Datalab Seminar – Quantum Databases and Quantum Machine Learning – How Far Can We Go on a Publicly Available Quantum Computer?
Quantum computers use the laws of quantum physics and are potentially much better suited to solve complex problems than classical computers. Google recently even claimed to have reached “quantum supremacy” where their quantum computer performed a certain task in 200 seconds which would require 10,000 years on a state-of-the-art classical supercomputer. IBM responded that with a classical system the same task can be performed in 2.5 days and with far greater fidelity. The race is on.
However, where are we really in the race of quantum computing?
What can we contribute when using a publicly available quantum computer?
In this talk we give an introduction into the fascinating field of quantum computing and show how to program a publicly available quantum computer. We will focus on selected problems from the areas of optimizing database search and machine learning. First, we will discuss how to tackle database searches on a quantum computer using Grover’s algorithm. Afterwards, will show how to approach a linear regression problem with a certain quantum algorithm called HHL (by Harrow, Hassidim and Lloyd). Both quantum algorithms have a significantly better run time complexity than their classical counterparts. However, there are some practical pitfalls that are typically not discussed in the literature. Hence, we describe our practical experience of quantum programming on a quantum simulator as well as on a real quantum computer. Finally, we discuss what real problems we can solve today and where further research is required to make quantum computers solve practical database and machine learning problems.
Raphael Weber studied Industrial Engineering and recently received a bachelor’s degree from Zurich University of Applied Sciences. Together with Mehmet Yesil he wrote his Bachelor’s thesis about linear regression on quantum computers. Previously, Raphael Weber completed an apprenticeship at Zürcher Kantonalbank as a bank officer.
Mehmet Yesil recently finished his Bachelor's degree in Engineering and Management at Zurich University of Applied Sciences. He wrote his bachelor thesis on quantum computing in relation to machine learning algorithm with Raphael Weber. Previously, Mehmet Yesil worked as a design engineer at MAN Diesel und Turbo Schweiz AG (today MAN Energy Solutions), where he had also completed his apprenticeship.
Prof. Dr. Ruedi Füchslin studied theoretical physics at ETH in Zürich. He got his PhD from the University of Zurich, where he wrote his thesis in the newly established group for computer assisted physics. By chance, he had the opportunity to act as a consultant for the Institute for Forensic Medicine. This experience fostered his interest in biological and medical problems. After various post doc positions on the interface between theoretical biology and engineering, he got a position as professor of applied complex systems science at the Zurich University of Applied Sciences. In addition, he is co – director of the European Centre for Living Technology in Venice, Italy and president of the Naturama foundation. Besides research, he has interested in questions relating to the interplay between natural sciences and the humanities.
Prof. Dr. Kurt Stockinger is Professor of Computer Science, Director of Studies in Data Science at Zurich University of Applied Sciences (ZHAW) and Deputy Head of the ZHAW Datalab. His research focuses on Data Science with emphasis on Big Data, Natural Language Query Processing, Query Optimization and Quantum Computing (Quantum Machine Learning). He is also on the Advisory Board of Callista Group AG. Previously Kurt Stockinger worked at Credit Suisse in Zurich, Switzerland, at Lawrence Berkeley National Laboratory in Berkeley, California, at California Institute of Technology, California as well as at CERN in Geneva, Switzerland. He holds a Ph.D. in computer science from CERN / University of Vienna.
Start date: 9 September 2020, 12.00 pm
MS Teams & room t.b.a., if possible