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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Quantum Science and Technology at Stellenbosch University

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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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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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.

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The latest paper with Vinayak Jagadish and R. Srikanth was just published in Physical Review A.

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Prof Francesco Petruccione joined Stellenbosch University on 1 May 2022.

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Welcome to quantum.sun.ac.za. The page will display all things “quantum” at Stellenbosch University.