Update README.md

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 👉 experiments/QA_SVM/QA_SVM.py
+Follow the instructions of the Jupyter Notebook 👉
 
+### Classification with QA-based QSVM (D-Wave QA)
 
+Follow the instructions of the Jupyter Notebook 👉 experiments/QA_SVM/QA_SVM.ipynb
 
-📐 Now you can proceed in two was:
+### Classification with Circuit-based QSVM (IBM Quantum Experience)
 
-(1) Follow the instructions of the Jupyter Notebook 👉 run_SVM.ipynb
+Follow the instructions of the Jupyter Notebook 👉
 
-(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
 
 📬 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, “Quantum support vector machine for big data classification,” Physical Review Letters, Sep 2014
 
-D. Willsch, M. Willsch, H. De Raedt and K. Michielsen, “Support Vector Machines on the D-Wave Quantum Annealer”, 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