diff --git a/README.md b/README.md index e961f06b2e1c027735d68a2e176bf912acf527d3..e12264dbe1ae369b60046c4bde0ee6f1f11e5eed 100644 --- a/README.md +++ b/README.md @@ -53,39 +53,27 @@ Test set: - X_test_tile_8_subregion.npy - Y_test_tile_8_subregion.npy -### Classification with SVM on QA +### Classification with classical SVM (Scikit-Learn) -Follow the instructions of the Jupyter Notebook ๐ experiments/QA_SVM/QA_SVM.py +Follow the instructions of the Jupyter Notebook ๐ +### Classification with QA-based QSVM (D-Wave QA) +Follow the instructions of the Jupyter Notebook ๐ experiments/QA_SVM/QA_SVM.ipynb -๐ Now you can proceed in two was: +### Classification with Circuit-based QSVM (IBM Quantum Experience) -(1) Follow the instructions of the Jupyter Notebook ๐ run_SVM.ipynb +Follow the instructions of the Jupyter Notebook ๐ -(2) Make your processing pipeline by using the Python functions: calibrate.py, train.py and test.py. - (See in the instructions in files) - -Have fun! +## Support ๐ฌ For any problem, feel free to contact me at g.cavallaro@fz-juelich.de +## Additional Bibliography and Sources - - - - - - -# Quantum_SVM_Algorithms - -## Extended Bibliography - P. Rebentrost, M. Mohseni, and S. Lloyd, โQuantum support vector machine for big data classification,โ Physical Review Letters, Sep 2014 -D. Willsch, M. Willsch, H. De Raedt and K. Michielsen, โSupport Vector Machines on the D-Wave Quantum Annealerโ, 2019 - D. Anguita, S. Ridella, F. Rivieccio, R. Zunino, "Quantum optimization for training support vector machines", Neural Networks, 2003 "Implementing QSVM Machine Learning Method on IBM's Quantum Computers", Quantum Computing UK, 2020