Polynomial neural surrogates for designing photonic quantum experiments
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 ac...
| Authors | Rohit Chaurasiya, Xuemei Gu |
|---|---|
| Primary category | quant-ph |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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