Integrated Circuits for Quantum Machine Learning Based on Superconducting Artificial Atoms and Methods of Their Control

Abstract

This paper is devoted to the use of quantum integrated circuits based on superconducting artificial atoms to solve quantum machine learning problems. The process of designing such chips is de- scribed in detail, including the selection of the most important geometric parameters of the device, as well as numerical calculations of electromagnetic characteristics. The process of controlling a quantum integrated circuit is described. Much attention is paid to the implementation of single- and two-qubit operations. The qubit state readout procedure is also described. A brief introduction into the field of quantum machine learning is given. An algorithm that makes it possible to solve multilabel classification problems using quantum integrated circuits is described. The selection of optimal quantum circuits for the implementation of this algorithm is made using numerical simulations. The operation of the algorithm is demonstrated by the example of standard datasets. Obtained experimental results are compared with the results of theoretical calculations.