Direct Fidelity Estimation of Quantum States using Machine Learning

TL;DR

This paper introduces a machine learning method for estimating quantum state fidelity that requires fewer measurements and is applicable to any quantum state, significantly simplifying the verification process in quantum computing.

Contribution

The paper presents a novel machine learning approach for quantum fidelity estimation that reduces measurement complexity and is universally applicable to arbitrary quantum states.

Findings

Requires only four measurement settings for five-qubit states

Achieves ±1% precision in fidelity estimation

Applicable to arbitrary quantum states without increasing measurement settings

Abstract

In almost all quantum applications, one of the key steps is to verify that the fidelity of the prepared quantum state meets expectations. In this Letter, we propose a new approach solving this problem using machine-learning techniques. Compared to other fidelity estimation methods, our method is applicable to arbitrary quantum states, the number of required measurement settings is small, and this number does not increase with the size of the system. For example, for a general five-qubit quantum state, only four measurement settings are required to predict its fidelity with $\pm1\%$ precision in a nonadversarial scenario. This machine-learning-based approach for estimating quantum state fidelity has the potential to be widely used in the field of quantum information.