Machine Learning for Ground State Preparation via Measurement and Feedback

• URL: http://arxiv.org/abs/2502.06517v1
• Date: Mon, 10 Feb 2025 14:37:34 GMT
• Title: Machine Learning for Ground State Preparation via Measurement and Feedback
• Authors: Chuanxin Wang, Yi-Zhuang You,
• Abstract summary: We present a recurrent neural network-based approach for ground state preparation utilizing mid-circuit measurement and feedback. We demonstrate that performance systematically improves as a larger fraction of ancilla qubits are utilized for measurement and feedback.
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• Abstract: We present a recurrent neural network-based approach for ground state preparation utilizing mid-circuit measurement and feedback. Unlike previous methods that use machine learning solely as an optimizer, our approach dynamically adjusts quantum circuits based on real-time measurement outcomes and learns distinct preparation protocols for different Hamiltonians. Notably, our machine learning algorithm consistently identifies a state preparation strategy wherein all initial states are first steered toward an intermediate state before transitioning to the target ground state. We demonstrate that performance systematically improves as a larger fraction of ancilla qubits are utilized for measurement and feedback, highlighting the efficacy of mid-circuit measurements in state preparation tasks.

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