{ "info": { "author": "hannahsim", "author_email": "hsim13372@gmail.com", "bugtrack_url": null, "classifiers": [], "description": "\n=========\nQCompress\n=========\n\n\nDescription\n===========\n\nQCompress is a Python framework for the quantum autoencoder (QAE) algorithm. Using the code, the user can execute instances of the algorithm on either a quantum simulator or a quantum processor provided by Rigetti Computing's `Quantum Cloud Services `__. For a more in-depth description of QCompress (including the naming convention for the types of qubits involved in the QAE circuit), click `here `__. \n\nFor more information about the algorithm, see `Romero et al `__. Note that we deviate from the training technique used in the original paper and instead introduce two alternative autoencoder training schemes that require lower-depth circuits (see `Sim et al `__).\n\nFeatures\n--------\n\nThis code is based on an older `version `__ written during Rigetti Computing's hackathon in April 2018. Since then, we've updated and enhanced the code, supporting the following features:\n\n* Executability on Rigetti's quantum processor(s)\n* Several training schemes for the autoencoder\n* Use of the ``RESET`` operation for the encoding qubits (lowers qubit requirement)\n* User-definable training circuit and/or classical optimization routine\n\n\nInstallation\n============\n\nThere are a few options for installing QCompress:\n\n1. To install QCompress using ``pip``, execute:\n\n.. code-block:: bash\n\n\tpip install qcompress\n\n\n2. To install QCompress using ``conda``, execute:\n\n.. code-block:: bash\n\n\tconda install -c rigetti -c hsim13372 qcompress\n\n\n3. To instead install QCompress from source, clone this repository, ``cd`` into it, and run:\n\n.. code-block:: bash\n\n\tgit clone https://github.com/hsim13372/QCompress\n\tcd QCompress\n\tpython -m pip install -e .\n\n\nTry executing ``import qcompress`` to test the installation in your terminal.\n\nNote that the pyQuil version used requires Python 3.6 or later. For installation on a user QMI, please click `here `__.\n\n\nExamples\n========\n\nWe provide several Jupyter notebooks to demonstrate the utility of QCompress. We recommend going through the notebooks in the order shown in the table (top-down).\n\n.. csv-table::\n :header: Notebook, Feature(s)\n\n `qae_h2_demo.ipynb `__, Simulates the compression of the ground states of the hydrogen molecule. Uses OpenFermion and grove to generate data. Demonstrates the \"halfway\" training scheme.\n `qae_two_qubit_demo.ipynb `__, Simulates the compression of a two-qubit data set. Outlines how to run an instance on an actual device. Demonstrates the \"full with reset\" training scheme.\n `run_landscape_scan.ipynb `__, Shows user how to run landscape scans for small (few-parameter) instances. Demonstrates setup of the \"full with no reset\" training scheme.\n\n\nDisclaimer\n==========\n\nWe note that there is a lot of room for improvement and fixes. Please feel free to submit issues and/or pull requests!\n\n\nHow to cite\n===========\n\nWhen using QCompress for research projects, please cite:\n\n\tSukin Sim, Yudong Cao, Jonathan Romero, Peter D. Johnson and Al\u00e1n Aspuru-Guzik.\n\t*A framework for algorithm deployment on cloud-based quantum computers*.\n\t`arXiv:1810.10576 `__. 2018.\n\n\nAuthors\n=======\n\n`Sukin (Hannah) Sim `__ (Harvard), `Zapata Computing, Inc. `__\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/hsim13372/QCompress", "keywords": "", "license": "Apache-2.0", "maintainer": "", "maintainer_email": "", "name": "qcompress", "package_url": "https://pypi.org/project/qcompress/", "platform": "", "project_url": "https://pypi.org/project/qcompress/", "project_urls": { "Homepage": "https://github.com/hsim13372/QCompress" }, "release_url": "https://pypi.org/project/qcompress/0.0.1.dev12/", "requires_dist": [ "pytest", "jupyter", "pyquil (>=2.0.0b1)", "openfermion (>=0.7)" ], "requires_python": ">=3.6", "summary": "A Python framework for the quantum autoencoder algorithm", "version": 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