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.
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 .
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.
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