Entaglement-Based Quantum Mean Estimator Circuit

TL;DR

This paper introduces a quantum circuit leveraging entanglement and superposition to efficiently estimate the mean of quantum states, potentially improving machine learning algorithms.

Contribution

It presents a novel quantum circuit design for mean estimation that utilizes quantum RAM and entanglement, achieving logarithmic complexity in certain parameters.

Findings

Simulated on IBM Q Experience shows promising results.

Potential to enhance mean-based machine learning algorithms.

Achieves $ ext{O}(rac{1}{ extepsilon} extlog Nd)$ complexity.

Abstract

This paper proposes a quantum circuit for computing the mean value from a given set of quantum states. The circuit consults a Quantum Random Access Memory to get the values of the set, and by using superposition, interference and entanglement phenomena, it can estimate the mean value in $\mathcal{O}(\frac{1}{\epsilon}\log{Nd})$ complexity. The proposed quantum mean-estimator circuit has been simulated on the IBM Q Experience and the results suggest that the proposed quantum circuit can have the potential to enhance many mean-based machine learning algorithms.