Format results
Statistical Physics - Lecture 221206
PIRSA:22120008Equivariant Higher Berry classes and chiral states
Nikita Sopenko Institute for Advanced Study (IAS) - School of Natural Sciences (SNS)
Quantum Error Mitigation and Error Correction: a Mathematical Approach
Ningping Cao University of Waterloo
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
Quantum Field Theory II - Lecture 221202
PIRSA:22120000Aggregative Efficiency of Bayesian Learning in Networks
Krishna Darasartha (Boston U. )
QFT2 - Quantum Electrodynamics - Morning Lecture
This course uses quantum electrodynamics (QED) as a vehicle for covering several more advanced topics within quantum field theory, and so is aimed at graduate students that already have had an introductory course on quantum field theory. Among the topics hoped to be covered are: gauge invariance for massless spin-1 particles from special relativity and quantum mechanics; Ward identities; photon scattering and loops; UV and IR divergences and why they are handled differently; effective theories and the renormalization group; anomalies.
Statistical Physics - Lecture 221206
PIRSA:22120008Equivariant Higher Berry classes and chiral states
Nikita Sopenko Institute for Advanced Study (IAS) - School of Natural Sciences (SNS)
I will talk about the generalization of Berry classes for quantum lattice spin systems. It defines invariants of topologically ordered states or families thereof. In particular, its equivariant version for 2d gapped states gives the zero-temperature Hall conductance and its various generalizations. I will also discuss the construction of chiral states realizing the topological order associated with a unitary rational vertex operator algebra for which these invariants are non-trivial
Zoom link: https://pitp.zoom.us/j/99910103969?pwd=VlVHVGRiV29iVEFyTXZyR3ovMkRaQT09
Cutting Cosmological Correlators
Harry Goodhew University of Cambridge
The initial conditions of our universe appear to us in the form of a classical probability distribution that we probe with cosmological observations. In the current leading paradigm, this probability distribution arises from a quantum mechanical wavefunction of the universe. In this talk I will discuss how we can adapt flat space bootstrapping techniques to the quantum fluctuations in the early universe, in particular showing that the requirement of unitary time evolution, colloquially the conservation of probabilities, fixes the analytic structure of the wavefunction and of all the cosmological correlators it encodes.
Zoom link: https://pitp.zoom.us/j/95812107239?pwd=bVZMcWdHTVM0Y0tFZGMxS2FCVGF0Zz09
Quantum Error Mitigation and Error Correction: a Mathematical Approach
Ningping Cao University of Waterloo
Error-correcting codes were invented to correct errors on noisy communication channels. Quantum error correction (QEC), however, has a wider range of uses, including information transmission, quantum simulation/computation, and fault-tolerance. These invite us to rethink QEC, in particular, the role that quantum physics plays in terms of encoding and decoding. The fact that many quantum algorithms, especially near-term hybrid quantum-classical algorithms, only use limited types of local measurements on quantum states, leads to various new techniques called Quantum Error Mitigation (QEM). We examine the task of QEM from several perspectives. Using some intuitions built upon classical and quantum communication scenarios, we clarify some fundamental distinctions between QEC and QEM. We then discuss the implications of noise invertibility for QEM, and give an explicit construction called Drazin-inverse for non-invertible noise, which is trace-preserving while the commonly-used MoorePenrose pseudoinverse may not be. Finally, we study the consequences of having imperfect knowledge about system noise and derive conditions when noise can be reduced using QEM.
Zoom link: https://pitp.zoom.us/j/91543402893?pwd=b09IS3VWNk5KZi8ya3gzSmRKRFJidz09
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.