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reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","nanophotonics","neural-network","optics","photonics"],"created_at":"2026-01-21T16:06:10.185Z","updated_at":"2026-01-21T16:06:10.883Z","avatar_url":"https://github.com/fancompute.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# neuroptica [![Documentation Status](https://readthedocs.org/projects/neuroptica/badge/?version=latest)](https://neuroptica.readthedocs.io/en/latest/?badge=latest) [![Build Status](https://travis-ci.com/fancompute/neuroptica.svg?token=CSoUuvqmixfJpdwkLqet\u0026branch=master)](https://travis-ci.com/fancompute/neuroptica)\n\n`neuroptica` is a flexible chip-level simulation platform for [nanophotonic neural networks](https://arxiv.org/abs/1903.04579) written in Python/NumPy. It provides a wide range of abstracton levels for simulating optical NN's: the lowest-level functionality allows you to manipulate the arrangement and properties of individual phase shifters on a simulated chip, and the highest-level features provide a Keras-like API for designing optical NN by stacking network layers.\n\n\n## Installation\n\nThe easiest way to get started with `neuroptica` is to install directly from the Python package manager:\n\n```\npip install neuroptica\n```\n\nAlternately, you can clone the repository source code and edit it as needed with:\n\n```\ngit clone https://github.com/fancompute/neuroptica.git\npip install -e neuroptica\n```\n\nTo run unit tests, use `- python -m unittest discover -v` from the root package directory.\n\n## Getting started\n\nFor an overview of `neuroptica`, read the [documentation](https://neuroptica.readthedocs.io). Example notebooks are included in the [`neuroptica-notebooks`](https://github.com/fancompute/neuroptica-notebooks) repository:\n\n- [Planar data classification using electro-optic activation functions](https://github.com/fancompute/neuroptica-notebooks/blob/master/neuroptica_demo.ipynb)\n\n![neuroptica training demo](https://raw.githubusercontent.com/fancompute/neuroptica/master/img/neuroptica_demo.gif)\n\n\n## Citing\n\n`neuroptica` was written by [Ben Bartlett](https://github.com/bencbartlett), [Momchil Minkov](https://github.com/momchilmm), [Tyler Hughes](https://github.com/twhughes), and  [Ian Williamson](https://github.com/ianwilliamson). If you find this useful for your research, please cite the GitHub repository and/or the JSQTE paper:\n\n```\n@misc{Bartlett2019Neuroptica,\n  author = {Ben Bartlett and Momchil Minkov and Tyler Hughes and Ian A. D. Williamson},\n  title = {Neuroptica: Flexible simulation package for optical neural networks},\n  year = {2019},\n  publisher = {GitHub},\n  journal = {GitHub repository},\n  howpublished = {\\url{https://github.com/fancompute/neuroptica}},\n  commit = {06484f698ee038eeb128cdfbd4c59a7e96185bb4}\n}\n```\n\n\n```\n@article{Williamson2019Reprogrammable, \n  author={I. A. D. Williamson and T. W. Hughes and M. Minkov and B. Bartlett and S. Pai and S. Fan}, \n  journal={IEEE Journal of Selected Topics in Quantum Electronics}, \n  title={Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks},\n  year={2020}, \n  volume={26}, \n  number={1}, \n  pages={1-12}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffancompute%2Fneuroptica","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffancompute%2Fneuroptica","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffancompute%2Fneuroptica/lists"}