Continuous-variable quantum neural networks

This repository contains the source code used to produce the results presented in "Continuous-variable quantum neural networks".

Requirements

To construct and optimize the variational quantum circuits, these scripts and notebooks use the TensorFlow backend of Strawberry Fields. In addition, matplotlib is required for generating output plots.

Due to subsequent interface upgrades, these scripts will work only with the following configuration

• Strawberry Fields version 0.10.0
• TensorFlow version 1.3
• Python version 3.5 or 3.6

Your version of Python can be checked by running python --version. The correct versions of StrawberryFields and TensorFlow can be installed by running pip install -r requirements.txt from the main directory of this repository.

Contents

• Function fitting: The folder function_fitting contains the Python script function_fitting.py, which automates the process of fitting classical functions using continuous-variable (CV) variational quantum circuits. Simply specify the function you would like to fit, along with other hyperparameters, and this script automatically constructs and optimizes the CV quantum neural network. In addition, training data is also provided.

• Quantum autoencoder: coming soon.

• Quantum fraud detection: The folder fraud_detection contains the Python script fraud_detection.py, which builds and trains a hybrid classical/quantum model for fraud detection. Additional scripts are provided for visualizing the results.

• Tetrominos learning: The folder tetrominos_learning contains the Python script tetrominos_learning.py, which trains a continuous-variable (CV) quantum neural network. The task of the network is to encode 7 different 4X4 images, representing the (L,O,T,I,S,J,Z) tetrominos, in the photon number distribution of two light modes. Once the training phase is completed, the script plot_images.py can be executed in order to generate a .png figure representing the final results.

Using the scripts

To use the scripts, simply set the input data, output data, and hyperparametersby modifying the scripts directly - and then enter the subdirectory and run the script using Python 3:

python3 script_name.py

The outputs of the simulations will be saved in the subdirectory.

To access any saved data, the file can be loaded using NumPy:

results = np.load('simulation_results.npz')

Authors

Nathan Killoran, Thomas R. Bromley, Juan Miguel Arrazola, Maria Schuld, Nicolás Quesada, and Seth Lloyd.

If you are doing any research using this source code and Strawberry Fields, please cite the following two papers:

Nathan Killoran, Thomas R. Bromley, Juan Miguel Arrazola, Maria Schuld, Nicolás Quesada, and Seth Lloyd. Continuous-variable quantum neural networks. Physical Review Research, 1(3), 033063 (2019).

Nathan Killoran, Josh Izaac, Nicolás Quesada, Ville Bergholm, Matthew Amy, and Christian Weedbrook. Strawberry Fields: A Software Platform for Photonic Quantum Computing. arXiv, 2018. Quantum, 3, 129 (2019).

License

This source code is free and open source, released under the Apache License, Version 2.0.