Metadata-Version: 2.1
Name: polyadicqml
Version: 0.1.0b3
Summary: High level API to define, train and deploy Polyadic Quantum Machine Learning models
Home-page: https://github.com/entropicalabs/polyadicQML
Author: William Cappelletti
Author-email: cappelletti.william@gmail.com
License: Apache 2.0
Description: #################################
        Polyadic Quantum Machine Learning
        #################################
        
        This package provides an high level API to define, train and deploy
        **Polyadic Quantum Machine Learning** models.
        
        It implements a general interface which can be used with any quantum provider.
        As for now, it supports a fast simulator, *manyq*, and
        Qiskit_.
        More are coming.
        
        With polyadicQML_, Training a model on a simulator and testing it on a real quantum computer can
        be done in a few lines:
        
        .. code-block:: python
        
           # Define the circuit structure
           make_circuit(bdr, x, params):
              ...
           
           # Prepare a circuit simulator:
        
           qc = mqCircuitML(make_circuit=make_circuit,
                            nbqbits=nbqbits, nbparams=nbparams)
        
           # Instanciate and train the model
        
           model = Classifier(qc, bitstr).fit(input_train, target_train)
        
           # Prepare to run the circuit on an IBMq machine:
        
           backend = Backends("ibmq_ourense", hub="ibm-q")
        
           qc2 = qkCircuitML(
              make_circuit=make_circuit,
              nbqbits=nbqbits, nbparams=nbparams,
              backend=backend
           )
        
           # Change the model backend and run it
           model.set_circuit(qc2)
           model.nbshots = 300
           model.job_size = 30
        
           pred_test = model(input_test)
        
        You can find out more in the `official documentation`_, where you will find tutorials and examples.
        A quickstart through examples can be found in the `examples folder`_, as well as on the website.
        As an introduction to the algorithm you can check out this `video
        presentation`_ from the **IBM Singapore Supercomputing Virtual Forum**. 
        This code has been used to fully train a Quantum Machine Learning model
        on a real quantum computer to classify the Iris flower dataset.
        
        Documentation
        #############
        
        You can find a `quickstart guide`_, the tutorial_ and the module references_ at polyadicqml.entropicalabs.io_.
        
        Installation
        ############
        
        From PyPI, at the command line (not yet supported)::
        
           pip install polyadicqml
        
        Installing latest stable from github::
        
           git clone https://github.com/entropicalabs/polyadicQML.git polyadicqml
           cd polyadicqml
           pip install -U .
         
        
        .. _`video presentation`: https://youtu.be/QZ8ynyG-O9U
        .. _polyadicQML: https://polyadicqml.entropicalabs.io/
        .. _Qiskit: https://qiskit.org/
        .. _polyadicqml.entropicalabs.io: https://polyadicqml.entropicalabs.io
        .. _`official documentation`: https://polyadicqml.entropicalabs.io
        .. _`examples folder`: https://github.com/entropicalabs/polyadicQML/tree/master/examples
        .. _`quickstart guide`: https://polyadicqml.entropicalabs.io/#quickstart
        .. _tutorial: https://polyadicqml.entropicalabs.io/#user-s-guide
        .. _references: https://polyadicqml.entropicalabs.io/#modules
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
