Kavli Affiliate: David W. Miller
| Summary:
Programmable photonic circuits are a natural platform for physical learning, where hardware parameters are trained directly from measured feedback. We propose a physical learning algorithm and architecture for continuous-variable quantum systems where a photonic circuit is trained in situ to invert an unknown lossless Gaussian quantum operation. We prove that a generic symplectic matrix $SinmathrmSp(2N,mathbbR)$ admits a decomposition into a triangular array of two-mode symplectic gates on neighboring modes, followed by single-mode symplectic gates. We then introduce the symplectic Reck mesh, which physically realizes this factorization, generalizing the unitary Reck mesh widely used in optical neural networks. When placed after an unknown Gaussian unitary, the mesh is trained in situ by nulling local response blocks one at a time using optical probes and measurements. We identify a local condition number that quantifies the squeezing required for a nulling step and its sensitivity to measurement error. We numerically demonstrate finite-shot training and robustness to systematic gate-calibration errors. Our work extends self-configuring optical networks from unitary to symplectic transformations, bringing physical learning to continuous-variable quantum photonic hardware.
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