Quantum Machine Learning Example

This project implements a disciminative algorithm for quantum machine learning described in Towards Quantum Machine Learning with Tensor Networks. The quantum circuits are implemented using Qiskit.

Install

Simply clone the repository and install the necessary requirements

    git clone https://github.com/Gistbatch/quantum.git
    cd quantum
    python -m venv venv
    venv\Scripts\activate (Windows)
    source venv\bin\activate (Linux)
    python -m pip install --upgrade pip

Install the project dependencies:

    pip install tensorflow numpy matplotlib qiskit

Run

As an example you can run the network file as an module

    python -m quantum.network

Availabel Ciruits

1. Default Circuit using unitaries for two Qubits at a time described in figure 7.

   

2. Efficient Circuit using unitaries for four Quibts at a time descibed in figure 11.

   

Sources

[1] W. Huggins, P. Patil, B. Mitchell, K. B. Whaley, and E. M. Stoudenmire, “Towards quantum machine learningwith tensor networks, ”Quantum Science and Technology, vol. 4, p. 024001, Jan 2019

[2] Qiskit: An Open-source Framework for Quantum Computing 2019, see Qiskit.bib