Quantum Computing @ UM

Quantum Computing at Maastricht University (QC@UM) is a collaboration between the Department of Advanced Computing Sciences, specialized in Data Sciences and Artificial Intelligence, and Gravitational Waves & Fundamental Physics, specialized in research in gravitational waves and elementary particles.

At Maastricht University, our quantum computing team bridges foundational computer science research with advanced physics applications. Our research bridges quantum computing with high-performance computing, smart data processing, and next-generation physics detectors. We explore Quantum AI, Quantum Algorithms, Quantum Internet, and their transformative potential across scientific domains, driving innovation through interdisciplinary collaboration.

Quantum AI

Quantum Artificial Intelligence is a research field with a twofold framework. The first component focuses on developing AI techniques to advance quantum technologies, while the second leverages quantum computing to enhance AI methodologies. In our group, we explore AI-driven approaches to address key challenges in quantum computing. Our research spans several application domains, including quantum chemistry, condensed matter physics, particle physics, gravitational waves and combinatorial optimization. By integrating these approaches, we aim to push the boundaries of computational intelligence, unlocking new possibilities for scientific discovery, optimization, and complex problem-solving in the quantum era.

Quantum Algorithms and Quantum Internet

We are interested in foundational algorithmic questions about quantum systems. For instance, a critical problem in several disciplines, including chemistry and materials science, is to find the lowest-energy state (the 'ground state') of a given system. Unfortunately, there is strong evidence that this problem is intractable in general, even in the presence of quantum machines, so we are interested in questions like how well it can be approximated for particular classes of system, and what structural properties can be exploited to find (approximate) such states efficiently.

We are also interested in security properties of quantum systems, and algorithmic questions arising therefrom, for instance in how the rich classical theory of 'Quantitative Information Flow' (for computing that amount of information that can flow between interacting participants) can be extended to quantum systems.

A Wafer of the Latest D-Wave Quantum Computers - Steve Jurvetson CC BY 2.0

The Einstein Telescope

Upon its completion in 2035, the Einstein Telescope (ET) will be the most sensitive gravitational wave detector in the world. The Einstein Telescope is set to become 10x more sensitive than its current-generation counterparts, LIGO and Virgo, which should result in an expected 100,000 to 1 million gravitational wave detections of black hole mergers alone, as compared to the current frequency of ~1 per week. In addition, novel exotic types of signals are expected to be found as well.

One of the current standard methods for detecting gravitational waves is a method called ‘matched filtering’, which uses computer-generated templates to compare whether incoming signals match the predicted shape of a gravitational wave. Matched filtering and parameter estimation of the sources will no longer be feasible due to ET's superior sensitivity, necessitating the development of new data analysis techniques.

Digital render of Einstein Telescope by Marco Kraan (Nikhef)

LHCb detector at the High-Luminosity Large Hadron Collider (HL-LHC)

The Large Hadron Collider at CERN in Geneva, Switzerland, is scheduled to be upgraded for superior performance from 2027 and onwards. Its luminosity will increase tenfold, proportionally increasing the number of particle collisions and allowing physicists to study them in greater detail.

The LHCb detector filters out traces of beauty particles by reconstructing the particles' tracks as they pass through the detector. Relevant tracks are filtered out of the data and saved in real-time, requiring us to rethink computing infrastructure as the number of particle collisions quickly becomes too much to handle.