Experimental semi-autonomous eigensolver using reinforcement learning

TL;DR

This paper presents a semi-autonomous eigensolver algorithm using reinforcement learning principles on IBM quantum computers, achieving high fidelity eigenvector approximations with low measurement resources.

Contribution

It introduces a novel reinforcement learning-inspired semi-autonomous eigensolver that reduces measurement resources for quantum eigenvector estimation.

Findings

Achieves over 0.97 fidelity for single-qubit eigenvectors

Obtains over 0.91 fidelity for two-qubit eigenvectors

Uses fewer measurements than traditional methods, suitable for current quantum devices

Abstract

The characterization of observables, expressed via Hermitian operators, is a crucial task in quantum mechanics. For this reason, an eigensolver is a fundamental algorithm for any quantum technology. In this work, we implement a semi-autonomous algorithm to obtain an approximation of the eigenvectors of an arbitrary Hermitian operator using the IBM quantum computer. To this end, we only use single-shot measurements and pseudo-random changes handled by a feedback loop, reducing the number of measures in the system. Due to the classical feedback loop, this algorithm can be cast into the reinforcement learning paradigm. Using this algorithm, for a single-qubit observable, we obtain both eigenvectors with fidelities over 0.97 with around 200 single-shot measurements. For two-qubits observables, we get fidelities over 0.91 with around 1500 single-shot measurements for the four eigenvectors, which is a comparatively low resource demand, suitable for current devices. This work is useful to the development of quantum devices able to decide with partial information, which helps to implement future technologies in quantum artificial intelligence.