Talks

In this talk, I will describe a method that rigorously transforms the toric code model into independent emergent qubits, which enables us to construct the toric code eigenstates exactly and devise precise quantum circuits for their implementation on quantum processors.

The Josephson diode effect (JDE), characterized by asymmetric critical currents in a Josephson junction, has drawn considerable attention in the field of condensed matter physics. We investigate the conditions under which JDE can manifest in a one-dimensional Josephson junction composed of a spin-orbit-coupled quantum wire with an applied Zeeman field, connected between two superconductors. Our study reveals that while spin-orbit coupling (SOC) and a Zeeman field in the quantum wire are not sufficient to induce JDE when the superconductors are purely singlet, the introduction of triplet pairing in the superconductors leads to the emergence of JDE. This finding highlights the potential of JDE as a probe for triplet superconductivity. We further demonstrate that even in absence of SOC in the quantum wire, JDE can arise when the directions of the triplet pairing and the Zeeman field are non-collinear, provided the superconductors exhibit mixed singlet-triplet pairing. Additionally, we identify specific conditions under which JDE is absent, namely, when the pairing is purely triplet and the directions of the SOC and the triplet pairing are perpendicular. Our results suggest that quantum wires in Josephson junctions could serve as effective platforms for probing triplet superconductivity through the observation of JDE.

Cosmology is undergoing a data revolution. Surveys such as the imminent Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory will deliver huge galaxy catalogues that provide critical tools for understanding the nature of dark matter and dark energy. However, in order to obtain accurate cosmological constraints from these enormous datasets, we need reliable ways of estimating galaxy properties using only photometry. I will present pop-cosmos: a forward modelling framework for photometric galaxy survey data, where galaxies are modelled as draws from a population prior distribution over redshift, mass, dust properties, metallicity, and star formation history. These properties are mapped to photometry using an emulator for stellar population synthesis, followed by the application of a learned model for a survey's noise properties. Application of selection cuts enables the generation of mock galaxy catalogues. This enables us to use simulation-based inference to solve the inverse problem of calibrating the population-level prior on a deep multiwavelength catalogue, COSMOS2020. We use a diffusion model as a flexible population-level prior, and optimise its parameters by minimising the Wasserstein distance between forward-simulated photometry and the real COSMOS2020 survey data. The resulting model can then be used to derive accurate redshift distributions for upcoming photometric surveys, to facilitate weak lensing and clustering science. I will show applications of this framework, demonstrating how we are able to extract redshift distributions, and make inferences about galaxy evolution. I will also discuss the use of pop-cosmos as a prior for performing inference on individual galaxies in a highly scaleable manner, as well as ongoing work to analyse data from the Kilo-Degree Survey (KiDS) in preparation for LSST.

We present results of construction of the ground states of a paradigmatic strongly interacting quantum system namely the square lattice quantum dimer model as a group equivariant convolutional neural network variational state. The network is trained by minimizing, using stochastic gradient descent, the Monte Carlo estimated energy expectation value. We show comparison with exact diagonalization for small systems (size = 8x8) and with quantum Monte Carlo for larger systems up to 48x48.

In this work, I explore the topological features of aggregation patterns in the Vicsek Model, a widely used framework for describing the collective dynamics of active matter. By varying the three key parameters—population size N, interaction radius R, and noise η, different point sets of self- organising agents are generated. To analyse the emergent structures, I employ topological tools, namely the Euler characteristic and Betti numbers, in both spatial and temporal domains. The Euler characteristic, a fundamental topological invariant, provides insights into system connectivity, while Betti numbers characterise features such as connected components, loops, and voids. Three-dimensional Euler Characteristic Surfaces (ECS) are constructed that carry the summary of the spatio-temporal evolution of the Euler Characteristic. Further, a metric distance, which we name the Euler Metric (EM), is estimated between these surfaces to investigate how system parameters influence aggregation dynamics. Additionally, I analyse order parameters to distinguish between ordered and chaotic regimes, further contextualising the topological findings.

Statistical physics deals with the large amount of heterogeneous population, nonequilibrium systems and have facilitated the studies of complex systems dynamics. Now with the help of volumes of data available it is possible to understand the dynamical processes ongoing on complex systems through nonlinearity, feedback loops, emergence and in some instances  critical transitions via data driven approaches, computational modeling and complex networks.  In addition to this there are wicked problems, characterized by their complexity and interconnectedness. These are referred to as social, economical, environmental or cultural issues that defy simple solutions due to their inherent ambiguity, multiple variable interactions and  lack of a clear convergent solution. The complex systems approach provides a framework for understanding and addressing such problems by emphasizing interconnectedness and feedback loops, which can help to identify and mitigate unintended consequences of policy interventions. Wicked problems involve multiple, interconnected factors, making it difficult to pinpoint through single cause or effect. Complex systems thinking involves interconnectedness of various factors and actors, helping to understand how different elements influence each other & drives the feedback loops and recognizing how actions and interventions can lead to unintended consequences through feedback loops and path dependencies which is crucial for detailed understanding and  effective policy design. In this talk I will discuss some wicked problems and their complexity inspired solutions.

Keywords: Interaction, Interconnectedness, Complex networks, Random Matrices, Ecological Flourishing

Glassy systems are ubiquitous in nature, appearing in materials ranging from window glass to biological matter. These systems are non-crystalline solids that structurally resemble liquids but exhibit extremely slow dynamics. In this talk, I will focus on a particular class of glassy materials known as topological glass formers—systems composed of ring polymers. We investigate how aging influences the dynamics of these systems and explore how their behavior changes across the temperature–stiffness phase space. Interestingly, we find a nonlinear relationship between the glass transition temperature and the stiffness of the rings. A central role is played by threading interactions—entanglement-like constraints unique to ring polymers, which become increasingly long-lived as the system ages. Together, these features give rise to a distinct form of glassy dynamics that emerges purely from the system’s topology.

In this talk, I will present findings from our recent work (Phys. Rev. B 108, 024203 (2023)), where we propose a Monte Carlo simulation to understand electron transport in a non-equilibrium steady state (NESS) for the lattice Coulomb Glass model, created by continuous excitation of single electrons to high energies followed by relaxation of the system. Around the Fermi level, the NESS state roughly obeys the Fermi-Dirac statistics, with an effective temperature (Teff) greater than the bath temperature of the system (T). Teff is a function of T and the rate of photon absorption by the system. Furthermore, we find that the change in conductivity is only a function of relaxation times and is almost independent of the bath temperature. Our results indicate that the conductivity of the NESS state can still be characterized by the Efros-Shklovskii law with an effective temperature of Teff > T. Additionally, the dominance of phononless hopping over phonon-assisted hopping is used to explain the hot electron model's relevance to the conductivity of the NESS state.

When do mucus films plug lung airways? Using reduced-order simulations of a large ensemble of randomly perturbed films, we show that the answer is not determined by just the film’s volume. While very thin films always stay open and very thick films always plug, we find a range of intermediate films for which plugging is uncertain. The fastest-growing linear mode of the Rayleigh-Plateau instability ensures that the film’s volume is divided among multiple humps. However, the nonlinear growth of these humps can occur unevenly, due to spontaneous axial sliding—a lucky hump can sweep up a disproportionate share of the film’s volume and so form a plug. This sliding-induced plugging is robust and prevails with or without gravitational and ciliary transport.