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Journal Article

Self-learning Machines based on Hamiltonian Echo Backpropagation

MPS-Authors

Lopez-Pastor,  Victor
Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society;

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Marquardt,  Florian
Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society;

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PhysRevX.13.031020.pdf
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Citation

Lopez-Pastor, V., & Marquardt, F. (2023). Self-learning Machines based on Hamiltonian Echo Backpropagation. Physical Review X, 13(3): 031020. doi:10.1103/PhysRevX.13.031020.


Cite as: https://hdl.handle.net/21.11116/0000-0008-2468-B
Abstract
A physical self-learning machine can be defined as a nonlinear dynamical system that can be trained on data (similar to artificial neural networks), but where the update of the internal degrees of freedom that serve as learnable parameters happens autonomously. In this way, neither external processing and feedback nor knowledge of (and control of) these internal degrees of freedom is required. We introduce a general scheme for self-learning in any time-reversible Hamiltonian system. We illustrate the training of such a self-learning machine numerically for the case of coupled nonlinear wave fields.