Further details
Participants choose one of two specialized tracks during Days 2–4:
Track 1 (Neural Quantum States): Focuses on representing complex many-body wave functions using neural network architectures, variational Monte Carlo, and ground state searches. No prior NQS knowledge is required!
Track 2 (Reinforcement Learning for Quantum Tech): Covers RL fundamentals, strategies for quantum control optimization, quantum error correction protocols, and quantum circuit dynamics. No prior RL knowledge is required!
Program Schedule & Logistics
Phase 1 (Nov 30, 1:30 PM Kick-off): Introduction to deep learning, neural network architectures, and optimization.
Phase 2 (Dec 1–3): Intensive track-specific lectures and interactive track-oriented hands-on programming tutorials.
Phase 3 (Dec 4, Ends 12:30 PM): Joint scientific session featuring contemporary research talks.
Prerequisites: A basic understanding of quantum mechanics and Python proficiency are expected; no prior machine learning experience is needed.
Equipment Required: Participants must bring their own laptops for the hands-on numerical and coding sessions.
Lecturers
NQS Track
Annabelle Bohrdt (LMU Munich)
Markus Heyl (Univ. of Augsburg)
RL Track
Florian Marquardt (MPL Erlangen)
Marin Bukov (MPI-PKS Dresden)
