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NanoPhotoNet-MPM: A Physics-Informed Deep Learning for Inverse Design of NbOCl2 Biphoton Quantum Sources

Islam I. Abdulaal, Omar A. M. Abdelraouf

Published Sep 15, 2026• 0 citations
Publisher

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.

DOI
10.20944/preprints202609.1239.v1

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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.
Single-photon sources generated by spontaneous parametric downconversion (SPDC) have been widely applied in quantum information science. Although analytical optimization methods have provided general and effective approaches for achieving high-purity single-photon sources, the design of such sources remains a complex task due to the intricate physical principles involved. We propose a hybrid learning framework to enable mode recognition and inverse design of pure single photons based on the joint spectral amplitude (JSA) in SPDC. This framework first enhances inter-mode differences and employs a probabilistic clustering approach to accurately recognize phase-matching modes, achieving a test accuracy of 91.92%. Subsequently, the multimodal information from the JSA and SPDC parameters is encoded into vectors and mapped into a shared high-dimensional embedding space to align the two modalities. Based on this alignment, a linear regression model is constructed to predict SPDC parameters, achieving accuracies of 90.66%, 85.03%, and 91.94% for the pump optical bandwidth, crystal length, and signal optical wavelength, respectively. Our framework confirms the feasibility of inferring phase-matching modes and key configurable information in reverse from the target JSA with the assistance of artificial intelligence, thereby offering new perspectives for photon source design. We hope it will be a useful and promising method for constructing high-purity single-photon sources.
Abstract Integrated photon-pair sources are crucial for scalable photonic quantum systems. Thin-film lithium niobate is a promising platform for on-chip photon-pair generation through spontaneous parametric down-conversion (SPDC). However, the device implementation faces practical challenges. Periodically poled lithium niobate (PPLN), despite enabling flexible quasi-phase matching, suffers from poor fabrication reliability and device repeatability, while conventional modal phase matching (MPM) methods yield limited efficiencies due to inadequate mode overlaps. Here, we introduce a layer-poled lithium niobate (LPLN) nanophotonic waveguide for efficient photon-pair generation. It leverages layer-wise polarity inversion through electrical poling to break spatial symmetry and significantly enhance nonlinear interactions for MPM, achieving a notable normalized second-harmonic generation (SHG) conversion efficiency of 4615% W-1 cm-2. Through a cascaded SHG and SPDC process, we demonstrate photon-pair generation with a normalized brightness of 3.1×106 Hz nm-1 mW-2 in a 3.3 mm long LPLN waveguide, surpassing existing on-chip sources under similar operating configurations. Crucially, our LPLN waveguides offer enhanced fabrication reliability and reduced sensitivity to geometric variations and temperature fluctuations compared to PPLN devices. We expect LPLN to become a promising solution for on-chip nonlinear wavelength conversion and non-classical light generation, with immediate applications in quantum communication, networking, and on-chip photonic quantum information processing.
Nonlinear 2D materials such as 3R-phase molybdenum disulfide (3R-MoS2) offer strong second-order optical nonlinearities in an atomically thin platform, making them attractive for on-chip frequency conversion, quantum light generation, and integrated nonlinear nanophotonics. However, the second harmonic generation (SHG) efficiency of monolayer or few-layer 3R-MoS2 deposited on planar substrates remains fundamentally limited by weak light-matter interaction, poor phase matching, and small interaction volumes. Here, we introduce NanoPhotoNet-PINL, a physics informed AI-driven inverse design framework based on a hybrid one-dimensional convolutional neural network and deep neural network autoencoder, tailored for nonlinear MLMs metasurfaces. The model directly maps target dual-resonant reflection spectra at the fundamental and second-harmonic wavelengths to the required multi-layer geometries and material compositions that maximize the effective nonlinear overlap with an embedded 3R-MoS2 sheet. By integrating Maxwell-based nonlinear electrodynamics into the inverse design loop, we compute the second-harmonic conversion efficiency and modal overlap factors for each predicted MLMs design, enabling physics-guided training and evaluation. Our approach achieves an inverse-design prediction efficiency exceeding ~99.2 % along the linear spectral manifold, while the optimized dual-resonant MLMs yield more than three orders of magnitude enhancement in SHG intensity compared to a bare 3R-MoS2 flake on a planar substrate. NanoPhotoNet-PINL establishes a generalizable paradigm for intelligent inverse design of nonlinear multi-layer metasurfaces and phase-matched dual-resonant cavities for high-efficiency second-order processes.
The realization of a scalable quantum internet requires efficient light-matter interfaces that map stationary qubits onto photonic carriers for long-distance transmission. A central challenge is the generation of entangled photons simultaneously compatible with single-emitter transitions and low-loss telecom fiber infrastructure. Spontaneous parametric down-conversion in integrated photonic platforms offers a promising route toward this goal. Among available material systems, AlGaAs is particularly attractive due to its large second-order nonlinearity and strong potential for monolithic integration. However, engineering the spectral and spatial properties of the generated quantum states requires the simultaneous optimization of numerous geometric and material parameters, a task remaining computationally demanding for conventional numerical approaches. To address this challenge and enable rapid and high-fidelity modeling of complex nonlinear photonic devices, we develop an inverse-design framework based on neural network surrogate models. Using this readily extendable method, we design a transversely pumped AlGaAs waveguide microcavity that produces polarization-entangled photon pairs in distinct spatial modes and frequency channels, one at 1092 nm, resonant with a 88Sr+^{88}\text{Sr}^{+}88Sr+ transition, and the other at 1550 nm in the telecom C-band. This device establishes a direct photonic interface between trapped-ion qubits and long-haul fiber networks, providing a scalable pathway toward hybrid quantum network architectures.
Single-photon sources are the cornerstone of the optical quantum information process, including quantum computing, simulation, metrology, etc. The practical application requires these sources to be high purity and indistinguishability. A readily available approach is to use heralded single-photon sources to generate photon pairs via spontaneous parametric down-conversion (SPDC). However, the purity of photon pairs generated by SPDC is usually limited by the spectral correlations because of energy conservation. Extra spectral filters and group velocity matching (GVM) can be used to generate spectral uncorrelated photon pairs on-chip. However, the material dispersion of most materials cannot meet the GVM requirements, and extra components will reduce the integration. In this work, we develop a strategy to generate pure single photon on-chip by mode coupling in heterogeneously coupled waveguides. This strategy can tailor the group velocity directly, or shape biphoton wave function through a new degree of freedom that is the non-constant overlap between interacting modes. This allows the generation of high-purity single photons totally on-chip. Design examples based on lithium niobate waveguide are provided to generate spectrally pure photon pairs. This strategy is generic that it is not limited to any material and waveguide system, providing new possibilities in the generation of quantum photons.
Abstract We study modal characteristics of a customized ridge waveguide in lithium niobate designed to generate twin photons at telecom wavelength. A quantum analysis of SPDC predicts the possible down conversion processes and optimizes the input beam parameters for fundamental mode emission. Further, a joint spectral amplitude analysis ensures that the parameters are ambient for generating frequency-correlated photons throughout the phase-matching spectrum. Moreover, negatively correlated degenerate signal/idler photon pairs in the fundamental mode emission occurs at 1550 nm. A key result of our paper is an explicit expression and plot for comparing the generated signal power in case of waveguides as compared to bulk crystals. Our calculations clearly show a dependence of ℒ 3/2 for a waveguide of length ℒ , compared to a linear dependence in case of a bulk crystal.