An augmented neural ODE framework learns single-qubit states and time-dependent dissipation parameters from simulated weak-measurement data, and supports PD and LQR feedback control.
Neural Controlled Differential Equations with Quantum Hidden Evolutions
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abstract
We introduce a class of neural controlled differential equation inspired by quantum mechanics. Neural quantum controlled differential equations (NQDEs) model the dynamics by analogue of the Schr\"{o}dinger equation. Specifically, the hidden state represents the wave function, and its collapse leads to an interpretation of the classification probability. We implement and compare the results of four variants of NQDEs on a toy spiral classification problem.
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Quantum Filtering and Stabilization of Dissipative Quantum Systems via Augmented Neural Ordinary Differential Equations
An augmented neural ODE framework learns single-qubit states and time-dependent dissipation parameters from simulated weak-measurement data, and supports PD and LQR feedback control.