https://github.com/xanaduai/constrained-quantum-learning
This repository contains the source code used to produce the results presented in the paper "Near-deterministic production of universal quantum photonic gates enhanced by machine learning".
https://github.com/xanaduai/constrained-quantum-learning
machine-learning optimization photonics quantum quantum-computing quantum-machine-learning quantum-optics
Last synced: 9 months ago
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This repository contains the source code used to produce the results presented in the paper "Near-deterministic production of universal quantum photonic gates enhanced by machine learning".
- Host: GitHub
- URL: https://github.com/xanaduai/constrained-quantum-learning
- Owner: XanaduAI
- License: apache-2.0
- Created: 2018-09-14T05:39:30.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-07-10T14:25:36.000Z (over 6 years ago)
- Last Synced: 2025-06-13T10:08:22.948Z (9 months ago)
- Topics: machine-learning, optimization, photonics, quantum, quantum-computing, quantum-machine-learning, quantum-optics
- Language: Python
- Homepage: https://arxiv.org/abs/1809.04680
- Size: 15.6 KB
- Stars: 22
- Watchers: 6
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Constrained quantum learning
### Using machine learning to train a Gaussian quantum circuit with PNRs to produce cubic phase resource states with high fidelity and probability.
This repository contains the source code used to produce the results presented in *"Near-deterministic production of universal quantum photonic gates enhanced by machine learning"* [arXiv:1809.04680](https://arxiv.org/abs/1809.04680).
## Contents
The following two scripts perform a constrained variational quantum circuit optimization, using both a global search (basin hopping) and a local search (BFGS optimization) to maximize the fidelity (and probability of generating) the cubic phase resource state in the last mode.
* `two_mode.py`: a Python script to generate the results of the two-mode gadget architecture presented in the paper. Here, a two mode squeezed displaced state is incident on a beamsplitter, with the first mode measured by a photon-number resolving detector.
* `three_mode.py`: a Python script to generate the results of the three-mode gadget architecture presented in the paper. Here, a three mode squeezed displaced state is incident on an interferometer consisting of three beamsplitters, with the first and second modes measured by photon-number resolving detectors.
## Requirements
To construct and optimize the constrained variational quantum circuits, these scripts use the Fock backend of [Strawberry Fields](https://github.com/XanaduAI/strawberryfields). In addition, SciPy is required for use of the global Basin Hopping optimization method, as well as the local BFGS optimization method.
## Authors
Krishna Kumar Sabapathy, Haoyu Qi, Josh Izaac, and Christian Weedbrook.
If you are doing any research using this source code and Strawberry Fields, please cite the following two papers:
> Krishna Kumar Sabapathy, Haoyu Qi, Josh Izaac, and Christian Weedbrook. Near-deterministic production of universal quantum photonic gates enhanced by machine learning. arXiv, 2018. [arXiv:1809.04680](https://arxiv.org/abs/1809.04680)
> 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.