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.