Exploring quantum algorithms, simulation, and AI links for sustainable systems
The Quantum Computing & AI group investigates where quantum methods can complement classical analytics for sustainability. We connect fundamentals (states, gates, measurement) with applications (optimization, simulation, machine learning) and emphasize clear, reproducible experiments using open tools.
Mentorship: Enis Yazıcı — contact: enis.yazici@srh.de.
Aisha Toichieva (University of Central Asia) is a three‑month exchange student at SRH Heidelberg, staying through December 2025. She is developing a hands‑on, simulation‑based training module to promote quantum computing within our community, focusing on beginner‑friendly circuit building, small variational workflows, and simple optimization demos that can be reproduced in class notebooks.
Gergana Kehayova (Heidelberg University, Physics) contributes to the educational content on introductory quantum computing. She focuses on explaining the basic concepts — such as qubits, superposition, and simple circuit logic — and helps prepare clear, beginner-level learning materials for students interested in the topic.
Our work links to other SustAInability streams: AI & Data for Climate · Physics for a Sustainable Future · Heidelberg Summer School 2025
We welcome motivated students across partner universities. Bring a problem idea (optimization, modeling, or education) and we will prototype it together with clear benchmarks and documentation.