polyadicqml 0.1.0b4

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

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: iris.entropicalabs.io

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.

Installation

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 .

Documentation

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(
   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 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.