STUDY OF A QUANTUM COMPUTING SYSTEM AND IMPLEMENTATION OF A QUANTUM CORE ON FPGA

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 of programmable gate matrices is demonstrated. The relevance of these studies lies in the mathematical and software modeling and implementation of a quantum computing system as part of the development of the implementation of a quantum core on FPGA for solving classes of problems of a classical nature. The scientific novelty of this research area is the development of a hybrid simulator of the quantum cores of a central processing unit (CPU) and a programmable logic integrated circuit (FPGA) several orders of magnitude faster than a conventional quantum computing simulator. 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 input features, up to 780-dimensional features using 4000 samples. We applied the quantum kernel to image classification problems using the Fashion-MNIST dataset and showed that the quantum kernel is comparable to Gaussian kernels with optimized throughput. The analysis of the work in this field has shown that a new qualitative level has now been reached, opening up promising opportunities for the implementation of multi-qubit quantum computing. The prospects for implementation and development are connected not only with technological capabilities, but also with solving the issues of building effective quantum systems for solving actual mathematical problems, cryptography problems and control (optimization) problems.