Decoding Primordial Fluctuations

One of the great insights of cryptography and computational complexity is the notion of computationally-bounded algorithms, which allow us to reason about properties that would otherwise be impossible to achieve. Could these ideas also be relevant to quantum information and quantum mechanics? In this talk, we discuss how computational thinking changes the landscape of quantum cryptography: What primitives can we construct, how definitions change, and what are the outstanding open problems. If time permits, we will also discuss new foundational questions on quantum information, motivated by cryptographic applications.

All the information we will ever obtain from the early universe is imprinted in the spatial correlations of primordial fluctuations at the hot Big Bang. I will explain how an influx of ideas from various areas of fundamental physics is providing us with new conceptual and practical tools to decode the physics of these primordial fluctuations. A thorough understanding of the fluctuations will give us insight into particle physics at high energies and may provide a window into the nature of spacetime itself.

The gravitational two-body problem has been fundamental to physics since Newton's time. With the advent of gravitational wave astronomy and the anticipated third generation of gravitational wave detectors in the 2030s, there is an increasing need for high-precision predictions from Einstein's theory of gravity regarding the encounters of black holes and neutron stars in our universe. Fascinatingly, perturbative quantum field theory methods developed for high-precision predictions of elementary particle scattering at the LHC have proven remarkably efficient for this classical physics problem. This unexpected connection has led to inspiring synergies between collider and gravitational wave physics. In my talk, I will present our approach using a worldline quantum field theory inspired by string theory, which has emerged as the most efficient tool for quantifying the scattering of spinning black holes. We have achieved highest-precision perturbative results for the scattering angle, radiated energy, and recoil of such black hole encounters at the fifth order in Newton's gravitational coupling G. Our four-loop calculations have revealed a new class of mathematical functions related to Calabi-Yau manifolds, previously studied only in mathematics and string theory compactifications, appearing for the first time in a physical context: The radiated energy in gravitational waves at NNNNLO perturbation theory.

We present Denario, an AI multi-agent system designed to be a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and writing a scientific paper. Denario is built as a modular system, and therefore, can perform either very specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using cmbagent as a deep-research backend. In this talk, we describe Denario and its modules in detail and illustrate its capabilities by presenting multiple AI-generated papers generated by it. These papers cover many scientific disciplines, such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, and planetary science. Denario can also perform research combining ideas from different disciplines, and we illustrate it by showing a paper that applies methods from quantum physics and machine learning with astrophysical data. We publicly release the code at https://github.com/AstroPilot-AI/Denario. A Denario demo can also be run directly on the web at https://huggingface.co/spaces/astropilot-ai/Denario, and the full app is deployed on the cloud.

An appealing and challenging route towards engineering materials with specic properties is to find ways of designing or selectively manipulate materials, especially at the quantum level. We will provide an overview of how well-established concepts in the fields of quantum chemistry and materials have to be adapted when the quantum nature of light becomes important. We will pursue the question whether it is possible to create these new states of materials as groundstates of the system. To this end we will show how the emerging (vaccum) dressed states resembles Floquet states in driven systems. A particular appeal of light dressing is the possibility to engineer symmetry breaking which can lead to novel properties of materials, e.g coupling to circularly polarized photons leads to local breaking of time-reversal symmetry enabling the control over a large variety of materials properties (e.g.topology). We show that the new quantum electrodynamics density-functional formalism (QEDFT) can acocunt for those effects. We illustrate the realisation of those ideas in molecular complexes and 2D materials.

Tensor Network States, like matrix product or projected entangled pair states play an important role in both, quantum information theory and many-body physics. They offer a compact and efficient representation, enabling accelerated numerical computations and providing intuitive insights into many-body phenomena. In this talk, I will discuss how certain states can be efficiently prepared and manipulated using quantum devices, highlighting the use of local operations and classical communication. Tensor networks can also be used to efficiently describe quantum channels. I will also mention how those channels can be efficiently implemented as quantum circuits.

A hallmark of living systems is their ability to generate and maintain order under constant fluctuations. In cells, such order often emerges from chemomechanical pattern formation, where proteins both sense and remodel the geometry of the cell. Here, I will discuss how theoretical modeling and simulations can capture this feedback across different spatial scales, using three example systems: on the macroscopic scale of individual cells, we used optogenetic control over a chemomechanical protein system to control the shape of starfish oocytes and induce self-organized surface contractions in these cells. On the mesoscopic scale of synthetic vesicles, I will discuss how protein patterns can drive the motility of synthetic liposomes, providing a minimal mechanism to transform chemical energy into motion without molecular motors. Finally, on the intracellular nanometer scale, I will present a mechanism for pattern formation without active energy consumption that relies on curvature sensitivity of membrane-binding proteins. Looking forward, I will discuss data-driven avenues for systematically analyzing biological self-organization, with particular focus on bringing experiments and simulations closer together.

Already within the Standard Model, we expect that the fundamental scale of gravity, the scale where gravity becomes strong, is slightly lower than the Planck scale. Theories with extra dimensions or with many additional particle species enhance this effect and are motivated by giving a unified solution to the Hierarchy problem, Dark Matter, and neutrino masses. In this talk, we will discuss their phenomenology in low-energy experiments, their unique astrophysical signatures, and present recent experimental results.

At a singularity the continuum description of spacetime breaks down and one can hope that the microscopic constituents will be revealed. Over 50 years ago, Belinski-Khalatnikov-Lifshitz (BKL) argued that the dynamics of spacetime close to the Big Bang singularity (or inside black holes) is chaotic and inhomogeneous. I will revisit the BKL scenario within a modern understanding of quantum chaos and holographic duality. I will argue that the remarkable modular symmetries that arise in the near-singularity dynamics suggests a dual description of the start of time as a so-called "primon gas", a description that is at once both simple and also connects with deep results from number theory.

In the development of animals, tissues self-organise starting from a single cell into lay- ers, shapes and patterns. This active mechanical process operates beyond the theoretical framework of reaction-diffusion equations such as Turing patterns. At the same time, combining active driving with careful mechanical design of a system is distinct route to pattern formation and artificial functionality. Here, I will begin by introducing vertex models, a tissue model where the two dimensional cell layer is approximated by a polygonal tilings. I will then how two types of active driving can generate function: First, for polar active materials, a coupling of activity to force, a.k.a. self-alignment, is generic. Governed by the activity-elasticity interactions, it generates either flocking or oscillatory dynamics depending on the boundary conditions of the tissue. Second, mechanochemical stress feedback in cell-cell junctions arises from the catch bond dynamics of the actomyosin cortex. It allows a junction to generate a contractile force that can overcome external pulling and thus allow for an active rear- rangement or T1. In vertex and continuum models, for strong enough feedback this gives rise to convergence-extension flows where the flow is opposite the direction of mechanical polarisation, effectively generating a negative viscosity state.