Talks

Self-organisation of proteins in cells

Protein pattern formation is essential for the spatial organisation of intracellular processes like cell division and flagellum positioning. A prominent example of intracellular patterns is the oscillatory pole-to-pole oscillations of Min proteins in E. coli, whose function is to ensure precise cell division. Cell polarisation, a prerequisite for processes such as stem cell differentiation and cell polarity in yeast, is also mediated by a diffusion-reaction process. More generally, these functional modules of cells serve as model systems for self-organisation, one of the core principles of life. Under which conditions spatiotemporal patterns emerge and how biochemical and geometrical factors regulate these patterns are major aspects of current research. In this talk, I will review recent theoretical and experimental advances in the field of intracellular pattern formation, focusing on general design principles and fundamental physical mechanisms.

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QDS: Building a hierarchy of methods for nonadiabatic molecular dynamics based on the multiple-spawning framework

Different theoretical strategies have been developed to simulate the photochemistry of molecules in their full dimensionality, incorporating nonadiabatic (non-Born-Oppenheimer) effects. Two examples of such methods include ab initio multiple spawning (AIMS) and trajectory surface hopping (TSH). AIMS describes the dynamics of nuclear wavepackets using adaptive linear combinations of traveling frozen Gaussians. TSH portrays the nuclear dynamics with a swarm of independent classical trajectories that can hop between potential energy surfaces. In this talk, I intend to survey some of our recent work on developing a hierarchy of methods for excited-state dynamics based on the AIMS framework. I will also spotlight challenges in the field of nonadiabatic molecular dynamics that were identified when simulating time-resolved experimental observables.

Insulating Magnetic Rare-Earth Pyrochlores: Frustrated Magnets for Every Taste

IMPRS Seminar: Topology in new settings

Topology is the study of quantities that do not change under smooth deformations. In quantum systems, topology gives rise to a variety of phenomena, including chiral edge states and quasi-particles that are neither fermions, nor bosons, but anyons. Topology has traditionally been studied primarily in crystalline systems, but motivated in part by new experimental possibilities, there is a growing interest in studying topology in different settings. Here, we will discuss topology in quantum systems on non-periodic graphs and in systems of finite size. As examples, we will show how the number of topological edge-like states can be greatly increased by considering models on a fractal graph, and we will discuss which measures of topology are most suitable for fractals. We will also show how localized, topological states can appear in the bulk of quasicrystals. Finally, we will show that systems that are long in one direction and narrow in another can simultaneous show topology normally associated with one-dimensional and two-dimensional systems.

IMPRS Seminar: Topological skyrmion semimetals

We introduce topological skyrmion semimetal phases of matter, characterized by bulk electronic structures with topological defects in ground state observable textures over the Brillouin zone (BZ), rather than topological degeneracies in band structures. We present and characterize toy models for these novel topological phases, focusing on realizing such topological defects in the ground state spin expectation value texture over the BZ. We find generalized Fermi arc bulk-boundary correspondences and chiral anomaly response signatures, including Fermi arc-like states which do not terminate with topological band structure degeneracies in the bulk, but rather with topological defects in the spin texture of bulk insulators. We also consider novel boundary conditions for topological semimetals, in which the 3D bulk is mapped to a 2D bulk plus 0D defect. Given the experimental significance of topological semimetals, our work paves the way to broad experimental study of topological skyrmion phases and the quantum skyrmion Hall effect.

Boundaries, Inclusions, and Disorder in Active Matter

Active systems are driven out of equilibrium by exchanging energy and momentum with their environment. This endows them with anomalous mechanical properties which leads to rich phenomena when active fluids are in contact with boundaries, inclusions, or disordered potentials. Indeed, studies of the mechanical pressure of active fluids and of the dynamics of passive tracers have shown that active systems impact their environment in non-trivial ways, for example, by propelling and rotating anisotropic inclusions. Conversely, the long-ranged density and current modulations induced by localized obstacles show how the environment can have a far-reaching impact on active fluids. This is best exemplified by the propensity of bulk and boundary disorder to destroy bulk phase separation in active matter, showing active systems to be much more sensitive to their surroundings than passive ones.

QDS: Can quantum computing enhance machine learning and, if yes, how?

I will begin by demonstrating that the answer to the first question in the title is yes [1], in principle. I will then discuss if the quantum advantage of quantum machine learning can be exploited in practice. To discuss how to build optimal quantum machine learning models, I will describe our recent work [2-3] on applications of classical Bayesian machine learning for quantum predictions by extrapolation. In particular, I will show that machine learning models can be designed to learn from observables in one quantum phase and make predictions of phase transitions as well as system properties in other phases. I will also show that machine learning models can be designed to learn from data in a lower-dimensional Hilbert space to make predictions for quantum systems living in higher-dimensional Hilbert spaces. I will then demonstrate that the same Bayesian algorithm can be extended to design gate sequences of a quantum computer that produce performant quantum kernels for data-starved classification tasks [4]. [1] J. Jäger and R. V. Krems, Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines, Nature Communications 14, 576 (2023) [2] R. A. Vargas-Hernandez, J. Sous, M. Berciu, and R. V. Krems, Extrapolating quantum observables with machine learning: Inferring multiple phase transitions from properties of a single phase, Physical Review Letters 121, 255702 (2018) [3] P. Kairon, J. Sous, M. Berciu and R. V. Krems, Extrapolation of polaron properties to low phonon frequencies by Bayesian machine learning, Phys. Rev. B 109, 144523 (2024). [4] E. Torabian and R. V. Krems, Compositional optimization of quantum circuits for quantum kernels of support vector machines, Physical Review Research 5, 013211 (2023)

IMPRS Seminar:

IMPRS Seminar

Colloquium

Colloquium

Colloquium

Copernicus Revisited: Is the Earth Special?

Nearly 500 years ago, Nicolas Copernicus published his disruptive theory that Earth is not the center of the universe. This "Copernican demotion" has held fast over the centuries, as astronomers have learned that there is nothing particularly remarkable about Earth or even the Milky Way. In the last two decades, however, a new test of the Copernican Principle has emerged -- the discovery of an abundance of planets orbiting other stars. These discoveries allow us to put Earth in context and evaluate whether the formation, architecture, and present-day characteristics of our Solar System are in fact typical. One of the biggest open questions is whether Earth-like exoplanets have water, a key ingredient for life. Thanks to the revolutionary new observing capabilities of the James Webb Space Telescope (JWST), it is possible to characterize the atmospheres of Earth-sized worlds for the first time. In this talk, I will share the latest observations of rocky exoplanet atmospheres from JWST, discuss the implications for their water abundances in comparison to the Earth, and answer the question: was Copernicus wrong?