Quantum Measurement Classification with Qudits

TL;DR

This paper introduces a hybrid quantum-classical approach for density estimation and supervised classification using qudits, demonstrating its viability on high-dimensional quantum computer simulators.

Contribution

It presents a novel quantum protocol for density estimation and classification that can be generalized to high-dimensional quantum systems.

Findings

Effective density estimation and classification demonstrated on various datasets.

Quantum protocols enable probability density function estimation in high-dimensional spaces.

The method is viable for implementation on high-dimensional quantum computers.

Abstract

This paper presents a hybrid classical-quantum program for density estimation and supervised classification. The program is implemented as a quantum circuit in a high-dimensional quantum computer simulator. We show that the proposed quantum protocols allow to estimate probability density functions and to make predictions in a supervised learning manner. This model can be generalized to find expected values of density matrices in high-dimensional quantum computers. Experiments on various data sets are presented. Results show that the proposed method is a viable strategy to implement supervised classification and density estimation in a high-dimensional quantum computer.