Precise measurement of quantum observables with neural-network estimators

TL;DR

This paper introduces a neural-network based approach to improve measurement precision in quantum simulators, significantly reducing the number of samples needed for complex observable estimation without extra quantum resources.

Contribution

It presents a novel hybrid measurement protocol combining neural networks with quantum platforms to efficiently estimate complex quantum observables.

Findings

Neural networks enable high-precision measurements with fewer samples.

The method is effective for quantum chemistry Hamiltonians.

Experimental validation shows substantial reduction in measurement overhead.

Abstract

The measurement precision of modern quantum simulators is intrinsically constrained by the limited set of measurements that can be efficiently implemented on hardware. This fundamental limitation is particularly severe for quantum algorithms where complex quantum observables are to be precisely evaluated. To achieve precise estimates with current methods, prohibitively large amounts of sample statistics are required in experiments. Here, we propose to reduce the measurement overhead by integrating artificial neural networks with quantum simulation platforms. We show that unsupervised learning of single-qubit data allows the trained networks to accommodate measurements of complex observables, otherwise costly using traditional post-processing techniques. The effectiveness of this hybrid measurement protocol is demonstrated for quantum chemistry Hamiltonians using both synthetic and experimental data. Neural-network estimators attain high-precision measurements with a drastic reduction in the amount of sample statistics, without requiring additional quantum resources.