Quantum Vector Embeddings (QuantumVE)

🔧Install environment

1. Create environment with conda:

conda create -n QuantumVE python=3.10 -y
conda activate QuantumVE

2. Install dependencies

git clone https://github.com/sebasmos/QuantumVE.git
cd QuantumVE
pip install -r requirement.txt

Contribute

1. Folder Focker-Plank-Solution contains the Fokker-Planck which examplifies the one-dimensional Fokker-Planck equation, including parameter inference, numerical spectrum calculation with varied boundary conditions, and numerical simulation on finite domains. Tutorials under Focker-Plank Solution folder where inspired by juliankappler

2. q_Net contains the implementation of audio classification via vector embeddings using qiskit. See Q_Net docs for more details on VE generation.

Papers

1. Machine learning toolbox for quantum many body physics
2. NetKet: A machine learning toolkit for many-body quantum systems
3. Neural-network quantum state tomography - 3d reconstruct the full quantum state from simple measurements
4. Training deep quantum neural networks and repository on Github

CERN

1. Quantum Machine learning 2021 by Maria Schuld : Based on PennyLane

Environments

1. Machine Learning for Many-Body Quantum Systems using netket
2. Netket repository on Github
3. Microsoft Azure - quantum module and repository on Github
4. Quantum - tensorflow and repositry of Github
5. Circ and API on TensorFlow
6. PennyLane

todo's

1. Netket

2. Quantum Tensorflow

3. Get token here

IBM Quantum Computing [Link]

pip install qiskit numpy jupyterlab matplotlib qiskit-ibmq-provider

Notes

Q_Net was sponsored by Google Cloud Research Credits and serves as baseline for Qubico

License

QuantumVE is free and open source, released under GNU GENERAL PUBLIC LICENSE

Contributing to QuantumVE

Feel free to contact me at sebasmos@mit.edu

Feel free to participate in QuantumVE's development! Start by forking the QuantumVE repository, and submit your contributions through a pull request. Every contributor will be recognized as an author in the releases. Noteworthy contributors, especially those introducing new plugins or functionalities, will be featured on the QuantumVE conference website.

We also appreciate bug reports, suggestions for new features, enhancements, and even sharing links to exciting projects or applications built using QuantumVE. Your engagement enriches the QuantumVE community!