Introduction to Quantum Machine learning course for SGH Warsaw School of Economics students
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.
JupyterLab for qiskit: IBM Quantum computing LAB
Other Quantum environment with jupyterlab: QBraid
Interesting report: Quantum computing report
M. Fingerhuth, T. Babej, P. Wittek. Open source software in quantum computing. PLOS ONE, 13:e0208561, 2018.
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.
S. Raschka. Python Machine Learning. Packt Publishing, 2015.
I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press, 2016.
Quantum Machine Learning
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd. Quantum Machine Learning. Nature, 549, 195-202, 2017.
V. Dunjko and H.J. Briegel. Machine learning and artificial intelligence in the quantum domain. Reports on Progress in Physics, 81, 074001, 2018.
M. Schuld and F. Petruccione. Supervised Learning with Quantum Computers. Springer, 2018.
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] sgh.waw.pl