The Quantum Convolutional Neural Network (QCNN) is a quantum circuit model inspired by the architecture of Convolutional Neural Networks (CNNs). CNNs are successful because they do not need manual feature design and can learn high-level features from raw data. Neural … Weiterlesen →

Abstract: The success of modern deep learning hinges on the ability to train neural networks at scale. Through clever reuse of intermediate information, backpropagation facilitates training through gradient computation at a total cost roughly proportional to running the function, rather … Weiterlesen →

Title of talk: Universal cooling dynamics toward a quantum critical point Speaker: Emma King (present affiliation: University of Saarland, Saarbrücken, Germany; the work I will present was done at Stellenbosch University, Stellenbosch, South Africa). Abstract: Curiosity surrounding concepts such as … Weiterlesen →

https://www.youtube.com/watch?v=A7-zuKanVbQ&t=1290s

Title of talk: Quantum work statistics at strong reservoir coupling. Speaker: Dr Ahsan Nazir (University of Manchester, United Kingdom). Abstract: Calculating the stochastic work done on a quantum system while strongly coupled to a reservoir is a formidable task, requiring … Weiterlesen →

Click here to register

Title of talk: Structural crossovers in trapped ions dispersively coupled to optical cavities. Speaker: Alan Kahan. Abstract: We consider an optomechanical system composed of a trapped ion dispersively coupled to a single mode of a pumped optical cavity. We focus … Weiterlesen →

At today's Group Meeting Chanelle will speak about Qunatum Logistic Regression. Her presentation will be based on the  paper https://arxiv.org/abs/1906.03834v2