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has been cited by the following article:

• TITLE: Leveraging Quantum Computing for the Ising Model to Simulate Two Real Systems: Magnetic Materials and Biological Neural Networks (BNNs)

  AUTHORS: David L. Cao, Khoi Dinh

  KEYWORDS: Ising Model, Magnetic Material, Biological Neural Network, Quantum Computting, International Business Machines (IBM)

  JOURNAL NAME: Journal of Quantum Information Science, Vol.13 No.3, September 11, 2023

  ABSTRACT: Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.