Polynomial neural surrogates for designing photonic quantum experiments

2026-10-05

AI Summary

This study addresses the computational bottleneck imposed by expensive physical simulators that hinder search efficiency in photonic quantum experiment design. To overcome this limitation, we propose a physics-inspired polynomial neural network surrogate model to replace conventional simulators. By exploiting the physical correspondence between graph perfect matchings and quantum amplitudes, the method designs polynomial activation functions that achieve higher predictive accuracy with fewer parameters. In 4- to 8-photon systems, the proposed model significantly outperforms standard multilayer perceptrons (MLPs) and successfully accelerates the inverse design of complex quantum states, such as Greenberger–Horne–Zeilinger (GHZ) states. This work establishes a new paradigm for efficient optimization in quantum optical experiments.

Abstract

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.

Problem

• quantum experiment design
• physics simulators
• inverse design
• neural surrogate
• photonic quantum experiments

Innovation

• Polynomial neural surrogate
• Photonic quantum experiments
• Inverse design
• Physics-informed neural networks
• Graph perfect matchings