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 and Diffusion models 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
Optimizing PyTorch AI Models with GPU Profiling and Triton Kernels
Presenters: Konrad Klimaszewski and Michał Obara (NCBJ)
This training provides a practical introduction to optimizing PyTorch-based AI models on GPUs, with a strong focus on performance profiling and custom Triton kernels. The aim is to equip attendees with the skills needed to identify performance bottlenecks and accelerate GPU-based computations. By the end of the training, attendees will be able to:
- manage CPU–GPU memory transfers and reason about performance,
- profile GPU code and interpret traces to spot bottlenecks,
- understand the motivation and principles behind writing custom GPU kernels,
- write simple custom kernels in Triton and integrate them into PyTorch workflows,
- compare custom kernel performance against built-in PyTorch operations.
Target audience: Users who already work with PyTorch and want to accelerate and optimize their GPU-based numerical or AI computations.
Requirements: Working knowledge of PyTorch tensors and basic GPU concepts; Python proficiency; familiarity with undergraduate-level linear algebra.
Required tools: Bring your own laptop — a remote Jupyter Notebook environment with everything needed will be provided.
Introduction to Quantum Computing
Presenters: Experts from Poznań Supercomputing and Networking Center (PSNC)
This training provides an accessible introduction to quantum computing, covering both photonic and gate-based (universal) systems. Through hands-on sessions and live demonstrations, attendees will explore the current state and future potential of quantum technology. By the end of the training, attendees will be able to:
- understand the basics of quantum computing,
- explain the differences between photonic and universal (gate-based) quantum systems,
- build simple quantum circuits and create quantum AI models,
- discover advanced uses of quantum technology.
Target audience: Anyone curious about quantum computing, regardless of prior experience.
Requirements: Basic computer knowledge and familiarity with any programming language.
Required tools: Bring your own laptop — a remote Jupyter Notebook environment with everything needed will be provided.
