Quantum Measurement Classification with Qudits
This work addresses quantum machine learning for researchers in quantum computing, but it is incremental as it applies existing methods to new quantum simulation contexts.
The paper tackles the problem of density estimation and supervised classification by proposing a hybrid classical-quantum program implemented in a high-dimensional quantum computer simulator, showing it is a viable strategy for these tasks.
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.