Introduction to Quantum Machine learning course for SGH Warsaw School of Economics students

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Welcome on Introduction to Quantum Machine Learning web page

Until recently, technological development was based on reducing the size of transistors and increasing the computing power of processors. Due to the physical aspects of nature, this process must, from a certain point in, consider the limitations of quantum physics. However, the future may take advantage of other tools beyond classical computing power. Although the construction of quantum computers is still an engineering stage, it is already possible to identify and use them to create algorithms that can be used in machine learning or in optimization problems. Using quantum algorithms reduces the processing time of large amounts of data and thus extends the possibilities of data processing and modelling. The libraries presented during the course - IBM qiskit and Pennylane (python), Yao, Bracket (Julia) - allow for simple and quick construction of any quantum algorithm. These algorithms, such as the VQA or QAOA, can be used for many computational machine-learning problems or for constructing quantum neural networks.


  1. Classical logic gates and computations - Boolian algebra, encryption.
  2. Vector spaces, quantum states, representation of classical and quantum bits.
  3. Quantum gates and quantum circuits for simple quantum algorithms.
  4. Quantum Machine Learning in business - methods and implementations
  5. Free environment for computation on quantum simulators and quantum computers
  6. Quantum bits using the IBM qiskit, pennylane, Yao, Bracket libraries
  7. One-qubit logic gates - true random bit generator
  8. Multi-qubit logic gates
  9. Shor’s factorisation algorithm, Grover search. Python realisations.
  10. Hybrid method for Quantum Machine learning and deep learning.
  11. Variational Quantum Eigensolver and Quantum Annealing
  12. Quantum Approximate Optimisation Algorithm - for max cut problem
  13. Community Detection with QAOA algorithm
  14. Variational quantum classifier, Quantum neural networks

Case studies

Quantum environments with python

JupyterLab for qiskit: IBM Quantum computing LAB

Other Quantum environment with jupyterlab: QBraid

Interesting report: Quantum computing report

Zalecana literatura

  1. L. Susskind, A. Friedeman, Mechanika Kwantowa. Teoretyczne minimum.
  2. E. R. Johnston, N. Harrigan, M. Gimeno-Segovia Komputer kwantowy. Programowanie, algorytmy, kod. O’Reilly (Helion). 2021
  3. M. Nielsen and I. Chuang. Quantum Computation and Quantum Information. Cambridge University Press, 2010.
  4. T.G. Wong. Introduction to Classical and Quantum Computing

Quantum Computing

  1. M. Fingerhuth, T. Babej, P. Wittek. Open source software in quantum computing. PLOS ONE, 13:e0208561, 2018.

  2. J.R. McClean, J. Romero, R. Babbush, A. Aspuru-Guzik. The theory of variational hybrid quantum-classical algorithms. New Journal of Physics, 18, 023023, 2016.

Machine Learning

  1. S. Raschka. Python Machine Learning. Packt Publishing, 2015.

  2. I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press, 2016.

Quantum Machine Learning

  1. J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd. Quantum Machine Learning. Nature, 549, 195-202, 2017.

  2. V. Dunjko and H.J. Briegel. Machine learning and artificial intelligence in the quantum domain. Reports on Progress in Physics, 81, 074001, 2018.

  3. M. Schuld and F. Petruccione. Supervised Learning with Quantum Computers. Springer, 2018.

  4. P. Wittek. Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier, 2014.


SGH Warsaw School of Economics Collegium of Economic Analysis al. Niepodległości 162 02-554 Warsaw, Poland

sebastian.zajac [at]

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