PhIQuS

Quantum Information Theory

Our research focuses on exploring the novel opportunities that quantum systems present for information processing, with a particular emphasis on quantum networks and distributed computing. Our work spans a broad spectrum of topics within quantum information theory and computer science. We investigate foundational differences between classical and quantum information theory, identify elementary tasks that highlight these differences, and develop specific protocols to implement these tasks in collaboration with experimental groups.

Key areas of our research include quantum correlations and distributed computing in networks, the foundations of quantum theory, certification of quantum devices, quantum optics, and many-body physics. We approach these challenges from multiple perspectives, utilizing concepts and methods from quantum information theory, theoretical computer science, and the mathematical frameworks of C*-algebras and non-commutative polynomial optimization.

Machine Learning for Quantum Sciences

Our understanding of several fundamental mechanisms underpinning physical materials, and the technological developments of Quantum Devices are constrained by the boundaries of what our current analytical and numerical tools can tackle. As the fields of Machine Learning and Data Science have developed several tools to tackle the curse of dimensionality, they offered a prime ground for novel ideas to be applied to computational quantum physics.

Our aspiration is to develop innovative algorithms that can address fundamental open questions in quantum physics, with a clear focus on technologically and societally relevant problems. Our approach is to recast physical questions into optimization problems of various sort, potentially relying on deep neural network approximations and Monte-Carlo integration to make those algorithms computationally tractable. We are also very interested in the connections between machine learning, quantum information theory and statistical mechanics.

Categorical and Graphical Frameworks for Quantum Computing

We are currently investigating several topics, including the connections between tensor networks and symbolic dynamics, promonads and programming, combinatorial interpretations of ZW-calculus (such as matchgates and perfect matchings), graphical languages for infinite-dimensional quantum mechanics, and the foundations of graphical languages within the prop formalism. We also explore the categorical approach to relational quantum mechanics, aiming to deepen the understanding of these frameworks.