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Polynomial neural surrogates for designing photonic quantum experiments
Oct 5, 2026Rohit Chaurasiya, Xuemei GuNanophotonic
Institute of Condensed Matter Theory and Optics, Friedrich Schiller University Jena, Max-Wien-Platz 1, 07743 Jena, Germany
HALO: A Physics-Aware LLM Agent Framework for Nanophotonic Design
Aug 28, 2026Yubo Zhang, Jinlin Xiang, Zijun Zhao +3Nanophotonic Physics-Aware Models
Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA
A Comprehensive Review of Large Language Models for Nanophotonics: From Surrogate Modeling to Autonomous Design
Aug 18, 2026Huanshu Zhang, Kegeng Tang, Lei Kang +3Nanophotonic Large Language Model Workflows
The Pennsylvania State University, Department of Electrical Engineering, University Park, PA 16802, USA · University of Tennessee at Chattanooga, Department of Computer Science and Engineering, Chattanooga, TN 37403, USA
Two-Stage Deformable-Convolutional Inverse Design of Nanophotonic Absorbers from Optical Spectra
Aug 12, 2026Waleed Waseer, Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim +1Nanophotonic Metamaterials
School of Physics and State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, China · SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia · Interdisciplinary Research Center for Intelligent Secure Systems (IRC-ISS), King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia +1
PICopilot: An LLM-based Agentic Framework for Assisting Photonic Integrated Circuit Design via Script Generation
Aug 3, 2026Xiaohan Jiang, Zeyu Li, Wei Zhang +1Nanophotonic Printed Circuit Board
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology · 2Microelectronics Thrust, The Hong Kong University of Science and Technology (Guangzhou)
From Pixels to PCells: A Neurosymbolic Approach to Photonic Component Creation
Jul 29, 2026Aadarsh Agarwal, Kenaish Al Qubaisi, Dirk EnglundNanophotonic Pixelloop
Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
When Every Simulation Counts: Value-Based Reinforcement Learning for Accelerated Photonics Inverse Design
Jul 26, 2026Longying Wen, Feiyang Wu, Jinglin Yu +3Nanophotonic Deep Q-Networks
School of Science and Engineering The Chinese University of Hong Kong, Shenzhen Shenzhen, China · Nick Holonyak Micro and Nanotechnology Laboratory University of Illinois Urbana-Champaign Urbana, Illinois, USA
Reliability-Aware Bayesian Optimization of 1310 nm PCSELs with FDTD Verification
Jul 23, 2026Jinglin Yu, Feiyang Wu, Longying Wen +3Nanophotonic Bayesian Optimization
School of Science and Engineering (SSE), The Chinese University of Hong Kong, Shenzhen, Shenzhen 518172, China · Nick Holonyak Micro and Nanotechnology Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801 USA
Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
Jul 8, 2026Xiangming Huang, Guannan Zhang, Lu Lu +1Generative Inverse Design Topology Optimization
Enhancing Co-packaging Optics Enabled Silicon Photonics Security Assurance Hardware Fingerprinting
Jun 26, 2026Liton Kumar Biswas, M Shafkat M Khan, Himanandhan Reddy Kottur +3Nanophotonic Optical
Department of Electrical and Computer Engineering, University of Florida 5000 Malachowsky Hall, 1889 Museum Road Gainesville, Florida-32611, USA
An LLM-Orchestrated Agent for Directional-Coupler Design with Self-Consistent Eigenmode and FDTD Validation
Jun 21, 2026Saumya Biswas, Amrit De, Md Tauhidul IslamNanophotonic
Department of Mechanical Engineering, University of Maryland, College Park, MD, USA · Apsidal LLC, Riverside, California, USA · Department of Radiation Oncology, Stanford University, United States
Will Accurate Fields Mislead Photonic Design? FromGlobal Accuracy to Port Readout
Jun 2, 2026Yitian Zhang, Yonghong chen, Youming Chen +6Nanophotonic Neural Fields
Sun Yat-sen University · The Hong Kong University of Science and Technology (Guangzhou)
Learning Design Skills as Memory Policies for Agentic Photonic Inverse Design
May 28, 2026Shengchao Chen, Ting Shu, Sufen RenNanophotonic Inverse Design
AAII, University of Technology Sydney · School of Artificial Intelligence, Shenzhen University · School of Information and Communication Engineering, Hainan University
Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices
May 21, 2026Farah Elnakhal, Alberto Marchisio, Nouhaila Innan +2Neural Architecture Search Nanophotonic
Science Division, New York University Abu Dhabi, UAE · eBrain Lab, Division of Engineering, New York University Abu Dhabi, PO Box 129188, Abu Dhabi, UAE · Center for Quantum and Topological Systems, NYUAD Research Institute, New York University Abu Dhabi, UAE +1
Artificial Intelligence Reshapes Microwave Photonics
May 20, 2026Peng Li, Xihua Zou, Jia Ye +2Nanophotonic
Center for Information Photonics and Communications, School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China · Key Laboratory of Photonic-Electronic Integration and Communication-Sensing Convergence, Southwest Jiaotong University, Chengdu 611756, China
Physics-Based Flow Matching for Full-Field Prediction of Silicon Photonic Devices
May 7, 2026Joseph Quaratiello, Anthony RizzoNanophotonic Queryable Sector Flow Fields
Thayer School of Engineering, Dartmouth College, Hanover, NH 03755
Physically Grounded Monocular Depth via Nanophotonic Wavefront Encoding
Mar 20, 2025Bingxuan Li, Jiahao Wu, Yuan Xu +6Monocular Depth Estimation Nanophotonic
New York University · Columbia University

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Polynomial neural surrogates for designing photonic quantum experiments

Rohit Chaurasiya, Xuemei Gu

Institute of Condensed Matter Theory and Optics, Friedrich Schiller University Jena, Max-Wien-Platz 1, 07743 Jena, Germany

Nanophotonic

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Physics simulators can support the discovery of quantum experiments by predicting the states generated by experimental configurations. When these simulators are computationally expensive, repeated simulator calls can limit the search for experiments that generate a desired quantum state. Here, we develop a physics-inspired polynomial neural surrogate for PyTheus, a graph-based quantum-optics simulator, to predict quantum states and use it to design quantum experiments. Its polynomial activations are motivated by the relation between graph perfect matchings and the resulting state amplitudes. We train separate surrogate models for four-, six-, and eight-photon systems and show that they achieve higher prediction accuracy with fewer trainable parameters than standard multilayer perceptrons. We then use the trained surrogates for inverse design of GHZ, W, and linear-cluster states. For the larger systems, the surrogates also enable faster inverse design than direct optimization with PyTheus. These results suggest that incorporating the underlying physics into neural surrogates can provide an efficient approach to quantum experiment design.

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