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

Project description

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:

# 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 .

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