Quasiprobability decompositions with reduced sampling overhead

TL;DR

This paper introduces an optimization-based algorithm for quasiprobability decompositions that significantly reduces sampling overhead in quantum error mitigation, enabling more efficient noise correction on current quantum hardware.

Contribution

It presents a noise-aware quasiprobability decomposition method using semidefinite programming to lower sampling overhead compared to existing techniques.

Findings

Reduced sampling overhead in quasiprobability error mitigation

Tradeoff between approximation error and sampling cost achieved

Robust quasiprobability method developed for practical noise mitigation

Abstract

Quantum error mitigation techniques can reduce noise on current quantum hardware without the need for fault-tolerant quantum error correction. For instance, the quasiprobability method simulates a noise-free quantum computer using a noisy one, with the caveat of only producing the correct expected values of observables. The cost of this error mitigation technique manifests as a sampling overhead which scales exponentially in the number of corrected gates. In this work, we present a new algorithm based on mathematical optimization that aims to choose the quasiprobability decomposition in a noise-aware manner. This directly leads to a significantly lower basis of the sampling overhead compared to existing approaches. A key element of the novel algorithm is a robust quasiprobability method that allows for a tradeoff between an approximation error and the sampling overhead via semidefinite programming.