Prof Uwe Jaekel (Hochshule Koblenz, University of Applied Sciences) is visiting Stellenbosch University from 31 January to 5 February 2023.
Prof Jaekel is one of the speakers at the 2023 CHPC-NITheCS Summer School on Theoretical and Computational Sciences.
On Friday, 3 February, he will deliver a Colloquium on the topic ‘Solving nonlinear classification problems with a complex valued almost linear perceptron”.
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 Architecture Search (NAS) builds on this success by learning network architecture and achieves state-of-the-art performance. However, NAS requires the design of a search space, which is currently not possible for QCNNs as no formal framework exists to capture its design elements. In this work, we provide such a framework by using techniques from NAS to create a hierarchical representation for QCNN architectures. Using this framework, we generate a family of popular QCNNs, those resembling reverse binary trees. We then evaluate this family of models on a music genre classification dataset, GTZAN, showing that alternating architecture has a greater impact on model performance than other modelling components, such as the choice of unitary ansatz and data encoding. Our framework provides a way to improve model performance without increasing complexity and to jump around the cost landscape to avoid barren plateaus. Finally, we implement the framework as an open-source Python package to enable dynamic QCNN creation and facilitate QCNN search space design for NAS.
It was great to have some of my UKZN students visiting in Stellenbosch and join the local team.
The Quantum Unconference brings to Stellenbosch some of my UKZN students. We will go through the drafts of their papers and their theses.
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 investigation is to compare the ML algorithm’s performance on tweet identification and use the findings to recommend an optimal way of solving the natural language processing task of automatically assigning labels to unseen tweets in particular or texts in general. The ML classification algorithms considered are the Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), Multi-layer Perceptron (MLP), Decision trees (DT), Naive Bayes (NB) and Discriminant Analysis (DA). The highest F-score value of 79% is reported for SVM, MLP and DT algorithms, while the least F-score value of 63% is reported for the NB algorithm. Analysis of model misclassifications revealed that there is a huge window of opportunity to develop state-of-the-art tweet classification systems by combining the potential of these six classification algorithms to produce an ensemble tweet classification system. Using an ensemble model could potentially stabilize the proportion of misclassified tweets to 39% (anti-Zuma), 39% (pro-Zuma) and 22% (neutral). The findings of this investigation are significant in that they form the basis for creating innovative data mining techniques under limited resource settings, e.g. time and computational resources.
Keywords: Support Vector Machines, K-Nearest Neighbors, Multi-Layer Perceptron, Discriminant Analysis, Naive Bayes, tweet classification system, ensemble model.
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 will reconstruct the story of quantum entanglement from the early days of quantum mechanics to today’s efforts to build quantum computers.
The formal announcement can be accessed below:
The latest paper with Betony Adams, Ilya Sinayskiy and Rienk van Grondelle was published in Scientific Reports.
A simplified illustration of vibration assisted tunnelling in the context of SARS-CoV-2 infection. The spike protein vibrational spectrum matches the energy of transition for an electron in the ACE2 receptor, facilitating electron transfer and the activation of the receptor.
Abstract:
The SARS‐CoV‐2 pandemic has added new urgency to the study of viral mechanisms of infection. But while vaccines offer a measure of protection against this specific outbreak, a new era of pandemics has been predicted. In addition to this, COVID‐19 has drawn attention to post‐viral syndromes and the healthcare burden they entail. It seems integral that knowledge of viral mechanisms is increased through as wide a research field as possible. To this end we propose that quantum biology might offer essential new insights into the problem, especially with regards to the important first step of virus‐ host invasion. Research in quantum biology often centres around energy or charge transfer. While this is predominantly in the context of photosynthesis there has also been some suggestion that cellular receptors such as olfactory or neural receptors might employ vibration assisted electron tunnelling to augment the lock‐and‐key mechanism. Quantum tunnelling has also been observed in enzyme function. Enzymes are implicated in the invasion of host cells by the SARS‐CoV‐2 virus. Receptors such as olfactory receptors also appear to be disrupted by COVID‐19. Building on these observations we investigate the evidence that quantum tunnelling might be important in the context of infection with SARS‐CoV‐2. We illustrate this with a simple model relating the vibronic mode of, for example, a viral spike protein to the likelihood of charge transfer in an idealised receptor. Our results show a distinct parameter regime in which the vibronic mode of the spike protein enhances electron transfer. With this in mind, novel therapeutics to prevent SARS‐CoV‐2 transmission could potentially be identified by their vibrational spectra.
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.
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”.
