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.

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.

quantum-neural-networks's Issues

Notice of license infringment

Opened this issue to inform the owner that action can be taken in order to take down this repository, https://github.com/llSourcell/The-Neural-Qubit , in which code from this repo has been taken from and license removed.

Error running the fraud_detection.py file

There is a similar codebase located in the below GIT link. People are leaving issues and someone mentioned the code was "leverage/stolen." Is this repo the correct location to ask questions and/or issues for the codebase? Should people move/copy their issues to this location? Any clarification would be appreciated? Thanks.

******similar codebase location:
https://github.com/llSourcell/The-Neural-Qubit/issues

Error running the roc.py file

I'm getting a latex error when attempting to run the roc.py code. I installed latex, but the problem is still persisting. Any help would be appreciated. Thanks.

ENV:**
Colab
!pip install strawberryfields==0.11.0 tensorflow==1.3.0
set to GPU

COMMAND RAN
!python roc.py

ERROR MESSAGE:*************************
Area under ROC curve: 0.5882831367606355
Closest threshold to optimal ROC: 0.46
Traceback (most recent call last):
File "roc.py", line 76, in
plt.savefig('./roc.pdf')
File "/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py", line 689, in savefig
res = fig.savefig(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py", line 2094, in savefig
self.canvas.print_figure(fname, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py", line 2075, in print_figure
**kwargs)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_pdf.py", line 2565, in print_pdf
self.figure.draw(renderer)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py", line 50, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py", line 1649, in draw
renderer, self, artists, self.suppressComposite)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/image.py", line 138, in _draw_list_compositing_images
a.draw(renderer)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py", line 50, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py", line 2628, in draw
mimage._draw_list_compositing_images(renderer, self, artists)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/image.py", line 138, in _draw_list_compositing_images
a.draw(renderer)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py", line 50, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py", line 1187, in draw
renderer)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py", line 1125, in _get_tick_bboxes
extent = tick.label1.get_window_extent(renderer)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/text.py", line 930, in get_window_extent
bbox, info, descent = self._get_layout(self._renderer)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/text.py", line 304, in _get_layout
ismath=False)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_pdf.py", line 2126, in get_text_width_height_descent
renderer=self)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/texmanager.py", line 464, in get_text_width_height_descent
dvifile = self.make_dvi(tex, fontsize)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/texmanager.py", line 328, in make_dvi
texfile], tex)
File "/usr/local/lib/python3.6/dist-packages/matplotlib/texmanager.py", line 298, in _run_checked_subprocess
stderr=subprocess.STDOUT)
File "/usr/lib/python3.6/subprocess.py", line 356, in check_output
**kwargs).stdout
File "/usr/lib/python3.6/subprocess.py", line 423, in run
with Popen(*popenargs, **kwargs) as process:
File "/usr/lib/python3.6/subprocess.py", line 729, in init
restore_signals, start_new_session)
File "/usr/lib/python3.6/subprocess.py", line 1364, in _execute_child
raise child_exception_type(errno_num, err_msg, err_filename)
FileNotFoundError: [Errno 2] No such file or directory: 'latex': 'latex'

Files not found error

Thanks for such a fabulous working. I want to run the script but facing error messages. When I try to run data_processor.py file it gives an error message of file not found. creditcard.csv
When I try to run fraud_detection.py file it also gives an error message of file not found. creditcard_genuine_1.csv
Do we have to create these files at our own or these are found at some other place.

Number of hidden and output neurons for different dataset

I have got a dataset of shape (2820520, 15), which has 14 independent features and 1 binary target feature. I'm performing SMOTE instead of undersampling on this QNN model using that particular dataset. What should be the number of neurons in this case? Is there anything that I need to modify for that particular dataset?

[Note: The features are not PCAs like credit card data]

AttributeError: module 'strawberryfields' has no attribute 'Program'

my strawberryfields version is 0.10

TypeError: backend must be a string or a BaseBackend instance.

When I run the function_fitting.py & fraud_detection.py, there was a same error
TypeError: backend must be a string or a BaseBackend instance.
When the code runs to this line
eng, q = sf.Engine(1)

btw, the version of tensorflow and strawberryfields in my environment are 1.3.0 and 0.13.0

Basics