Search Talks

ZOOM: https://pitp.zoom.us/j/94595394881?pwd=OUZSSXpzYlhFcGlIRm81Y3VaYVpCQT09 In this work, we use data-driven methods to reduce the dimensionality of the vertex function for the Hubbard model and spin liquid model. By employing a deep learning architecture based on the autoencoder, we show that the functional renormalization group (FRG) dynamics can be efficiently learned. Our approach is compared with other methods, including principal component analysis and dynamic mode decomposition. Our results demonstrate the effectiveness of our proposed approach for understanding the FRG flow in these models.

While entanglement has been examined extensively in AdS/CFT, it has avoided significant attention in the study of celestial holography and asymptotic symmetries relevant to asymptotically flat spacetime. I will present work that considers the entanglement of a Milne patch for Maxwell theory in Minkowski spacetime from the perspective of celestial holography. In the Minkowski vacuum, we find that the Milne patch is thermally entangled. We interpret the thermal entangling operator that builds the Minkowski vacuum from the Milne vacuum as an interaction term in the celestial CFT. We further examine the edge modes of the Milne patch, assigning them a physical interpretation as fluctuations in Milne asymptotic charge. Interestingly, we find that the constraint governing these edge modes includes sources that avoid the Minkowski interior. Altogether, by studying entanglement along the extra holographic direction present in celestial holography but absent in AdS/CFT, our work bridges a critical gap between our understanding of entanglement in the latter and the physically relevant setting of asymptotically flat spacetime.

Zoom Link: TBD

It is often the case that interaction with a quantum system does not simply occur between an initial point in time and a final one, but rather over many time steps. In such cases, an interaction at a given time step can have an influence on the dynamics of the system at a much later time. Just as quantum channels model dynamics between two time steps, quantum combs model the more general multi-time dynamics described above, and have accordingly found application in such fields as open quantum systems and quantum cryptography. In this talk, we will consider ensembles of combs indexed by a random variable, dubbed classical-quantum combs, and discuss how much can be learnt about said variable through interacting with the system. We characterise the amount of information gain using the comb min-entropy, an extension of the analogous entropic quantity for quantum states. With combs and the min-entropy in our toolbox, we turn to a number of applications largely inspired by Measurement-Based Quantum Computing (MBQC), including the security analysis of a specific Blind Quantum Computing protocol and some comments regarding learning causal structure.

Zoom Link: https://pitp.zoom.us/j/98315660866?pwd=cWU3RzB6SG9DOGIza1BqV1lqNklvQT09