ИССЛЕДОВАНИЕ КВАНТОВОЙ ВЫЧИСЛИТЕЛЬНОЙ СИСТЕМЫ И РЕАЛИЗАЦИЯ КВАНТОВОГО ЯДРА НА ПЛИС

Abstract

The quantum core method is one of the most important methods in quantum machine learning.However, the number of features used for quantum nuclei is limited to a few dozen features.The block product state structure is used as a quantum feature map and the implementation ofprogrammable gate matrices is demonstrated. The relevance of these studies lies in the mathematicaland software modeling and implementation of a quantum computing system as part of thedevelopment of the implementation of a quantum core on FPGA for solving classes of problems ofa classical nature. The scientific novelty of this research area is the development of a hybrid simulatorof the quantum cores of a central processing unit (CPU) and a programmable logic integratedcircuit (FPGA) several orders of magnitude faster than a conventional quantum computingsimulator. This joint development of the implemented quantum core and its efficient FPGA implementation allowed numerical simulation of the quantum core based on gates in terms of inputfeatures, up to 780-dimensional features using 4000 samples. We applied the quantum kernel toimage classification problems using the Fashion-MNIST dataset and showed that the quantumkernel is comparable to Gaussian kernels with optimized throughput. The analysis of the work inthis field has shown that a new qualitative level has now been reached, opening up promising opportunitiesfor the implementation of multi-qubit quantum computing. The prospects for implementationand development are connected not only with technological capabilities, but also with solvingthe issues of building effective quantum systems for solving actual mathematical problems,cryptography problems and control (optimization) problems.