{"id":20669499,"url":"https://github.com/iitis/qcontrol_lstm_approx","last_synced_at":"2025-04-19T18:12:54.876Z","repository":{"id":73908654,"uuid":"128891268","full_name":"iitis/qcontrol_lstm_approx","owner":"iitis","description":"Approximation of quantum control using LSTM","archived":false,"fork":false,"pushed_at":"2020-09-08T13:19:07.000Z","size":173,"stargazers_count":7,"open_issues_count":0,"forks_count":4,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-03-29T11:34:28.665Z","etag":null,"topics":["control-pulses","lstm","quantum-control"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/iitis.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2018-04-10T07:22:42.000Z","updated_at":"2023-02-02T13:54:43.000Z","dependencies_parsed_at":null,"dependency_job_id":"c6b94da9-97fc-4a85-950e-bc1820760135","html_url":"https://github.com/iitis/qcontrol_lstm_approx","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iitis%2Fqcontrol_lstm_approx","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iitis%2Fqcontrol_lstm_approx/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iitis%2Fqcontrol_lstm_approx/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iitis%2Fqcontrol_lstm_approx/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/iitis","download_url":"https://codeload.github.com/iitis/qcontrol_lstm_approx/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249760135,"owners_count":21321843,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["control-pulses","lstm","quantum-control"],"created_at":"2024-11-16T20:14:35.113Z","updated_at":"2025-04-19T18:12:54.863Z","avatar_url":"https://github.com/iitis.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# qcontrol_lstm_approx\n\n# Steps required to execute the code\n\n* Run `generate_data.py` to generate random unitary matrices and control pulses\n  for learning\n* Set `testing_effectiveness` to `True` in the argument of the `main()` function.\n* Run `lstm_as_approximation.py` to use generated matrices to train the network and test its efficiency \n* Set `testing_effectiveness` to `False` in the argument of the `main()` function.\n* Run `lstm_as_approximation.py` to use the trained network for testing the effect of local disturbances.\n* Run `printing_results.ipynb` to plot results of the experiments.\n\n# Description of file\n\n* `generate_data.py` - generate data and create directory structure\n* `get_data.py` - functions for loading data from files\n* `configuratons/` - this is folder with control panels, with all needed parameters of experiments.\n* `architecture.py` - LSTM architecture and cost functions\n* `noise_models_and_integration.py` - models of quantum systems and related\n  functions\n* `lstm_as_approximation.py` - the main file with experiments.\n* `printing_results.ipynb` - file divided into blocks in which we can plot results of experiment.\n\n# Requirements\n\nThe code has been tested with Python 3.6 distributed with Anaconda. The packages\nutilizes QuTIP is based on TensorFlow library. It also utilizes QuTip for generating\ncontrol pulses.\n\n# Citing\n\nM. 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\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiitis%2Fqcontrol_lstm_approx","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fiitis%2Fqcontrol_lstm_approx","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiitis%2Fqcontrol_lstm_approx/lists"}