https://github.com/iitis/qcontrol_lstm_approx
Approximation of quantum control using LSTM
https://github.com/iitis/qcontrol_lstm_approx
control-pulses lstm quantum-control
Last synced: about 1 year ago
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Approximation of quantum control using LSTM
- Host: GitHub
- URL: https://github.com/iitis/qcontrol_lstm_approx
- Owner: iitis
- License: mit
- Created: 2018-04-10T07:22:42.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2020-09-08T13:19:07.000Z (almost 6 years ago)
- Last Synced: 2025-03-29T11:34:28.665Z (about 1 year ago)
- Topics: control-pulses, lstm, quantum-control
- Language: Jupyter Notebook
- Homepage:
- Size: 169 KB
- Stars: 7
- Watchers: 5
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# qcontrol_lstm_approx
# Steps required to execute the code
* Run `generate_data.py` to generate random unitary matrices and control pulses
for learning
* Set `testing_effectiveness` to `True` in the argument of the `main()` function.
* Run `lstm_as_approximation.py` to use generated matrices to train the network and test its efficiency
* Set `testing_effectiveness` to `False` in the argument of the `main()` function.
* Run `lstm_as_approximation.py` to use the trained network for testing the effect of local disturbances.
* Run `printing_results.ipynb` to plot results of the experiments.
# Description of file
* `generate_data.py` - generate data and create directory structure
* `get_data.py` - functions for loading data from files
* `configuratons/` - this is folder with control panels, with all needed parameters of experiments.
* `architecture.py` - LSTM architecture and cost functions
* `noise_models_and_integration.py` - models of quantum systems and related
functions
* `lstm_as_approximation.py` - the main file with experiments.
* `printing_results.ipynb` - file divided into blocks in which we can plot results of experiment.
# Requirements
The code has been tested with Python 3.6 distributed with Anaconda. The packages
utilizes QuTIP is based on TensorFlow library. It also utilizes QuTip for generating
control pulses.
# Citing
M. Ostaszewski, J.A. Miszczak, P. Sadowski, L. Banchi, *Approximation of quantum control correction scheme using deep neural networks*, https://arxiv.org/abs/1803.05193, Quantum Inf Process (2019), 18:126, https://doi.org/10.1007/s11128-019-2240-7