Quantum Pattern Recognition in Photonic Circuits
This work addresses quantum state analysis for photonics researchers, but it appears incremental as it applies existing supervised learning methods to new data.
The paper tackled the problem of characterizing photonic states, such as entanglement and tomography, using machine learning on photon number distributions from optical circuits, achieving satisfactory regression metrics.
This paper proposes a machine learning method to characterize photonic states via a simple optical circuit and data processing of photon number distributions, such as photonic patterns. The input states consist of two coherent states used as references and a two-mode unknown state to be studied. We successfully trained supervised learning algorithms that can predict the degree of entanglement in the two-mode state as well as perform the full tomography of one photonic mode, obtaining satisfactory values in the considered regression metrics.