# Quantum Pattern Recognition in Photonic Circuits

arXiv: https://arxiv.org/abs/2107.09961 · HTML: https://mlsanity.com/paper/2107.09961/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Experimental neural network enhanced quantum tomography](https://mlsanity.com/paper/1904.05902/) (2019)
- [Machine learning assisted quantum state estimation](https://mlsanity.com/paper/2003.03441/) (2020)
- [Experimental quantum homodyne tomography via machine learning](https://mlsanity.com/paper/1907.06589/) (2019)
- [Supervised Quantum Learning without Measurements](https://mlsanity.com/paper/1612.05535/) (2016)
- [Measurement-based adaptation protocol with quantum reinforcement learning](https://mlsanity.com/paper/1803.05340/) (2018)

## Built on by (most cited first, 3 of 3)

- [Dimension-adaptive machine-learning-based quantum state reconstruction](https://mlsanity.com/paper/2205.05804/) (2022)
- [Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation](https://mlsanity.com/paper/2309.04434/) (2023)
- [Machine Learning for maximizing the memristivity of single and coupled quantum memristors](https://mlsanity.com/paper/2309.05062/) (2023)

## Abstract (arXiv)

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.