A new photonic framework is proposed in which local learning rules, tunable memory, and adaptive dynamics arise intrinsically from driven-dissipative nonlinear optics, shown through numerical simulations.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
QuChaTeR hybridizes chaotic maps and variational quantum circuits with recurrent networks and wavelets to achieve faster convergence and better performance than classical and quantum-inspired baselines on real seismic datasets.
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Reconfigurable Nonlinear Photonic Networks for In-Situ Learning and Memory Formation via Driven-Dissipative Dynamics
A new photonic framework is proposed in which local learning rules, tunable memory, and adaptive dynamics arise intrinsically from driven-dissipative nonlinear optics, shown through numerical simulations.
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QuChaTeR: A Hybrid Quantum-Chaotic Temporal Framework for Earthquake Prediction
QuChaTeR hybridizes chaotic maps and variational quantum circuits with recurrent networks and wavelets to achieve faster convergence and better performance than classical and quantum-inspired baselines on real seismic datasets.