As part of the Seminar: Advanced Topics in Quantum Computing at TUM, I experiment with the implementation of Quantum Support Vector Machines.
The goal of this Quantum Machine Learning Project is to successfully
- implement a classical machine learning algorithm (the Support Vector Machine)
- implement a quantum algorithm (Quantum Kernel Estimation)
- integrate them into a Quantum Machine Learning Algorithm (Quantum Support Vector Classifier)
The project is written in Python and uses the qiskit library as the quantum circuit simulator for the implementation of the quantum kernel estimation. The quadratic programming problem of the classical algorithm is implemented using the cvxopt solver.
The source code is located in the package folder quantum_svm 🗄️.
The notebooks display some benchmarking against commonly used libraries, such as scikit-learn for Support Vector Classification and qiskit for quantum algorithms.
For further information, the accompanying slides with the theory and some references can be found in the Slides 🗄️ folder.