GitHub - Shesh6/Quantum-Machine-Learning: A Quantum Computing MOOC from University of Toronto focusing on Machine Learning applications, taken in summer 2019. Coded in Python using IBM's qiskit library and D-Wave's ocean library.

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
.gitattributes		.gitattributes
01 - Classical and Quantum Probability Distributions.ipynb		01 - Classical and Quantum Probability Distributions.ipynb
02 - Measurements and Mixed_States.ipynb		02 - Measurements and Mixed_States.ipynb
03 - Evolution in Closed and Open Systems.ipynb		03 - Evolution in Closed and Open Systems.ipynb
04 - Classical and Quantum Many-Body Physics.ipynb		04 - Classical and Quantum Many-Body Physics.ipynb
05 - Gate-Model Quantum Computing.ipynb		05 - Gate-Model Quantum Computing.ipynb
06 - Adiabatic Quantum Computing.ipynb		06 - Adiabatic Quantum Computing.ipynb
07 - Variational Circuits.ipynb		07 - Variational Circuits.ipynb
08 - Sampling a Thermal State.ipynb		08 - Sampling a Thermal State.ipynb
09 - Discrete Optimization and Ensemble Learning.ipynb		09 - Discrete Optimization and Ensemble Learning.ipynb
10 - Discrete Optimization and Unsupervised Learning.ipynb		10 - Discrete Optimization and Unsupervised Learning.ipynb

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A Quantum Computing MOOC from University of Toronto focusing on Machine Learning applications, taken in summer 2019. Coded in Python using IBM's qiskit library and D-Wave's ocean library.