Enhanced Hamiltonian Learning Precision with Multi-Stage Neural Networks

• URL: http://arxiv.org/abs/2503.07356v1
• Date: Mon, 10 Mar 2025 14:10:59 GMT
• Title: Enhanced Hamiltonian Learning Precision with Multi-Stage Neural Networks
• Authors: Zhengjie Kang, Hao Li, Shuo Wang, Jiaojiao Li, Yuanjie Zhang, Zhihuang Luo,
• Abstract summary: We propose a multi-stage neural network framework that enhances Hamiltonian learning precision.<n>Our approach utilizes time-series data from single-qubit Pauli measurements of random initial states.<n>We demonstrate the framework on two-qubit systems, achieving orders-of-magnitude improvement in parameter accuracy.
• Score: 10.285214278728528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning quantum Hamiltonians with high precision is important for quantum physics and quantum information science. We propose a multi-stage neural network framework that significantly enhances Hamiltonian learning precision through successive network optimization of residual errors. Our approach utilizes time-series data from single-qubit Pauli measurements of random initial states, enabling the estimation of unknown Hamiltonian parameters without prior structural assumptions. We demonstrate the framework on two-qubit systems, achieving orders-of-magnitude improvement in parameter accuracy, and further extend the method to larger systems by integrating dynamical decoupling techniques. Additionally, the protocol exhibits robustness against experimental noise. This work bridges the gap between scalable Hamiltonian learning and high-precision requirements, offering a practical tool for precise quantum control and metrology.

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