RESEARCH

We study models for quantum information processing and fundamental aspects of quantum information theory. One focus of our research is the theory of measurement-based quantum computation, which has resulted in a new and more thorough understanding of many-body entanglement as resource, and applications in quantum communication, quantum error correction, and quantum algorithms. Another focus lies on the application and development of machine learning in basic science and on the study of physical models for learning. Some of our work is highly interdisciplinary and addresses questions in different fields including quantum physics, robotics, behavioural biology, and the philosophy of action.

AI and Science

Quantum Computation

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News & Activities

New pre-print: Learning to reset in target search problems
In this pre-print we introduce a reinforcement learning framework for optimizing stochastic search processes via adaptive resetting, uncovering interpretable strategies that surpass existing benchmarks.

Prof. Briegel receives the Schrödinger Prized from the ÖAW
Hans Briegel has received this important prize in recognition for his "research achievements in the field of quantum information and quantum computing (QIC) [...] and the development of physical models for autonomous and capable agents."

FFG grant awarded to our group
Lukas Fiderer and Hendrik Poulsen Nautrup from our group, together with M. Ringbauer and P. Schindler (UIBK), N. Friis (TU Vienna), and R. Küng (JKU Linz) won an FFG Grant "Protecting Quantum Information through high-dimensional Encoding''.

New publication: Measurement-based quantum computation from Clifford quantum cellular automata
This paper, published in Phys. Rew. A, explores the connection between MBQC and Clifford quantum cellular automata and its ability to create adaptive, hardware-efficient quantum ansätze.

New publication: Variational measurement-based quantum computation for generative modeling
In this paper we propose variational MBQC algorithms that harness inherent measurement randomness as a resource, enhancing expressivity and performance in quantum generative modeling.

Florian Fürruter has received the Best Poster award at the 2nd Workshop of Machine Learning for Quantum Technology
He received this award for his poster "Quantum circuit synthesis with diffusion models"