Quantum measurement classification with qudits

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.

Automated summary

This paper presents a hybrid classical-quantum program for density estimation and supervised classification in a high-dimensional quantum computer. The proposed quantum protocols allow for estimating probability density functions and making predictions using supervised learning. The model can be generalized to find expected values of density matrices in high-dimensional quantum computers. Experimental results demonstrate that this method is a feasible strategy for implementing supervised classification and density estimation in a high-dimensional quantum computer.