Noise-robust exploration of many-body quantum states on near-term quantum devices

TL;DR

This paper introduces a resource-efficient method for studying many-body quantum states on noisy quantum devices, enabling local observable estimation with reduced circuit size and qubits, and demonstrating noise mitigation effects.

Contribution

The authors propose a sequential generation model that bounds correlations and allows local measurements without full state preparation, applicable to near-term quantum algorithms.

Findings

Local observables can be estimated from smaller state patches.

Noise effects decrease along the computation process.

Method applies broadly to tensor network states and variational algorithms.

Abstract

We describe a resource-efficient approach to studying many-body quantum states on noisy, intermediate-scale quantum devices. We employ a sequential generation model that allows us to bound the range of correlations in the resulting many-body quantum states. From this, we characterize situations where the estimation of local observables does not require the preparation of the entire state. Instead smaller patches of the state can be generated from which the observables can be estimated. This can potentially reduce circuit size and number of qubits for the computation of physical properties of the states. Moreover, we show that the effect of noise decreases along the computation. Our results apply to a broad class of widely studied tensor network states and can be directly applied to near-term implementations of variational quantum algorithms.