Quantum Measurement Classification with Qudits
arXiv:2107.09781 · doi:10.1007/s11128-021-03363-y
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.
15 pages, 10 figures
References in corpus (4)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- High-dimensional quantum communication: benefits, progress, and future challenges
- Experimental access to higher-dimensional entangled quantum systems using integrated optics
- Learning with Density Matrices and Random Features