Format results
- PIRSA:22120001
Statistical Physics - Lecture 221205
PIRSA:22120007Learning the sign structures of quantum systems: is it hard or trivial?
Tom Westerhout Radboud Universiteit Nijmegen
Quantum Field Theory II - Lecture 221202
PIRSA:22120000Aggregative Efficiency of Bayesian Learning in Networks
Krishna Darasartha (Boston U. )Social Learning and Sample Herding in Networks with Homophily
Matt Jackson (Stanford)Quantum Impulse Sensing with Mechanical Sensors in the Search for Dark Matter
Sohitri Ghosh University of Maryland, College Park
Holographic measurement and bulk teleportation
Stefano Antonini University of Maryland, College Park
Hidden symmetries in cosmology and black holes
Francesco Sartini Okinawa Institute of Science and Technology Graduate University
Quantum Field Theory II - Lecture 221205
PIRSA:22120001Statistical Physics - Lecture 221205
PIRSA:22120007Learning the sign structures of quantum systems: is it hard or trivial?
Tom Westerhout Radboud Universiteit Nijmegen
A well-established approach to solving interacting quantum systems is variational Monte Carlo. There is a lot of renewed interest in it since the introduction of neural networks as a highly expressive and unbiased variational ansatz. Similar to more traditional ansätze, neural networks struggle with solving frustrated quantum systems. A conjecture has been made that the cause of these difficulties lies in the sign structures of the ground state wavefunctions. Here, we will discuss these sign structures in more detail and try to analyze how complex they really are by establishing a connection to classical Ising models.
Zoom link: https://pitp.zoom.us/j/99087954160?pwd=Vm5zWWRFbHBwVFR1RHZMc3ptem03QT09
Quantum Field Theory II - Lecture 221202
PIRSA:22120000Aggregative Efficiency of Bayesian Learning in Networks
Krishna Darasartha (Boston U. )When individuals in a social network learn about an unknown state from private signals and neighbors’ actions, the network structure often causes information loss. We consider rational agents and Gaussian signals in the canonical sequential social-learning problem and ask how the network changes the efficiency of signal aggregation. Rational actions in our model are a log-linear function of observations and admit a signal-counting interpretation of accuracy. This generates a fine-grained ranking of networks based on their aggregative efficiency index. Networks where agents observe multiple neighbors but not their common predecessors confound information, and we show confounding can make learning very inefficient. In a class of networks where agents move in generations and observe the previous generation, aggregative efficiency is a simple function of network parameters: increasing in observations and decreasing in confounding. Generations after the first contribute very little additional information due to confounding, even when generations are arbitrarily large.(Relaxing) Common Belief for Social Networks
Grant Schoenbeck (U. Michigan)Many social network phenomena such as norms and cascades depend not only on what agents believe but on what they believe other agents believe. One important instantiation of agents’ beliefs about beliefs is knowledge commonly known by all agents. However, current definitions that capture this idea such as common knowledge and common belief are too restrictive for use in understanding strategic coordination and cooperation in social network settings. In this talk, I will propose a relaxation of common belief called factional belief that is suitable for the analysis of social network phenomena. I will then show how this definition can be used to analyze revolt games on sparse graphs. In particular, I will show that for a certain natural class of revolt games, the degree sequence of a network almost entirely characterizes whether any equilibrium can often support a large revolt. The proof is via an efficient algorithm for determining the same. A key goal of this talk is to provide the background to start a conversation about where common knowledge (or its variants) can help us to reason about social network phenomena.Organizing Modular Production
Bryony Reich (Northwestern)We characterize the optimal communication network in a firm with a modular production function, which we model as a network of decisions with a non-overlapping community structure. Optimal communication is characterized by two hierarchies that determine whom each agent receives information from and sends information to. Receiver rank depends only on module cohesion while sender rank also depends on decision-specific values of adaptation. When the hierarchies are the reverse of each other, optimal communication is bottom up in aggregate, and when they are the same, it has a core-periphery structure, in which the core contains the most cohesive modules.Social Learning and Sample Herding in Networks with Homophily
Matt Jackson (Stanford)Other peoples' experiences serve as primary sources of information about the potential payoffs to various available opportunities. Homophily in social networks affects both the quality and diversity of information to which people have access.On the one hand, homophily provides higher quality information since observing the experiences of another person is more informative as that person is more similar to the decision maker. On the other hand, homophily lowers the variety of actions about which people can learn when a group ends up herding on specific actions about which they have better information. This can lead to inefficiencies and inequalities across groups, as we show. Homophily lowers efficiency and increases inequality in sparse networks, while enhancing efficiency and decreasing inequality in denser networks. We characterize conditions under which groups herd on separate actions, and show how such homophily-induced herding driven by limited scope of information differs from standard forms of herding driven by cascading inferences.Quantum Impulse Sensing with Mechanical Sensors in the Search for Dark Matter
Sohitri Ghosh University of Maryland, College Park
Recent advances in mechanical sensing technologies have led to the suggestion that heavy dark matter candidates around the Planck mass range could be detected through their gravitational interaction alone. The Windchime collaboration is developing the necessary techniques, systems, and experimental apparatus using arrays of optomechanical sensors that operate in the regime of high-bandwidth force detection, i.e., impulse metrology. Today's sensors can be limited by the added noise due to the act of measurement itself. Techniques to go beyond this limit include squeezing of the light used for measurement and backaction evading measurement by estimating quantum non-demolition operators — typically the momentum of a mechanical resonator well above its resonance frequency. In this talk, we will discuss the theoretical limits to noise reduction using such quantum enhanced readout techniques for these optomechanical sensors.
Holographic measurement and bulk teleportation
Stefano Antonini University of Maryland, College Park
In holography, spacetime is emergent and its properties depend on the entanglement structure of the dual theory. An interesting question is how changes in the entanglement structure affect the bulk dual description. In this talk, I will describe how local projective measurements performed on a subregion of the boundary theory modify the bulk dual spacetime. The post-measurement bulk is cut off by end-of-the-world branes and is dual to the complementary unmeasured region . Using a bulk calculation in —which involves a phase transition triggered by the measurement—and tensor network models of holography, I will show that the portion of bulk preserved after the measurement depends on the size of and the state we project on. Interestingly, the post-measurement bulk includes regions that were part of the entanglement wedge of before the measurement. Our results indicate that the effect of a measurement performed on a subregion of the boundary is to teleport part of the bulk information contained in into the complementary region . Finally, I will comment on applications to the eternal black hole in JT gravity (dual to the SYK thermofield double state) and the relationship between measurements and traversable wormholes.
Hidden symmetries in cosmology and black holes
Francesco Sartini Okinawa Institute of Science and Technology Graduate University
Cosmological models and black holes belong to classes of space-time metrics defined in terms of a finite number of degrees of freedom, for which the Einstein–Hilbert action reduces to a one-dimensional mechanical model. We investigate their classical symmetries and the algebra of the corresponding Noether charges. These dynamical symmetries have a geometric interpretation, not in terms of spacetime geometry, but in terms of motion on the field space. Moreover, they interplay with the fiducial scales, introduced to regulate the homogenous model, suggesting a relationship with the boundary symmetries of the full theory.
Finally, the existence of these symmetries unravels new aspects of the physics of black holes and cosmology. It opens the way towards a rigorous group quantization of the reduced model and to the study of their holographic properties. It might have significant consequences on the propagation of test fields and the corresponding perturbation theory.
Zoom link: https://pitp.zoom.us/j/92846533238?pwd=cERGUjd6OXB5S0ZaSzVIdVJyMHZxUT09
Experimentation on Networks
Moritz Meyer-ter-Vehn (UCLA)We introduce a model of strategic experimentation on social networks where forward-looking agents learn from their own and neighbors’ successes. In equilibrium, private discovery is followed by social diffusion. Social learning crowds out own experimentation, so total information decreases with network density; we determine density thresholds below which agents asymptotically learn the state. In contrast, agent welfare is single-peaked in network density, and achieves a second-best benchmark level at intermediate levels that achieve a balance between discovery and diffusion. We also study how learning and welfare differ across directed, undirected and clustered networks.