Talks

My research investigates the role of Bose-Einstein statistics in the genesis of quantum optics. It explores key developments following the emergence of quantum statistics in the late 1920s, which eventually led to the classification of elementary entities as bosons and fermions in 1945. Additionally, it examines whether the concept of an evolving statistical style of reasoning in physics provides a useful analytical framework for understanding the debate on the Hanbury Brown and Twiss (HBT) effect, the early investigations of photon correlations in the late 1950s, and the emergence of photon statistics in the early 1960s.

Einstein, Podolsky and Rosen’s “Can quantum mechanical description of reality be considered complete?”(1935) and Schrödinger’s “Die gegenwärtige Situation in derQuantenmechanik” (1936) are commonly accepted as the seminal papers for the modern study of quantum mechanical entanglement. However, not much has been known about the prehistory of these papers. By a study of Schrödinger’s correspondence and extensive research notes we show that Schrödinger was aware of what is essentially the EPR argument already in November 1931, after a talk by Einstein on the photon box thought experiment. We also note the importance of thei nput from Leo Szilard in Schrödinger’s notes, which credit Szilard both with the simplification of the photon box experiment to that of a collision between a photon and a mirror and with proposing a quantum mechanical state that is essentially the state used in the EPR argument.

Critical fluctuations coupled to Fermi surfaces can destroy conventional Fermi liquids, giving rise to diverse non-Fermi liquids that may remain metallic or become superconducting at zero temperature. I will present a classification of non-Fermi liquids with globally convex hot Fermi surfaces based on the concept of projective fixed points, which account for the flow of the Fermi momentum under renormalization. This framework organizes non-Fermi liquids into seven super-universality classes, each characterized by distinct superconducting fluctuations. Stable non-Fermi liquids exhibit universal but non-scale-invariant pairing interactions, while unstable ones display universal constraints on pairing symmetry, superconducting transition temperature, and its scaling with the Fermi momentum. I will discuss physical examples and a toy model that captures the essential universal properties of these classes.

I will introduce factorization algebras as an approach to understanding quantum field theory. I will outline some of the benefits of this approach, as well as how it connects with other topics in mathematical physics.

This study presents the modelling of the gravitational wave (GW) bias parameter by bridging between simulated GW sources and galaxies in low redshift galaxy surveys 2MPZ, WISExSCOS (WISC) and SDSS DR7. We study this connection by creating a mock host catalog for GW events and populating galaxy surveys with binary black holes (BBHs) for different scenarios of the GW host-galaxy probability as a function of the galaxy stellar mass, SFR and metallicity. For 2MPZ, WISExSCOS (WISC) galaxy surveys with only stellar mass information, we consider a phenomenological broken power law model for the host-galaxy probability function, with a potential turnover where the star formation efficiency begins to drop. We vary the parameters of the GW host-galaxy probability function and find that generically the GW bias increases as the turnover point increases. The change in the GW bias parameter shows a maximum change of about 30% for different scenarios explored in this work in comparison to the galaxy bias. For the SDSS DR7 survey with stellar mass, SFR and metallicity information, we use a joint host-galaxy probability function defined over stellar mass, star formation rate (SFR), and metallicity. This probability is modelled as the product of three broken power-law distributions, each with a turnover point motivated by astrophysical processes governing the relation between current-day galaxy properties and BBH mergers, such as galaxy quenching and BBH delay time. In this case, our results show that GW bias is most sensitive to host-galaxy probability dependence on stellar mass, with increases of up to ∼O(10)% relative to galaxy bias as the stellar mass pivot scale rises. We also find a notable relationship between GW bias and SFR: when the host-galaxy probability favors low-SFR galaxies, the GW bias significantly increases. In contrast, we observe no strong correlation between GW bias and metallicity. These findings suggest that the spatial clustering of GW sources is primarily driven by the stellar mass and SFR of their host galaxies and shows how GW bias measurements can inform models of the host-galaxy probability function.

Neural quantum states have emerged as a powerful framework for representing classical probability distributions derived from quantum many-body systems. However, fundamental questions remain about their computational learnability, particularly for physically relevant quantum states. We identify and analyze a fundamental obstacle to the efficient learning of locally indistinguishable quantum states using neural network architectures. We introduce a restricted statistical query (SQ) learning model that captures the essential features of noisy gradient descent training in autoregressive models such as recurrent neural networks networks. Within this framework, we prove that locally indistinguishable states cannot be learned in polynomial time, establishing an inherent computational barrier. Conversely, we show that distributions with finite Markov length remain efficiently learnable in the restricted SQ model. We demonstrate the practical implications through numerical experiments on paradigmatic examples of strong-to-weak symmetry breaking, including ferromagnetic ground states and syndrome distributions of quantum error-correcting codes beyond their decoding thresholds. Our findings suggest that the hardness of neural quantum state learning could serve as a novel computational probe for identifying mixed-state phase transitions and decoding thresholds in both numerical simulations and experiments.