intro_to_qml

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


Project maintained by sebkaz-teaching Hosted on GitHub Pages — Theme by mattgraham

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, take into account the limitations of quantum physics. The future, however, may take advantage of other tools that go beyond classical computing power. Although the construction of quantum computers is still an engineering stage, it turns out that it is already possible to identify and use them to create algorithms that can be used in the field of machine learning. The use of quantum algorithms allows to reduce 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) - allow for a 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 the construction of quantum neural networks.

Spis tematów:

  1. Matematyczne podstawy - Liczby zespolone, Przestrzenie wektorowe, reprezentacja kwantowego bitu
  2. Quantum computing - relaizacje i sposoby + info historyczne
  3. KLasyczne bramki logiczne - algebra Boola, szyfrowanie XOR
  4. Python Libraries for QC - PennyLane, IBM qiskit
  5. Bramki jedno i wielo qubitowe

Case studies:

Środowisko Jupyter Lab: IBM Quantum computing LAB

Inne środowisko Jupyter Lab z wszystkimi kwantowymi maszynami: QBraid

To do ciekawych stron: Quantum computing report

Contact

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

sebastian.zajac [at] sgh.waw.pl

My Web page