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 ๐Ÿ‘‰ experiments/QA_SVM/QA_SVM.py
+Follow the instructions of the Jupyter Notebook ๐Ÿ‘‰
 
+### Classification with QA-based QSVM (D-Wave QA)
 
+Follow the instructions of the Jupyter Notebook ๐Ÿ‘‰ experiments/QA_SVM/QA_SVM.ipynb
 
-๐Ÿ“ Now you can proceed in two was:
+### Classification with Circuit-based QSVM (IBM Quantum Experience)
 
-(1) Follow the instructions of the Jupyter Notebook ๐Ÿ‘‰ run_SVM.ipynb
+Follow the instructions of the Jupyter Notebook ๐Ÿ‘‰
 
-(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
 
 ๐Ÿ“ฌ 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, โ€œQuantum support vector machine for big data classification,โ€ Physical Review Letters, Sep 2014
 
-D. Willsch, M. Willsch, H. De Raedt and K. Michielsen, โ€œSupport 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