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arXiv · 2502.06517

Machine Learning for Ground State Preparation via Measurement and Feedback

Chuanxin Wang·Yi-Zhuang You
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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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Chuanxin Wang, Yi-Zhuang You. 2025-02-10. Machine Learning for Ground State Preparation via Measurement and Feedback. https://arxiv.org/abs/2502.06517

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