Metadata-Version: 1.2
Name: qcompress
Version: 0.0.1.dev12
Summary: A Python framework for the quantum autoencoder algorithm
Home-page: https://github.com/hsim13372/QCompress
Author: hannahsim
Author-email: hsim13372@gmail.com
License: Apache-2.0
Description: 
        =========
        QCompress
        =========
        
        
        Description
        ===========
        
        QCompress 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 <https://www.rigetti.com/qcs>`__. For a more in-depth description of QCompress (including the naming convention for the types of qubits involved in the QAE circuit), click `here <https://github.com/hsim13372/QCompress/blob/master/examples/intro.rst>`__. 
        
        For more information about the algorithm, see `Romero et al <https://arxiv.org/abs/1612.02806>`__. 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 <https://arxiv.org/abs/1810.10576>`__).
        
        Features
        --------
        
        This code is based on an older `version <https://github.com/hsim13372/QCompress-1>`__ written during Rigetti Computing's hackathon in April 2018. Since then, we've updated and enhanced the code, supporting the following features:
        
        * Executability on Rigetti's quantum processor(s)
        * Several training schemes for the autoencoder
        * Use of the ``RESET`` operation for the encoding qubits (lowers qubit requirement)
        * User-definable training circuit and/or classical optimization routine
        
        
        Installation
        ============
        
        There are a few options for installing QCompress:
        
        1. To install QCompress using ``pip``, execute:
        
        .. code-block:: bash
        
        	pip install qcompress
        
        
        2. To install QCompress using ``conda``, execute:
        
        .. code-block:: bash
        
        	conda install -c rigetti -c hsim13372 qcompress
        
        
        3. To instead install QCompress from source, clone this repository, ``cd`` into it, and run:
        
        .. code-block:: bash
        
        	git clone https://github.com/hsim13372/QCompress
        	cd QCompress
        	python -m pip install -e .
        
        
        Try executing ``import qcompress`` to test the installation in your terminal.
        
        Note that the pyQuil version used requires Python 3.6 or later. For installation on a user QMI, please click `here <https://github.com/hsim13372/QCompress/blob/master/qmi_instructions.rst>`__.
        
        
        Examples
        ========
        
        We 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).
        
        .. csv-table::
           :header: Notebook, Feature(s)
        
           `qae_h2_demo.ipynb <https://github.com/hsim13372/QCompress/blob/master/examples/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.
           `qae_two_qubit_demo.ipynb <https://github.com/hsim13372/QCompress/blob/master/examples/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.
           `run_landscape_scan.ipynb <https://github.com/hsim13372/QCompress/blob/master/examples/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.
        
        
        Disclaimer
        ==========
        
        We note that there is a lot of room for improvement and fixes. Please feel free to submit issues and/or pull requests!
        
        
        How to cite
        ===========
        
        When using QCompress for research projects, please cite:
        
        	Sukin Sim, Yudong Cao, Jonathan Romero, Peter D. Johnson and Alán Aspuru-Guzik.
        	*A framework for algorithm deployment on cloud-based quantum computers*.
        	`arXiv:1810.10576 <https://arxiv.org/abs/1810.10576>`__. 2018.
        
        
        Authors
        =======
        
        `Sukin (Hannah) Sim <https://github.com/hsim13372>`__ (Harvard), `Zapata Computing, Inc. <https://zapatacomputing.com/>`__
        
Platform: UNKNOWN
Requires-Python: >=3.6
