coffee, tea, cookies at 16:00 in the main hall

Monday 16:30-17:30

Seminar room 1+2

- monthly seminars -

Wednesday 15:30 - 17:00

Seminar room 4

- weekly seminars -

Monday 11:00-12:00

Seminar room 4

Wednesday 16:30 - 17:30

Seminar room 1D1

Thursday 14:00-15:00

Seminar room 4

27 May 2024

04:30 PM

04:30 PM

Seminarroom 1+2+3
iCal Event

29 May 2024

02:00 PM

02:00 PM

The discovery of intrinsically disordered proteins (IDPs) has heralded a paradigm shift in molecular biology away from the principle of ”form follows function”. These IDPs can form biomolecular condensates that fulfill numerous functions in living cells, e.g., signal transduction, stress response and controlled reactions. Due to the conceptual similarities between IDPs and classical polymers, physics-based theories and computer simulations can help to understand, predict and engineer the static and dynamic properties of naturally occurring and synthetic IDPs. In this talk, I will present selected insights we have gained from coarse-grained molecular simulations, and discuss the intricacies and limitations of the underlying models. Key findings include that IDPs inherently exhibit heterogeneous interactions that are weak and distributed along the chain contour, and that IDPs collapse at the condensate-water interface and are tangentially oriented. Further, we discovered that the phase behavior and materials properties of condensates can be deducted with great accuracy from the conformations of single IDP chains in solution.

Seminarroom 4
iCal Event

30 May 2024

02:00 PM

02:00 PM

We investigate the dynamics of a single chiral active particle subject to an external torque due to the presence of a gravitational field -- coined gravitaxis in Ref. [1]. Our computer simulations reveal an arbitrarily strong increase of the long-time diffusivity of the gravitactic agent when the external torque approaches the intrinsic angular drift. We provide analytic expressions for the mean-square displacement in terms of eigenfunctions and eigenvalues of the noisy-driven-pendulum problem. The pronounced maximum in the diffusivity is then rationalized by the vanishing of the lowest eigenvalues of the Fokker-Planck equation for the angular motion as the rotational diffusion decreases and the underlying classical bifurcation is approached. A simple harmonic-oscillator picture for the barrier-dominated motion provides a quantitative description for the onset of the resonance while its range of validity is determined by the crossover to a critical-fluctuation-dominated regime [2]. [1] B. ten Hagen, F. Kümmel, R. Wittkowski, D. Takagi, H. Löwen, and C. Bechinger, Gravitaxis of asymmetric self-propelled colloidal particles, Nature Communications 5, 4829 (2014). [2] O. Chepizhko and T. Franosch, Resonant Diffusion of a Gravitactic Circle Swimmer, Phys. Rev.Lett. 129, 228003 (2022).

Room 1D1
iCal Event

05 Jun 2024

02:00 PM

02:00 PM

Seminarroom 4
iCal Event

12 Jun 2024

02:00 PM

02:00 PM

Seminarroom 4
iCal Event

13 Jun 2024

04:30 PM

04:30 PM

Room 1D1
iCal Event

17 Jun 2024

04:30 PM

04:30 PM

Seminarroom 1+2+3
iCal Event

27 Jun 2024

04:30 PM

04:30 PM

Room 1D1
iCal Event

03 Jul 2024

02:00 PM

02:00 PM

Seminarroom 4
iCal Event

25 Jul 2024

04:30 PM

04:30 PM

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)

Room 1D1
iCal Event