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This study introduces a Physics-Informed Neural Quantum Control (PINQC) framework that leverages differentiable quantum propagation to optimize laser pulses for molecular photoassociation without external training data. By successfully applying this method to extended rovibrational models with higher dimensional complexity, the work demonstrates how neural networks can directly interface with fundamental quantum dynamics to solve previously intractable control problems. The approach establishes a robust computational pathway for manipulating coherent quantum states in increasingly complex molecular systems.
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Physics-Informed Neural Quantum Control for Rovibrational Photoassociation in a Morse Molecular System

arXiv2 min read

arXiv:2606.29610v4 Announce Type: replace Abstract: We present a Physics-Informed Neural Quantum Control (PINQC) framework for rovibrational photoassociation in a Morse molecular system. The proposed method combines neural-network-based laser-field generation with differentiable quantum propagation, allowing optimized laser pulses to be obtained directly from the underlying quantum dynamics without requiring external training data. The optimized control fields efficiently transfer an initially continuum-like Gaussian wave packet into the vibrational ground-state level, promoting continuum-to-bound population transfer through coherent rovibrational dynamics. The resulting photoassociation process involves both vibrational stabilization and rotational redistribution arising naturally from dipole-induced couplings between neighboring rotational channels. A central result of the present work is the successful application of the PINQC framework to extended rovibrational models containing larger rotational levels than those previously accessible in our conventional photoassociation calculations. The optimization remains numerically stable despite the increased complexity of the molecular system, demonstrating that differentiable optimization provides an effective strategy for treating rovibrational models of increased dimensionality. These results establish the PINQC framework as a promising computational tool for molecular photoassociation and motivate future investigations of increasingly complex rovibrational quantum-control problems.

Abstract:We present a Physics-Informed Neural Quantum Control (PINQC) framework for rovibrational photoassociation in a Morse molecular system. The proposed method combines neural-network-based laser-field generation with differentiable quantum propagation, allowing optimized laser pulses to be obtained directly from the underlying quantum dynamics without requiring external training data. The optimized control fields efficiently transfer an initially continuum-like Gaussian wave packet into the vibrational ground-state level, promoting continuum-to-bound population transfer through coherent rovibrational dynamics. The resulting photoassociation process involves both vibrational stabilization and rotational redistribution arising naturally from dipole-induced couplings between neighboring rotational channels. A central result of the present work is the successful application of the PINQC framework to extended rovibrational models containing larger rotational levels than those previously accessible in our conventional photoassociation calculations. The optimization remains numerically stable despite the increased complexity of the molecular system, demonstrating that differentiable optimization provides an effective strategy for treating rovibrational models of increased dimensionality. These results establish the PINQC framework as a promising computational tool for molecular photoassociation and motivate future investigations of increasingly complex rovibrational quantum-control problems.

Quantum Physics (quant-ph); Atomic Physics (physics.atom-ph)

arXiv:2606.29610 [quant-ph]

(or arXiv:2606.29610v4 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2606.29610 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 270 words

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