We are co-organizing a seminar on quantum this Friday by Professor Vedran Dunjko, University of Leiden. Quantum is a novel interdisciplinary research field relevant for our project, as we expect these technologies to have a huge impact in current algorithms and practical applications and, in consequence, in the way human intelligence is complemented my machine intelligence.
The seminar can be followed on streaming.
9:30-11:30 - Part 1: Introduction to Quantum Computing and Quantum Information Processing (QIP)
Quantum computing has been a buzzword in science writing for decades despite being a hypothetical technology. Recent progress in experimental quantum computing, like quantum computing in the cloud, has further increased the excitement around this field. Will quantum computing live up to the hype? This question is unlikely to have an unequivocal answer in the near term.
The objective of this talk is simpler: to provide a sober perspective of the field.
In the first part of this talk, we will explain the basics of quantum computing, clarifying what is known and not known about the differences between quantum and classical computing and discuss the potential and the limitations of these devices.
In the second part, we will reflect on modern trends in quantum computing, including investigations into so-called quantum supremacy, computation with limited quantum devices and quantum machine learning.
12:00-14:30 - Part 2: Quantum Machine Learning (QML):
Interdisciplinary programs which study the exchange between quantum physics and various information sciences have been quite successful over the years. Well known examples are quantum computing and quantum cryptography, which are now mature, thriving research lines. One of the most exciting recent programs of this type is quantum machine learning (QML).
QML research is typically driven by two basic objectives: finding ways in which quantum information processing (QIP) can help with machine learning (ML) problems, and, conversely, understanding the extent to which ML can be beneficially applied in QIP settings.
In this overview talk, we will illustrate the basic ideas of both aspects of the field, and highlight intriguing parallels between the disciplines of QIP and ML. These parallels have been used to argue that these two disciplines are singularly well matched.
Following this, we will showcase some of the most recent results of the field, and also new developments in classical computer science, which put the potential impact of QML to the test.
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