Wisconsin Quantum Institute

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Events During the Week of April 24th through May 1st, 2022

Monday, April 25th, 2022

No events scheduled

Tuesday, April 26th, 2022

IQUIST seminar: Qubit symmetries, combinatorial designs, and a rainbow of four basic colors: Common Patterns
Time: 11:00 am - 12:00 pm
Speaker: A. Ravi P. Rau, Louisiana State University
Abstract: A recreational puzzle posed 175 years ago of 15 schoolgirls to walk three abreast to school for seven days of the week so that no girl sees a friend repeated in her row has links to many areas of mathematics: combinatorics, finite projective geometries, design and coding theory, etc. A link can also be made to states and operators of two qubits in today's quantum information, those Lie algebras and groups also providing a systematic way to get the required arrangements of the girls. These patterns can be further linked to four-color vision and analogs in acoustics. They may be useful for

manipulating states and operators of a pair of qubits, with generalization also to multiple qubits. The Bloch Sphere is a well-known and very useful geometrical picture of a single qubit. Similar geometries and pictures will be discussed for two or more qubits. This will be an easily accessible pedagogical presentation, also to students, undergraduate and graduate.
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Wednesday, April 27th, 2022

Quantum field theory and deep neural networks
Time: 11:00 am - 12:15 pm
Place: Chamberlin 5280 (Zoom link for those attending online: )
Speaker: Ro Jefferson, Nordita
Abstract: Recently, exciting progress has been made in the study of deep neural networks (DNNs) by applying ideas and techniques from physics, and in particular QFT. In this talk, I will first give a brief overview of some key aspects of the approach to DNNs from effective theory, and highlight the information-theoretic language that unites these two seemingly disparate fields. Then, I will explain how one can go beyond the level of analogy by explicitly constructing a bona-fide QFT corresponding to a general class of DNNs encompassing both recurrent and feedforward architectures. The resulting theory closely resembles the well-studied O(N) vector model, in which the variance of the weight initializations plays the role of the 't Hooft coupling. In this framework, the Gaussian process approximation used in machine learning corresponds to a free field theory, and finite-width effects can be computed perturbatively in the ratio of depth to width, T/N. These provide corrections to the correlation length that controls the depth to which information can propagate through the network, and thereby sets the scale at which such networks are trainable by gradient descent. This analysis provides a first-principles approach to the rapidly emerging NN-QFT correspondence, and opens several interesting avenues to the study of criticality in deep neural networks. Based on 2109.13247 with Kevin T. Grosvenor.
Host: Gary Shiu
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Thursday, April 28th, 2022

No events scheduled

Friday, April 29th, 2022

No events scheduled