NanoPhotoNet-MPM: A Physics-Informed Deep Learning for Inverse Design of NbOCl2 Biphoton Quantum Sources
Abstract
Integrated quantum photonics requires chip-scale biphoton sources, but phase matching, nonlinear coupling, loss, and spectral purity are coupled in high-index anisotropic waveguides. We present NanoPhotoNet-MPM, a physics-informed neural surrogate for modal spontaneous parametric down-conversion (SPDC) in monoclinic NbOCl2 waveguides (d_22≈100 pm V^(-1)). The framework couples a DeepONet transverse eigensolver, regularised by the full-vectorial Helmholtz equations, with a quantum coupled-wave-equation propagator trained on 70 audited finite-difference eigenmode geometries. The trained model predicts a degenerate phase-matched crossing at w=283.13 nm and h=500.00 nm with an effective-index MAE of (4.16±0.75)×〖10〗^(-3), outperforming Gaussian process regression and plain multi-layer perceptron baselines by one to two orders of magnitude while guaranteeing wave-equation consistency. Joint spectral amplitude analysis at the degenerate design yields a heralded purity of P=0.122 (K=8.20), limited by symmetry-enforced group-velocity mismatch. A multi-mode genetic-algorithm search across Type-0, Type-I, and Type-II SPDC configurations identifies a Type-II design (m_p=4, m_s=0, m_i=1) at w=592.0 nm and h=270.0 nm that reaches P≈0.350 at L=0.5 mm with 95.9% idler survival under realistic propagation loss. The resulting framework is a generalizable surrogate-assisted design and active-learning guidance method for anisotropic van der Waals quantum photonic platforms.
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