QAutoencoder for Solving Combinatorial Optimization Problems with Constraints

In this work, we are aiming to create a novel approach for solving constraint satisfaction problems, by making use of the latent state representations for the data.

@TUM Faculty of Informatics, Thesis- 2021WS- 2022SS, Burak Mete, Irene Lopez Gutierrez, Prof. Dr. Christian Mendl

Name		Name	Last commit message	Last commit date
Latest commit History 82 Commits
PennyLane		PennyLane
QAOA		QAOA
QAutoencoder		QAutoencoder
StabilizerCode		StabilizerCode
Test		Test
Visualization		Visualization
__pycache__		__pycache__
tests		tests
.gitignore		.gitignore
Ansatz.py		Ansatz.py
DataCreation.py		DataCreation.py
Guided_Research_Report_Burak_Mete.pdf		Guided_Research_Report_Burak_Mete.pdf
HamiltonianUtils.py		HamiltonianUtils.py
QAOA-E.py		QAOA-E.py
QAOA_with_encoding.py		QAOA_with_encoding.py
README.md		README.md
encoder.py		encoder.py
main.py		main.py
pennylaneQAOA_with_encoder.py		pennylaneQAOA_with_encoder.py
utils.py		utils.py

Bmete7/QuantumNeuralNet

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QAutoencoder for Solving Combinatorial Optimization Problems with Constraints

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