What a day: 4 preprints in the arXiv!
Congratulations to Shivani Pillay, Ian David, Rowan Pellow-Jarman and Shane McFarthing for submitting their first preprints to the arXiv:
Hybrid Genetic Optimisation for Quantum Feature Map Design R Pellow-Jarman, A Pillay, I Sinayskiy, F Petruccione
Digital Simulation of Single Qubit Markovian Open Quantum Systems: A Tutorial , IJ David, I Sinayskiy, F Petruccione
A Multi-Class SWAP-Test Classifier, SM Pillay, I Sinayskiy, E Jembere, F Petruccione
Classical Ensembles of Single-Qubit Quantum Variational Circuits for Classification, S McFarthing, A Pillay, I Sinayskiy, F Petruccione
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”.
It was great to have some of my UKZN students visiting in Stellenbosch and join the local team.
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.
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.
Adams, B., Sinayskiy, I., van Grondelle, R. et al. Quantum tunnelling in the context of SARS-CoV-2 infection. Sci Rep 12, 16929 (2022).
We are very happy to welcome Graeme Pleasance as a PostDoc at Stellenbosch University. Graeme joined us on Monday 9 September 2022.
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
My first paper with Stellenbosch University affiliation was published in Quantum Science and Technology.
Abstract: Quantum computing opens exciting opportunities for kernel-based machine learning methods, which have broad applications in data analysis. Recent works show that quantum computers can efficiently construct a model of a classifier by engineering the quantum interference effect to carry out the kernel evaluation in parallel. For practical applications of these quantum machine learning methods, an important issue is to minimize the size of quantum circuits. We present the simplest quantum circuit for constructing a kernel-based binary classifier. This is achieved by generalizing the interference circuit to encode data labels in the relative phases of the quantum state and by introducing compact amplitude encoding, which encodes two training data vectors into one quantum register. When compared to the simplest known quantum binary classifier, the number of qubits is reduced by two and the number of steps is reduced linearly with respect to the number of training data. The two-qubit measurement with post-selection required in the previous method is simplified to single-qubit measurement. Furthermore, the final quantum state has a smaller amount of entanglement than that of the previous method, which advocates the cost-effectiveness of our method. Our design also provides a straightforward way to handle an imbalanced data set, which is often encountered in many machine learning problems.
The latest paper with Vinayak Jagadish and R. Srikanth was just published in Physical Review A.
Prof Francesco Petruccione joined Stellenbosch University on 1 May 2022.
Welcome to quantum.sun.ac.za. The page will display all things “quantum” at Stellenbosch University.