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This paper proposes a quantum shortcuts to adiabaticity scheme based on physics-informed neural networks. Compared with traditional shortcuts to adiabaticity techniques, our approach innovatively integrates machine learning methodologies by employing parameterized physics-informed neural networks to solve parameterized differential equations. The neural networks serves as an approximating function for quantum adiabatic evolution processes, while incorporating parameter-dependent differential equations and various physical constraints as components of the loss function. Through networks training, we effectively simulate quantum system dynamics and derive driving control fields for population inversion. Numerical simulations demonstrate that the quantum system can achieve rapid population inversion within significantly reduced time while maintaining high fidelity and exceptional robustness against parameter fluctuations. The neural networks exhibit remarkable computational capabilities, particularly suitable for generating control functions in complex quantum systems. Compared with conventional counter-diabatic driving and transitionless quantum driving methods, this PINN-based framework not only achieves better control performance but also offers improved practicality for experimental implementations. The success of this methodology suggests promising applications in quantum control tasks including but not limited to quantum state preparation, quantum gate optimization, and adiabatic quantum computing acceleration.
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Keywords:
- shortcuts to adiabaticity /
- deep learning /
- physics-Informed neural networks /
- differential equation
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