Dr. Vyacheslav Kungurtsev, Ph.D.

It is well known that what we eat can affect our health – that people with lower quality diets are more likely to develop diseases like heart disease, diabetes, and obesity. However, there is remarkably little known about the crucial steps in between – the changes and processes that take place within our bodies as a result of what we eat that actually lead to us to developing these diseases. In this project, we develop causal models of metabolism. In terms of methods, this draws on a history of work on learning dynamical systems. In terms of inputs, this draws on the information we gather from existing research, the information we get from new ways to monitor diets, as well as other information that often isn’t considered, such as genetics, metabolism, and gut bacteria, to improve our understanding of this pathway and discover new indicators of disease risk. The project will also use AI to help us analyse this information and to connect the dots that humans usually wouldn’t be able to find.

Machine Learning and AI techniques have increasingly been broadening their scope of applicability, and particularly exciting in the present day is the use of data to perform simulation, identification and broad expansion of understanding of physical processes. Using careful choices of classical learning and statistical techniques, neural networks, and Bayesian approaches, large quantities of data can be leveraged to discover profound and useful scientific insights, and learn the uncertainty profile of a physical process in order for accurate and robust forecasting and control and management.

Multiple novel applications arises in quantum technologies. Notably, quantum optimal control is behind many recent advances in science and technology, where one shapes a laser or microwave pulse, so as to optimise a functional of the states produced. In biology, quantum optimal control allows nuclear magnetic resonance (NMR) spectroscopy to study large biomolecules in solution. In chemistry, quantum optimal control in laser spectroscopy brings fundamental insights into reaction dynamics; laser control directs chemical reactions to a desired target or even enables a design of new chemical species and materials. In neurology and neurosciences, quantum optimal control provides radio-frequency pulses yielding higher resolution in functional magnetic resonance imaging (fMRI), and hence better diagnoses with less time spent in the scanner. In photonics and metrology, interferometers utilise quantum optimal control as a means of designing semi-classical probes. In quantum computing, better quantum optimal control provides faster and more accurate two-qubit gates, and multi-level operations in general. We have recently shown that quantum optimal control can be formulated as a commutative (arXiv:2209.05790) or non-commutative (arXiv:2001.06464) polynomial optimization problem. To take full advantage of quantum systems, e.g., within quantum optimal control, we need to learn a model of the quantum system. Although the identification of the Hamiltonian of an open or closed quantum system is a very natural problem, progress has been hindered by the interdisciplinary nature of the problem. Indeed, it requires nontrivial Computer Science and Statistics (statistical learning theory, system identification), Mathematics (algebraic geometry andnonconvex optimization in the form of non-commutative polynomial optimization), and Physics (quantum information theory), in order to extend the well-established results of system identification from classical to quantum systems. We have recently made some progress in this direction (arXiv:2203.17164). There is funding available from the Czech Science Foundation (GACR) under award number GA23-07947S (Learning Models of Quantum Systems as a Non-Commutative Polynomial Optimization Problem) and from the European Commission under grant agreement number 101120296 (HORIZON-MSCA-2022-DN-01 Tensor modEliNg, geOmetRy and optimiSation). This topic is supervised by Jakub Marecek and Vyacheslav Kungurtsev (Dept. of Computer Science), in close collaboration with Milan Korda and Didier Henrion (Dept. of Control Engineering; both co-PIs on the project funded in HORIZON-MSCA-2022-DN-01) and their research team in Toulouse, France (LAAS CNRS is a beneficiary of the project funded in HORIZON-MSCA-2022-DN-01) and Georgios Korpas and his research team in London, UK (HSBC is an associate partner for the project funded in HORIZON-MSCA-2022-DN-01).