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library to integrate automatic differentiation tools such as JAX with QuTiP and related quantum software packages.\n\nThis package is a work in progress. Feel free to take part in the discussions by opening new issues.\n\n- [Installation](#installation)\n- [About](#about)\n- [Documentation](#documentation)\n- [Contributing](#contributing)\n- [Acknowledgements](#acknowledgements)\n\n\n## Installation\n\nTo install ``qgrad`` development version, clone this repository and from the terminal type\n\n```\npython setup.py develop\n```\n\n### Requirements\n``qgrad`` dependencies are automatically installed with `pip`. They are:\n\n``numpy scipy matplotlib cython pytest qutip jax``\n\n\n## About\n\n``qgrad`` is a library that implements Hamiltonian learning in the context of quantum physics-based optimization tasks.\n ``qgrad`` reproduces essential [QuTiP](http://qutip.org/) functions to reduce the friction for existing QuTiP users.\n\n\n``qgrad`` leverages the powerful Python scientific stack and interfaces with the popular machine learning library JAX, to make auto-differentiation of many quantum routines possible for the desired learning tasks.\n\n## Documentation\nThe latest documentation can be found [here](https://qgrad.readthedocs.io/en/latest). It includes the API reference and examples.\n\n\n## Contributing\nWe are in the early stages of designing the tool and welcome any discussion in the form of [Issues](https://github.com/qgrad/qgrad/issues/new) or Pull Requests.\n\n## Acknowledgements\nThis package started as part of @araza6's GSoC 2020 project. \nAll the work relevant to GSoC 2020 is compiled in\nthis release: https://github.com/qgrad/qgrad/releases/tag/0.0.dev2\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqgrad%2Fqgrad","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqgrad%2Fqgrad","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqgrad%2Fqgrad/lists"}