Abstract: Quantum-access security, where an attacker is granted superposition access to secret keyed functionalities, is a fundamental security model and its study has inspired results in post-quantum security. We revisit, and fill a gap in, the quantum-access security analysis of … Weiterlesen
Weekly group meeting
Abstract: In this study, a hybrid tweet classification system built using Word2Vec and Support Vector Machines is presented. The aim of the study is to use the tweet classification system to solve the natural language processing task of automatically assigning … Weiterlesen
The 6th edition of the Qunatum Africa Conference series will take place from 12 to 16 September 2022. To register please click here.
In today's group meeting Dr Graeme Pleasance will speak about his recent work on "Pseudomode description of general open quantum system dynamics with applications to heat transport".
Prof Stefan Lotz (North-West University & SANSA) will speak about Knowledge Discovery in Time Series Data. For further information please visit the NITheCS website To register for the webinar please click here.
Entanglement: from theory to quantum computing Francesco Petruccione (Stellenbosch University and NITheCS) Abstract: The Nobel Prize in Physics 2022 was awarded to Alain Aspect, John Clauser and Anton Zeilinger for conducting groundbreaking experiments using entangled quantum states. In this lecture, we … Weiterlesen
Comparison of machine learning algorithms for an automated tweet identification system Martin Canaan Mafunda (UKZN) Abstract: In this study, six machine learning algorithms are trained based on 3100 tweets with either pro-Zuma, anti-Zuma or neutral sentiments. The aim of the … Weiterlesen
The Quantum Unconference brings to Stellenbosch some of my UKZN students. We will go through the drafts of their papers and their theses.
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
