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https://github.com/XanaduAI/quantum-neural-networks

This repository contains the source code used to produce the results presented in the paper "Continuous-variable quantum neural networks". Due to subsequent interface upgrades, these scripts will work only with Strawberry Fields version <= 0.10.0.
https://github.com/XanaduAI/quantum-neural-networks

deep-learning machine-learning optimization photonics quantum quantum-computing quantum-machine-learning tensorflow

Last synced: 3 months ago
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Host: GitHub
URL: https://github.com/XanaduAI/quantum-neural-networks
Owner: XanaduAI
License: apache-2.0
Created: 2018-09-27T12:07:18.000Z (about 6 years ago)
Default Branch: master
Last Pushed: 2023-03-24T23:52:23.000Z (over 1 year ago)
Last Synced: 2024-07-29T07:31:58.039Z (3 months ago)
Topics: deep-learning, machine-learning, optimization, photonics, quantum, quantum-computing, quantum-machine-learning, tensorflow
Language: Python
Homepage: https://arxiv.org/abs/1806.06871
Size: 1.01 MB
Stars: 292
Watchers: 25
Forks: 86
Open Issues: 3
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# Continuous-variable quantum neural networks

This repository contains the source code used to produce the results presented in [*"Continuous-variable quantum neural networks"*](https://doi.org/10.1103/PhysRevResearch.1.033063).

## Requirements

To construct and optimize the variational quantum circuits, these scripts and notebooks use the TensorFlow backend of [Strawberry Fields](https://github.com/XanaduAI/strawberryfields). 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](https://en.wikipedia.org/wiki/Tetromino), 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:

```bash
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:

```python
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](https://doi.org/10.1103/PhysRevResearch.1.033063) (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](https://quantum-journal.org/papers/q-2019-03-11-129/) (2019).

## License

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