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With the availability of noisy-intermediate scale quantum (NISQ) computers, the field of digital quantum simulation of physics-based systems has garnered significant attention due to the usage of quantum AI algorithms, which directly resemble classical AI techniques. Certain research problems in condensed matter physics and material sciences are well suited to be solved using quantum AI algorithms, such as describing magnetism in quantum systems. The Heisenberg model based on lattice structures is a model used to understand magnetic phenomena in materials, such as interactions, quantum magnetic effects, and numerical studies. In this paper, we show one of the first kinds of experiments for the computation of ground state properties of 1D and 2D Heisenberg lattice models compared to the true value of ground state with Qiskit Nature. We employ the Variational Quantum Eigensolver (VQE) that is inspired by the quantum AI technique of variational quantum algorithm (VQA) along with gradient and non-gradient optimizers to obtain results that closely converge towards the true values, showcasing the potential of NISQ algorithms for simulating quantum-mechanical systems.

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Quantum Simulation of 1D and 2D Lattice-Based Magnetic Quantum Systems Using Heisenberg Model via Variational Quantum Eigensolver

  • 会议论文

  • 发表日期：

    2025年

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  • Shalini Devendrababu,
  • Srinjoy Ganguly,
  • Prateek Jain,
  • Hasan Mustafa,
  • Luis Gerardo Ayala Bertel

摘要

With the availability of noisy-intermediate scale quantum (NISQ) computers, the field of digital quantum simulation of physics-based systems has garnered significant attention due to the usage of quantum AI algorithms, which directly resemble classical AI techniques. Certain research problems in condensed matter physics and material sciences are well suited to be solved using quantum AI algorithms, such as describing magnetism in quantum systems. The Heisenberg model based on lattice structures is a model used to understand magnetic phenomena in materials, such as interactions, quantum magnetic effects, and numerical studies. In this paper, we show one of the first kinds of experiments for the computation of ground state properties of 1D and 2D Heisenberg lattice models compared to the true value of ground state with Qiskit Nature. We employ the Variational Quantum Eigensolver (VQE) that is inspired by the quantum AI technique of variational quantum algorithm (VQA) along with gradient and non-gradient optimizers to obtain results that closely converge towards the true values, showcasing the potential of NISQ algorithms for simulating quantum-mechanical systems.