Unbiased observable estimation with approximate channels in fault-tolerant quantum computation

TL;DR

This paper introduces a resource-efficient method for unbiasedly estimating observables in fault-tolerant quantum computation by decomposing ideal channels into probabilistic mixtures of noisy channels, validated through numerical simulations.

Contribution

It presents a novel approach to correct bias in observable estimates without extra resource overhead by using probabilistic channel decomposition.

Findings

Unbiased observable estimation demonstrated with unitary errors.

Method effective in noisy near-term quantum devices.

Validated through simulations of Ising Hamiltonian dynamics.

Abstract

Unitary errors, such as those arising from fault-tolerant compilation of quantum algorithms, systematically bias observable estimates. Correcting this bias typically requires additional resources, such as an increased number of non-Clifford gates. In this work, we present an alternative method for correcting bias in the expectation values of observables. The method leverages a decomposition of the ideal quantum channel into a probabilistic mixture of noisy quantum channels. Using this decomposition, we construct unbiased estimators as weighted sums of expectation values obtained from the noisy channels. We provide a detailed analysis of the method, identify the conditions under which it is effective, and validate its performance through numerical simulations. In particular, we demonstrate unbiased observable estimation in the presence of unitary errors by simulating the time dynamics of the Ising Hamiltonian. Our strategy offers a resource-efficient way to reduce the impact of unitary errors, improving methods for estimating observables in noisy near-term quantum devices and fault-tolerant implementation of quantum algorithms.