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High level API to define, train and deploy Polyadic Quantum Machine Learning models

Project description

This package provides a library to define, train and deploy Quantum Machine Learning models.

This library has been used to train a qmodel with the Iris flower dataset on IBM quantum computers:

The quantum circuits can run on top of any quantum computer provider. As for now, it implements interfaces for a fast simulator, manyq, and Qiskit.


From PyPI, at the command line:

pip install polyadicqml

Installing latest stable from github:

git clone polyadicqml
cd polyadicqml
pip install -U .


You can find a quickstart guide, the tutorial and the module references in the docs.

Sample code

Training a model on a simulator and testing it on a real quantum computer can be done in a few lines:

# 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(
   nbqbits=nbqbits, nbparams=nbparams,

# Change the model backend and run it
model.nbshots = 300
model.job_size = 30

pred_test = model(input_test)

You can find out more in the 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.

Project details

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