The Quantum Unconference brings to Stellenbosch some of my UKZN students. We will go through the drafts of their papers and their theses.
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Weekly Quantum Group Webinar: Martin Canaan Mafunda, Comparison of machine learning algorithms for automated tweet identification system
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 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.
Public Lecture at AIMS: Francesco Petruccione, Entanglement: from Theory to Quantum Computers
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 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:
Quantum tunnelling in the context of SARS‐CoV‐2 infection
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.
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2022-09-19 NITheCS Colloquium
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.
2022-09-16 Group Meeting: Dr Graeme Pleasance (SU):Pseudomode description of general open quantum system dynamics with applications to heat transport
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”.
Dr Graeme Pleasance joins Quantum@SUN
We are very happy to welcome Graeme Pleasance as a PostDoc at Stellenbosch University. Graeme joined us on Monday 9 September 2022.
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Machine learning for excitation energy transfer dynamics
The latest paper with Kimara Naicker and Ilya Sinayskiy was just published in Physical Review Research.
Abstract: A wellknown approach to describe the dynamics of an open quantum system is to compute the master equation evolving the reduced density matrix of the system. This approach plays an important role in describing excitation transfer through photosynthetic light harvesting complexes (LHCs). The hierarchical equations of motion (HEOM) was adapted by Ishizaki and Fleming [J. Chem. Phys.130, 234111 (2009)] to simulate open quantum dynamics in the biological regime. We generate a set of time-dependent observables that depict the coherent propagation of electronic excitations through the LHCs by solving the HEOM. The computationally intractable problem here is addressed using classical machine learning (ML). The ML architecture constructed here is of model character and it is used to solve the inverse problem for open quantum systems within the HEOM approach. The objective is to determine whether a trained ML model can perform Hamiltonian tomography by using the time dependence of the observables as inputs. We demonstrate the capability of convolutional neural networks to tackle this research problem. The models developed here can predict Hamiltonian parameters such as excited state energies and inter-site couplings of a system up to 99.28% accuracy.
Reference: Kimara Naicker, Ilya Sinayskiy, and Francesco Petruccione, Machine learning for excitation energy transfer dynamics, Phys. Rev. Research 4, 033175 – Published 6 September 2022
The pdf can be downloaded from here
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Quantum Africa 6 (Kigali, Rwanda and online)
The 6th edition of the Qunatum Africa Conference series will take place from 12 to 16 September 2022.
To register please click here.