

TL;DR

This paper demonstrates that, despite fundamental limitations in uniquely identifying quantum noise, self-consistent characterization allows accurate prediction and mitigation of noisy dynamics in quantum computers, validated through large-scale experiments.

Contribution

It introduces a self-consistent framework for characterizing and mitigating quantum noise that overcomes gauge ambiguities, validated on up to 92 qubits.

Findings

Gauge choice does not affect error-mitigated observable values.

Optimizing gauge reduces sampling overhead.

Framework validated on large-scale quantum hardware.

Abstract

To successfully perform quantum computations, it is often necessary to first accurately characterize the noise in the underlying hardware. However, it is well known that fundamental limitations prevent the unique identification of the noise. This raises the question of whether these limitations impact the ability to predict noisy dynamics and mitigate errors. Here, we show, both theoretically and experimentally, that when learnable parameters are self-consistently characterized, the unlearnable (gauge) degrees of freedom do not impact predictions of noisy dynamics or error mitigation. We use the recently introduced framework of gate set Pauli noise learning to efficiently and self-consistently characterize and mitigate noise of a complete gate set, including state preparation, measurements, single-qubit gates and multi-qubit entangling Clifford gates. We validate our approach through experiments with up to 92 qubits and show that while the gauge choice does not affect error-mitigated observable values, optimizing it reduces sampling overhead. Our findings address an outstanding issue involving the ambiguities in characterizing and mitigating quantum noise.