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Proposal : Quantum Autoencoder #280
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* add autoencoder based on qiskit-ml example: https://qiskit.org/ecosystem/machine-learning/tutorials/12_quantum_autoencoder.html turned to be compatible with scikit-learn * set up example with autoencoder. Works with qiskit ml 0.7.1 * add dataset from https://zenodo.org/records/5055046 (partially implemented) * change localisation of the dataset. adapt exapmle to use the ergonomic dataset * apply denoising on raw signal * reduce epoch dimension use cross session evaluation * update pipeline * update optimizer * add rs and fix some bugs * add epochselectchannel * doc * improve code for channel selection * expose qaoa optimizer * push example * [pre-commit.ci] auto fixes from pre-commit.com hooks * typo in example * fix Attributes section duplicated * use moabb Hinss2021 * autoencoder.py Co-authored-by: Yash Chauhan <116367139+jiggychauhi@users.noreply.github.com> Co-authored-by: Ahmed Mostafa <121709141+AhmedMostafa167@users.noreply.github.com> * refactor autoencoder, add documentation * Complete documentation * extract devectorizer to preprocessing.py WIP * documentation for devectorizer * complete doc for EpochSelectChannel * refactor qword example. Do not use xDawn. * fix bug with spatial filter * add callback parameter * [pre-commit.ci] auto fixes from pre-commit.com hooks * typo * flake8 * [pre-commit.ci] auto fixes from pre-commit.com hooks * typo * flake8 * [pre-commit.ci] auto fixes from pre-commit.com hooks --------- Co-authored-by: Gregoire Cattan <gregoire.cattan@ibm.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Yash Chauhan <116367139+jiggychauhi@users.noreply.github.com> Co-authored-by: Ahmed Mostafa <121709141+AhmedMostafa167@users.noreply.github.com>
qbarthelemy
requested changes
May 23, 2024
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
qbarthelemy
approved these changes
May 24, 2024
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Minor comments. Thx @gcattan !
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com>
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This is a quantum autoencoder, that we transformed into a sklearn transformer.
The example illustrates a possible pipeline with RG.
This is an early research, there are no positive results, and there is a lot of parameters to consider (type of the optimizer, latent space/trash space, component selections, etc.).
Yet, I found it is well in the scope of this library.