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Graph Generation for Quantum States Using Qiskit and Its Application for Quantum Neural Networks

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  • Alexandru-Gabriel Tudorache

    (Department of Computer Science and Engineering, “Gheorghe Asachi” Technical University of Iasi, D. Mangeron Street nr. 27A, 700050 Iasi, Romania)

Abstract

This paper describes a practical approach to the quantum theory using the simulation and processing technology available today. The proposed project allows us to create an exploration graph so that for an initial starting configuration of the qubits, all possible states are created given a set of gates selected by the user. For each node in the graph, we can obtain various types of information such as the applied gates from the initial state (the transition route), necessary cost, representation of the quantum circuit, as well as the amplitudes of each state. The project is designed not as an end goal, but rather as a processing platform that allows users to visualize and explore diverse solutions for different quantum problems in a much easier manner. We then describe some potential applications of this project in other research fields, illustrating the way in which the states from the graph can be used as nodes in a new interpretation of a quantum neural network; the steps of a hybrid processing chain are presented for the problem of finding one or more states that verify certain conditions. These concepts can also be used in academia, with their implementation being possible with the help of the Python programming language, the NumPy library, and Qiskit—the open-source quantum framework developed by IBM.

Suggested Citation

  • Alexandru-Gabriel Tudorache, 2023. "Graph Generation for Quantum States Using Qiskit and Its Application for Quantum Neural Networks," Mathematics, MDPI, vol. 11(6), pages 1-15, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1484-:d:1100809
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    References listed on IDEAS

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    1. Liu, Ling & Song, Tingting & Sun, Zhiwei & Lei, Jiancheng, 2022. "Quantum generative adversarial networks based on Rényi divergences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    2. Ajagekar, Akshay & You, Fengqi, 2021. "Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems," Applied Energy, Elsevier, vol. 303(C).
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