Topics and scope
The scope of the workshop covers among others quantum simulations, quantum algorithms, and (classical or quantum) machine learning algorithms with a focus on application in Physics and Medicine.
List of topics:
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machine learning methods in medical applications,
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machine learning methods in high-energy physics and astrophysics,
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quantum machine learning,
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Generative Adversarial Networks for fast simulations both in medicine and particle physics,
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quantum simulations,
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quantum and quantum-inspired computing algorithms,
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novel methods in medical imaging,
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High-Performance Computing (HPC) in physics and medicine, in particular on heterogeneous platforms such as FPGA and GPU.
Training hands-on sessions
Quantum Computing Hardware with QGates
Presenters: Hans-Georg Stöhr and Beatrix C. Hiesmayr
In this workshop, a modular hardware will be presented that enables an introduction to the basic algorithms of quantum computing such as the Shor algorithm (factorization of 15 and 21), the Grover algorithm, the quantum K-means algorithm, etc. The low-cost hardware is based on microcontrollers and enables exact quantum simulations of quantum circuits with up to 8 qubits with so-called "QGates". The modular design consisting of several identical boards enables the complexity to be cascaded and thus a didactic introduction to the complexity and challenges of the upcoming quantum computers.
EuroCC Tutorial on Using Large Language Models (LLM) for Private Data
The tutorial will explore the possibilities of utilizing LLMs for interacting with private data. We will introduce tools that enable harnessing the power of generative AI in scenarios where no data can leave your execution environment at any point. We explore the architecture and data requirements for creating your private ChatGPT, leveraging semantic understanding while maintaining control over your data.
Accelerating your computing with the LUMI European supercomputer
This one-day workshop aims at presenting how to run common machine learning task on a large supercomputer system. It covers setting up an environment for AI workflows combining common high performance computing tools with popular machine learning frameworks. The setup leverage multi-GPU acceleration and scaling across multiple computing nodes. On top of the computing environment runs the Megatron-DeepSpeed framework to experiment with pre-trained large language model for typical prompt engineering tasks.
Warning! The LUMI tutorial will be held on the 3rd of June, one day before the beginning of the workshop. Attending this tutorial requires additional registration on the following webpage: https://events.plgrid.pl/e/lumi2024.
