Quantum simulation of the Hubbard model on a graphene hexagon: Strengths of IQPE and noise constraints

• URL: http://arxiv.org/abs/2506.05031v1
• Date: Thu, 05 Jun 2025 13:40:08 GMT
• Title: Quantum simulation of the Hubbard model on a graphene hexagon: Strengths of IQPE and noise constraints
• Authors: Mohammad Mirzakhani, Kyungsun Moon,
• Abstract summary: We simulate the Hubbard model on a six-site graphene hexagon using Qiskit.<n>We employ IQPE and adiabatic evolution algorithms to determine its ground-state properties.<n>To examine these limitations, we utilize the Qiskit Aer simulator with a custom noise model tailored to the characteristics of IBM's real backend.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum computing offers transformative potential for simulating real-world materials, providing a powerful platform to investigate complex quantum systems across quantum chemistry and condensed matter physics. In this work, we leverage this capability to simulate the Hubbard model on a six-site graphene hexagon using Qiskit, employing the Iterative Quantum Phase Estimation (IQPE) and adiabatic evolution algorithms to determine its ground-state properties. Noiseless simulations yield accurate ground-state energies (GSEs), charge and spin densities, and correlation functions, all in excellent agreement with exact diagonalization, underscoring the precision and reliability of quantum simulation for strongly correlated electron systems. However, deploying IQPE and adiabatic evolution on today's noisy quantum hardware remains highly challenging. To examine these limitations, we utilize the Qiskit Aer simulator with a custom noise model tailored to the characteristics of IBM's real backend. This model includes realistic depolarizing gate errors, thermal relaxation, and readout noise, allowing us to explore how these factors degrade simulation accuracy. Preliminary hardware runs on IBM devices further expose discrepancies between simulated and real-world noise, emphasizing the gap between ideal and practical implementations. Overall, our results highlight the promise of quantum computing for simulating correlated quantum materials, while also revealing the significant challenges posed by hardware noise in achieving accurate and reliable physical predictions using current quantum devices.

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