Welcome to Polyadic QML’s documentation!

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(
   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 Quickstart and in the User’s Guide. 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.


From PyPI, at the command line:

pip install polyadicqml

Installing latest stable from github:

git clone https://github.com/entropicalabs/polyadicQML.git polyadicqml
cd polyadicqml
pip install -U .


With polyadicQML, training a quantum-machine-learning model and using it to predict on new points can be done in a few lines.

The following links provide a quick overview of the API through some motivating examples. To gain a deeper understanding of the interface, and of quantum machine learning, have a look at our User’s Guide.

Indices and tables