AlphaTensor-Quantum

This repository accompanies the paper

Ruiz, F. J. R. et al. Quantum Circuit Optimization with AlphaTensor. Nature Machine Intelligence (2024).

There are two directories:

• /src contains the code developed for this project pertaining to the reinforcement learning algorithm, along with a subdirectory /src/demo containing a runnable demo that connects this code with a simplified version of AlphaTensor, built using the MCTX package.

• /decompositions contains the outputs of AlphaTensor-Quantum reported in the paper in the form of tensor decompositions, along with a Colab showing how to load and inspect them. Decompositions are stored in .npz files containing a Python dictionary mapping from the circuit name (a string) to a boolean Numpy array of shape (num_decompositions, num_factors, tensor_size), where we report at most num_decompositions = 10 non-equivalent decompositions (equivalence is determined solely based on factor permutations).

Installation

• /src: No installation required. This folder does not contain runnable code.

• /src/demo: A machine with Python 3 installed is required, ideally with a hardware accelerator such as a GPU or TPU. The required dependencies (assuming an Nvidia GPU is available) can be installed by executing pip3 install -r alphatensor_quantum/src/demo/requirements.txt (this has been tested with Python 3.11.9 on an Nvidia Quadro P1000 GPU).

• /decompositions: No installation required. The provided notebook can be opened and run in Google Colab.

Usage

• /src: This folder does not contain runnable code.

• /src/demo: Execute python3 -m alphatensor_quantum.src.demo.run_demo on the command line, from the parent directory that contains the alphatensor_quantum repository as a subdirectory.

• /decompositions: The notebook load_decompositions.ipynb can be opened via . When running the notebook, you will be asked to upload certain files containing the decompositions; please select the appropriate .npz file from the folder.

Citing this work

If you use the code or data in this package, please cite:

@article{alphatensor_quantum,
      author={Ruiz, Francisco J. R. and Laakkonen, Tuomas and Bausch, Johannes and Balog, Matej and Barekatain, Mohammadamin and Heras, Francisco J. H. and Novikov, Alexander and Fitzpatrick, Nathan and Romera-Paredes, Bernardino and van de Wetering, John and Fawzi, Alhussein and Meichanetzidis, Konstantinos and Kohli, Pushmeet},
      title={Quantum Circuit Optimization with {A}lpha{T}ensor},
      journal = {Nature Machine Intelligence (Under Review)},
      year={2024},
}

License and disclaimer

Copyright 2024 DeepMind Technologies Limited

All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you may not use this file except in compliance with the Apache 2.0 license. You may obtain a copy of the Apache 2.0 license at: https://www.apache.org/licenses/LICENSE-2.0

All other materials are licensed under the Creative Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of the CC-BY license at: https://creativecommons.org/licenses/by/4.0/legalcode

Unless required by applicable law or agreed to in writing, all software and materials distributed here under the Apache 2.0 or CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.

This is not an official Google product.