Talks

Emergent dynamics in proliferating cellular matter

The ability to grow and multiply is a central and indispensable non-equilibrium feature of living matter which leads to striking self-organization phenomena in systems as diverse as bacterial colonies, tissues, tumors or embryos. Here, I will present some of our recent theoretical efforts in understanding how the specific properties of proliferation-induced activities drive collective behavior of such dense cellular aggregates. Using minimal particle-based models, we first investigate how large-scale flows and mechanical stresses caused by growth in confinement interact with the anisotropic shape of particles, such as rod-shaped bacteria, to produce orientational order. This reveals a strong relationship between near-perfect alignment accompanied by an inversion of stress anisotropy for particles with large length-to-width ratios, as well as a sensitive dependence on particle shape. Second, we consider exponentially growing, three-dimensional colonies of motile cells such as tissue spheroids or tumors. By developing statistical measures suited for non-conserved particle numbers, we detect a new kind of mixing transition which is characterized by a diverging mixing time scale despite cellular-scale diffusive motion of individual cells. If time permits, I will briefly outline a volume-conserving analog of this expanding system where similar considerations uncover universal scaling behavior, and discuss connections to other kinds of activities such as metabolism, gene regulation or cell removal.

Colloquium

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Optimizing leapover lengths of Lévy flights with resetting

IMPRS Seminar

Colloquium

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QDS

Colloquium: Lighting up Superconductivity

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Colloquium

QDS

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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