Verification of Quantum Programs
Our long-term objective is to build high-assurance systems. These systems come with independently verifiable analyses of their security and privacy. We develop foundations and tools for high-assurance cryptography, and are working towards providing good support for high-assurance post-quantum and quantum cryptography. This involves developing verification methods for quantum programs.
Prof. Gilles Barthe
MPI for Security and Privacy, Bochum Scientific Director
Quantum Information Theory
The main research focus of Ignacio Cirac and the members of his department is Quantum Information Theory. On the one hand, they investigate new platforms to build quantum computers, quantum communication systems and quantum devices in general. On the other hand, they develop algorithms to solve specific problems with such computers in a more efficient way than with classical ones. They also study protocols that leverage quantum features in different applications. In addition, they are actively involved in the development of a new theory of information based on quantum physics. Finally, they apply concepts from this theory to establish new techniques in order to describe and compute the properties of many-body quantum systems (see our research page for a list of topics).
Prof. Ignacio Cirac
MPI of Quantum Optics, Garching
Artificial Scientist Lab
Our research group investigates how new artificial intelligence (AI) can make conceptual advances in physics, particularly quantum physics and quantum optics.
We are excited about the potential of artificial intelligence-inspired and -augmented science, and how we can use algorithms in a more creative way. We are convinced that the application of AI in science is not a mere technical question but touches the foundations of science. Thus, in order to make progress, it will be important to learn what humans mean by crucial scientific concepts such as surprising, creativity or understanding.
Concretely, we are developing AI-based tools for the design of new quantum experiments and hardware. Beyond the immediate interest in the designs themselves, the main goal is to understand the underlying concepts and ideas discovered by the machine to solve specific questions. We also build autonomously semantic network from scientific publications, and use machine learning to predict and suggest personalized future research questions and ideas. In that sense, we use the machine as a source of inspiration to accelerate scientific progress.
Dr. Mario Krenn
MPI for the Science of Light, Erlangen,
Complexity of Quantum Correlations and Coherence
We study quantum correlations (quantum entanglement & quantum non-locality) and quantum coherence, and the relations between them, as well as quantum uncertainty relations, masking problems, monogamy relations, etc. We also use concepts from information geometry and operator algebras to understand quantum information theory.
Dr. Xianqing Li-Jost
MPI for Mathematics in the Sciences, Leipzig Group Leader
The research focus of our group is to construct cryptographic schemes with new functionalities, prove their security in a rigorous manner, and understanding the mathematical hard problems that underly those systems. Examples include (but are not limited to) schemes for secure computation of quantum circuits, schemes for efficient verification of quantum computations, and classical schemes that remain secure even in the presence of a quantum attacker.
Dr. Giulio Malavolta
MPI for Security and Privacy, Bochum
Quantum Matter, New Kinds of Order,
and Quantum Dynamics
The Condensed Matter Division of MPI-PKS is interested in a broad range of collective phenomena. These can be grouped under three umbrellas of quantum matter, new kinds of order and quantum dynamics, with substantial interconnections naturally continuing to emerge.
A key scope of research is the identification and theoretical description of new kinds of order ranging from unconventional phases such as quantum spin liquids appearing in frustrated magnets to topological matter; this includes non-equilibrium phenomena such as time crystals. Such phases often cannot be described by local order parameters but rather entail peculiar entanglement properties linking condensed matter theory directly to quantum information concepts.
Prof. Roderich Moessner
MPI for the Physics of Complex Systems, Dresden
Machine Learning and Causal Inference
Our main focus is on machine learning and causal inference, with occasional connections to quantum computing and questions of causality in physics. Examples of the latter include work to represent probability distributions in pure quantum states living in reproducing kernel Hilbert spaces, and information-theoretic implications of classical and quantum causal structures. We are also interested in the emerging field of quantum machine learning [see our publication].
Prof. Bernhard Schölkopf
MPI for Intelligent Systems, Tübingen Scientific Director
Quantum Optimization Algorithms for Visual Computing and AI
Our group focuses on research problems at the intersection of computer graphics, computer vision and machine learning. Many research questions in this domain involve challenging correspondence search problems (e.g., optical flow or shape alignment), or the optimization of non-convex objective functions in machine learning. For such problems, we develop new algorithmic formulations which can be solved on modern adiabatic quantum annealers or universal quantum computers of IBM — prototypes of which became recently available for the broader research community — and investigate which advantages these approaches offer in comparison to existing classical methods.
Prof. Christian Theobalt
MPI for Informatics, Saarbrücken