Introduction to Quantum Computing, Quantum Machine Learning and Optimization
Tuesday 30 July 2024 -
13:30
Monday 29 July 2024
Tuesday 30 July 2024
13:30
Basics of quantum computing (theory)
-
Ema Puljak
(
Universitat Autonoma de Barcelona (ES)
)
Basics of quantum computing (theory)
Ema Puljak
(
Universitat Autonoma de Barcelona (ES)
)
13:30 - 14:30
Room: 31/3-004 - IT Amphitheatre
<h2>Abstract</h2> In this basic introduction to quantum computing, the underlying mathematical concepts will be presented together with quantum mechanical phenoma of interest such as superposition and entanglement in order to enable the understanding of basic quantum circuits. <h2>Bio</h2> Ema is a computer scientist pursuing PhD in physics at CERN and Universitat Autonoma de Barcelona, specializing in quantum and quantum-inspired algorithms for anomaly detection with applications in high-energy physics and medical imaging.
14:30
Quantum computing hands-on
-
Giulio Crognaletti
Quantum computing hands-on
Giulio Crognaletti
14:30 - 15:30
Room: 31/3-004 - IT Amphitheatre
<h2>Abstract</h2> In this hands-on session a general introduction to the Pennylane python framework for quantum computing will be given, with particular focus on the practical implementation and simulation of basic quantum circuits. <h2>Bio</h2> Giulio is a PhD student in Physics at the University of Trieste, specializing in quantum computing, with a particular interest in the trainability of variational quantum algorithms and quantum machine learning.
15:30
Quantum Optimization and Quantum Machine Learning
-
Carla Sophie Rieger
(
Technische Universitat Munchen (DE)
)
Quantum Optimization and Quantum Machine Learning
Carla Sophie Rieger
(
Technische Universitat Munchen (DE)
)
15:30 - 16:30
Room: 31/3-004 - IT Amphitheatre
<h2>Abstract</h2> This talk introduces the fundamental concepts of quantum machine learning (QML). In the realm of parametrised quantum circuits, embedding of classical data and parameter optimization methods as part of the general data processing pipeline for quantum networks are being discussed. Furthermore, possible advantages and challenges in the QML domain are considered and the presentation concludes with examples of CERN specific use-cases. <h2>Bio</h2> Carla is a theoretical physicist specializing in quantum computing and quantum algorithms. With a master's degree from ETH Zurich, Carla is currently pursuing a Ph.D. at CERN with TUM, focusing on quantum algorithms for combinatorial problems and efficient classical simulability of quantum circuits