27 results on '"Schuld, Maria"'
Search Results
2. Early transmission of SARS-CoV-2 in South Africa: An epidemiological and phylogenetic report
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Giandhari, Jennifer, Pillay, Sureshnee, Wilkinson, Eduan, Tegally, Houriiyah, Sinayskiy, Ilya, Schuld, Maria, Lourenço, José, Chimukangara, Benjamin, Lessells, Richard, Moosa, Yunus, Gazy, Inbal, Fish, Maryam, Singh, Lavanya, Sedwell Khanyile, Khulekani, Fonseca, Vagner, Giovanetti, Marta, Carlos Junior Alcantara, Luiz, Petruccione, Francesco, and de Oliveira, Tulio
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- 2021
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3. On quantum ensembles of quantum classifiers
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Abbas, Amira, Schuld, Maria, and Petruccione, Francesco
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- 2020
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4. Machine learning in quantum spaces
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Schuld, Maria
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- 2019
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5. Generalization despite overfitting in quantum machine learning models
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Peters, Evan and Schuld, Maria
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Quantum Physics ,FOS: Physical sciences ,Quantum Physics (quant-ph) - Abstract
The widespread success of deep neural networks has revealed a surprise in classical machine learning: very complex models often generalize well while simultaneously overfitting training data. This phenomenon of benign overfitting has been studied for a variety of classical models with the goal of better understanding the mechanisms behind deep learning. Characterizing the phenomenon in the context of quantum machine learning might similarly improve our understanding of the relationship between overfitting, overparameterization, and generalization. In this work, we provide a characterization of benign overfitting in quantum models. To do this, we derive the behavior of a classical interpolating Fourier features models for regression on noisy signals, and show how a class of quantum models exhibits analogous features, thereby linking the structure of quantum circuits (such as data-encoding and state preparation operations) to overparameterization and overfitting in quantum models. We intuitively explain these features according to the ability of the quantum model to interpolate noisy data with locally "spiky" behavior and provide a concrete demonstration example of benign overfitting., 41 pages, 6 figures
- Published
- 2022
6. THE PREVALENCE OF VIOLENCE IN POST-CONFLICT SOCIETIES : A CASE STUDY OF KWAZULU-NATAL, SOUTH AFRICA
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SCHULD, MARIA
- Published
- 2013
7. Using word embeddings to investigate cultural biases.
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Durrheim, Kevin, Schuld, Maria, Mafunda, Martin, and Mazibuko, Sindisiwe
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SEMANTICS , *IMPLICIT bias , *NATURAL language processing , *PREJUDICES , *STEREOTYPES , *CULTURAL prejudices , *SOCIAL psychology - Abstract
Word embeddings provide quantitative representations of word semantics and the associations between word meanings in text data, including in large repositories in media and social media archives. This article introduces social psychologists to word embedding research via a consideration of bias analysis, a topic of central concern in the discipline. We explain how word embeddings are constructed and how they can be used to measure bias along bipolar dimensions that are comparable to semantic differential scales. We review recent studies that show how familiar social biases can be detected in embeddings and how these change over time and in conjunction with real‐world discriminatory practices. The evidence suggests that embeddings yield valid and reliable estimates of bias and that they can identify subtle biases that may not be communicated explicitly. We argue that word embedding research can extend scholarship on prejudice and stereotyping, providing measures of the bias environment of human thought and action. [ABSTRACT FROM AUTHOR]
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- 2023
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8. The quest for a Quantum Neural Network
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Schuld, Maria, Sinayskiy, Ilya, and Petruccione, Francesco
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- 2014
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9. Supervised quantum machine learning models are kernel methods
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Schuld, Maria
- Subjects
FOS: Computer and information sciences ,Quantum Physics ,Statistics - Machine Learning ,ComputerSystemsOrganization_MISCELLANEOUS ,FOS: Physical sciences ,Machine Learning (stat.ML) ,Quantum Physics (quant-ph) - Abstract
With near-term quantum devices available and the race for fault-tolerant quantum computers in full swing, researchers became interested in the question of what happens if we replace a supervised machine learning model with a quantum circuit. While such "quantum models" are sometimes called "quantum neural networks", it has been repeatedly noted that their mathematical structure is actually much more closely related to kernel methods: they analyse data in high-dimensional Hilbert spaces to which we only have access through inner products revealed by measurements. This technical manuscript summarises and extends the idea of systematically rephrasing supervised quantum models as a kernel method. With this, a lot of near-term and fault-tolerant quantum models can be replaced by a general support vector machine whose kernel computes distances between data-encoding quantum states. Kernel-based training is then guaranteed to find better or equally good quantum models than variational circuit training. Overall, the kernel perspective of quantum machine learning tells us that the way that data is encoded into quantum states is the main ingredient that can potentially set quantum models apart from classical machine learning models., 26 pages, 9 figures - Version 2 emphasises focus on supervised learning, adds more references to existing literature, deletes section on state discrimination due to a technical error, and updates the comparison between kernel-based and variational training
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- 2021
10. Quantum embeddings for machine learning
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Lloyd, Seth, Schuld, Maria, Ijaz, Aroosa, Izaac, Josh, and Killoran, Nathan
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Quantum Physics ,FOS: Physical sciences ,Quantum Physics (quant-ph) - Abstract
Quantum classifiers are trainable quantum circuits used as machine learning models. The first part of the circuit implements a quantum feature map that encodes classical inputs into quantum states, embedding the data in a high-dimensional Hilbert space; the second part of the circuit executes a quantum measurement interpreted as the output of the model. Usually, the measurement is trained to distinguish quantum-embedded data. We propose to instead train the first part of the circuit -- the embedding -- with the objective of maximally separating data classes in Hilbert space, a strategy we call quantum metric learning. As a result, the measurement minimizing a linear classification loss is already known and depends on the metric used: for embeddings separating data using the l1 or trace distance, this is the Helstrom measurement, while for the l2 or Hilbert-Schmidt distance, it is a simple overlap measurement. This approach provides a powerful analytic framework for quantum machine learning and eliminates a major component in current models, freeing up more precious resources to best leverage the capabilities of near-term quantum information processors., 11 pages, 6 figures; tutorial available at https://pennylane.ai/qml/app/tutorial_embeddings_metric_learning.html [Version 2 contains minor update]
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- 2020
11. PennyLane: Automatic differentiation of hybrid quantum-classical computations
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Bergholm, Ville, Izaac, Josh, Schuld, Maria, Gogolin, Christian, Ahmed, Shahnawaz, Ajith, Vishnu, Alam, M. Sohaib, Alonso-Linaje, Guillermo, Akashnarayanan, B., Asadi, Ali, Arrazola, Juan Miguel, Azad, Utkarsh, Banning, Sam, Blank, Carsten, Bromley, Thomas R., Cordier, Benjamin A., Ceroni, Jack, Delgado, Alain, Di Matteo, Olivia, Dusko, Amintor, Garg, Tanya, Guala, Diego, Hayes, Anthony, Hill, Ryan, Ijaz, Aroosa, Isacsson, Theodor, Ittah, David, Jahangiri, Soran, Jain, Prateek, Jiang, Edward, Ankit Khandelwal, Kottmann, Korbinian, Lang, Robert A., Lee, Christina, Loke, Thomas, Lowe, Angus, Mckiernan, Keri, Meyer, Johannes Jakob, Montañez-Barrera, J. A., Moyard, Romain, Niu, Zeyue, O Riordan, Lee James, Oud, Steven, Panigrahi, Ashish, Park, Chae-Yeun, Polatajko, Daniel, Quesada, Nicolás, Roberts, Chase, Sá, Nahum, Schoch, Isidor, Shi, Borun, Shu, Shuli, Sim, Sukin, Singh, Arshpreet, Strandberg, Ingrid, Soni, Jay, Száva, Antal, Thabet, Slimane, Vargas-Hernández, Rodrigo A., Vincent, Trevor, Vitucci, Nicola, Weber, Maurice, Wierichs, David, Wiersema, Roeland, Willmann, Moritz, Wong, Vincent, Zhang, Shaoming, and Killoran, Nathan
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FOS: Computer and information sciences ,Quantum Physics ,Computer Science - Machine Learning ,Emerging Technologies (cs.ET) ,FOS: Physical sciences ,Computer Science - Emerging Technologies ,Computational Physics (physics.comp-ph) ,Quantum Physics (quant-ph) ,Physics - Computational Physics ,Machine Learning (cs.LG) - Abstract
PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications., Code available at https://github.com/XanaduAI/pennylane/ . Significant contributions to the code (new features, new plugins, etc.) will be recognized by the opportunity to be a co-author on this paper
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- 2018
12. Quantum gradient descent and Newton's method for constrained polynomial optimization
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Rebentrost, Patrick, Schuld, Maria, Wossnig, Leonard, Petruccione, Francesco, and Lloyd, Seth
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Quantum Physics ,quantum optimization ,quantum computing ,density matrix exponentiation ,FOS: Physical sciences ,Quantum Physics (quant-ph) - Abstract
Optimization problems in disciplines such as machine learning are commonly solved with iterative methods. Gradient descent algorithms find local minima by moving along the direction of steepest descent while Newton's method takes into account curvature information and thereby often improves convergence. Here, we develop quantum versions of these iterative optimization algorithms and apply them to polynomial optimization with a unit norm constraint. In each step, multiple copies of the current candidate are used to improve the candidate using quantum phase estimation, an adapted quantum state exponentiation scheme, as well as quantum matrix multiplications and inversions. The required operations perform polylogarithmically in the dimension of the solution vector and exponentially in the number of iterations. Therefore, the quantum algorithm can be useful for high-dimensional problems where a small number of iterations is sufficient., New Journal of Physics, 21 (7), ISSN:1367-2630
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- 2016
13. Applications of near-term photonic quantum computers: software and algorithms.
- Author
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Bromley, Thomas R, Arrazola, Juan Miguel, Jahangiri, Soran, Izaac, Josh, Quesada, Nicolás, Gran, Alain Delgado, Schuld, Maria, Swinarton, Jeremy, Zabaneh, Zeid, and Killoran, Nathan
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- 2020
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14. Simulating a perceptron on a quantum computer
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Schuld, Maria, Sinayskiy, Ilya, and Petruccione, Francesco
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- 2015
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15. Quantum computing for pattern classification
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Schuld, Maria, Sinayskiy, Ilya, and Petruccione, Francesco
- Subjects
Quantum Physics ,FOS: Physical sciences ,Quantum Physics (quant-ph) - Abstract
It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of pattern classification. We introduce a quantum pattern classification algorithm that draws on Trugenberger's proposal for measuring the Hamming distance on a quantum computer (CA Trugenberger, Phys Rev Let 87, 2001) and discuss its advantages using handwritten digit recognition as from the MNIST database., 14 pages, 3 figures, presented at the 13th Pacific Rim International Conference on Artificial Intelligence
- Published
- 2014
16. Voting and violence in KwaZulu-Natal’s no-go areas: Coercive mobilisation and territorial control in post-conflict elections
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Schuld, Maria
- Abstract
Post-confl ict elections have become an important tool of international confl ict resolution over the last decades. Theoretical studies usually point out that in warto- democracy transitions, military logics of territorial control are transformed into electoral logics of peaceful political contestation. Empirical reality, however, shows that the election process is often accompanied by various forms of violence. This paper analyses post-confl ict elections in war-to-democracy transitions by comparing support structures for confl ict parties as well as their coercive mobilisation strategies in times of violent confl ict and post-confl ict elections. It does so through a single case study of KwaZulu-Natal. This South African province faced a civil war-scale political confl ict in the 80s and early 90s in which the two fi ghting parties – the African National Congress (ANC) and the Inkatha Freedom Party (IFP) – used large-scale violence to establish and protect no-go areas of territorial control. This study finds that in the first decade after South Africa’s miraculous transition, these spatial structures of violence and control persisted at local levels. Violent forms of mobilisation and territorial control thus seem to be able to survive even a successful transition to democracy by many years. Measures to open up the political landscape, deescalate heated-up party antagonisms and overcome geopolitical borders of support structures seem to be crucial elements for post-conflict elections that introduce a pluralist democracy beyond the voting process.African Journal on Conflict Resolution,Volume 13, Number 1, 2013
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- 2013
17. An introduction to quantum machine learning.
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Schuld, Maria, Sinayskiy, Ilya, and Petruccione, Francesco
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MACHINE learning , *INFORMATION technology industry , *QUANTUM computing , *QUANTUM computers , *IMAGE recognition (Computer vision) , *AUTOMATIC speech recognition - Abstract
Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning. [ABSTRACT FROM PUBLISHER]
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- 2015
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18. Quantum walks on graphs representing the firing patterns of a quantum neural network.
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Schuld, Maria, Sinayskiy, Ilya, and Petruccione, Francesco
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QUANTUM mechanics , *RANDOM walks , *QUANTUM networks (Optics) , *ASSOCIATIVE storage , *QUBITS , *QUANTUM computing - Abstract
Quantum walks have been shown to be fruitful tools in analyzing the dynamic properties of quantum systems. This article proposes using quantum walks as an approach to quantum neural networks (QNNs). QNNs replace binary McCulloch-Pitts neurons with a qubit in order to use the advantages of quantum computing in neural networks. A quantum walk on the firing states of such a QNN is supposed to simulate the central properties of the dynamics of classical neural networks, such as associative memory. It is shown that a biased discrete Hadamard walk derived from the updating process of a biological neuron does not lead to a unitary walk. However, a stochastic quantum walk between the global firing states of a QNN can be constructed, and it is shown that it contains the feature of associative memory. The quantum contribution to the walk accounts for a modest speedup in some regimes. [ABSTRACT FROM AUTHOR]
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- 2014
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19. Quantum Machine Learning in Feature Hilbert Spaces.
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Schuld, Maria and Killoran, Nathan
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MACHINE learning , *HILBERT space , *QUANTUM computing - Abstract
A basic idea of quantum computing is surprisingly similar to that of kernel methods in machine learning, namely, to efficiently perform computations in an intractably large Hilbert space. In this Letter we explore some theoretical foundations of this link and show how it opens up a new avenue for the design of quantum machine learning algorithms. We interpret the process of encoding inputs in a quantum state as a nonlinear feature map that maps data to quantum Hilbert space. A quantum computer can now analyze the input data in this feature space. Based on this link, we discuss two approaches for building a quantum model for classification. In the first approach, the quantum device estimates inner products of quantum states to compute a classically intractable kernel. The kernel can be fed into any classical kernel method such as a support vector machine. In the second approach, we use a variational quantum circuit as a linear model that classifies data explicitly in Hilbert space. We illustrate these ideas with a feature map based on squeezing in a continuous-variable system, and visualize the working principle with two-dimensional minibenchmark datasets. [ABSTRACT FROM AUTHOR]
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- 2019
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20. New OCC chief should encourage banks, fintechs to partner with CDFIs.
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Schuld, Maria
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BANKING industry ,VENTURE capital ,CREDIT scoring systems - Abstract
There are currently 143 banks and 511 credit unions across the U.S. with collective assets of nearly $300 billion that are either owned or directed primarily by Black, Asian, Latinx or Native Americans. From a regulatory standpoint, initiatives like the OCC's Project REACh (Roundtable for Economic Access and Change) aims at building partnerships to help minority-owned banks thrive and support the communities they serve. [Extracted from the article]
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- 2021
21. Helping community banks do what they do best.
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SCHULD, MARIA
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COMMUNITY banks ,MOBILE banking industry ,ONLINE banking ,BANK customers - Published
- 2020
22. Prediction by linear regression on a quantum computer.
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Schuld, Maria, Sinayskiy, Ilya, and Petruccione, Francesco
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MACHINE learning , *QUANTUM computing , *APPROXIMATION theory - Abstract
We give an algorithm for prediction on a quantum computer which is based on a linear regression model with least-squares optimization. In contrast to related previous contributions suffering from the problem of reading out the optimal parameters of the fit, our scheme focuses on the machine-learning task of guessing the output corresponding to a new input given examples of data points. Furthermore, we adapt the algorithm to process nonsparse data matrices that can be represented by low-rank approximations, and significantly improve the dependency on its condition number. The prediction result can be accessed through a single-qubit measurement or used for further quantum information processing routines. The algorithm's runtime is logarithmic in the dimension of the input space provided the data is given as quantum information as an input to the routine. [ABSTRACT FROM AUTHOR]
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- 2016
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23. Small banks don't have to take a backseat to fintechs.
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Schuld, Maria
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COMMUNITY banks ,MOBILE banking industry ,ONLINE banking ,BANK customers ,BATCH processing - Abstract
Community banks will never be able to keep up with startups and online-only banks - or so pundits might have you believe. Every bank is different, but these are the four investments no community bank can afford to ignore. [Extracted from the article]
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- 2020
24. Community banks can win the tech arms race.
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Schuld, Maria
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COMMUNITY banks ,ARMS race ,MOBILE banking industry ,ONLINE banking ,BATCH processing - Abstract
Community banks will never be able to keep up with startups and online-only banks -- or so pundits might have you believe. Every bank is different, but these are the four investments no community bank can afford to ignore. [Extracted from the article]
- Published
- 2020
25. Early transmission of SARS-CoV-2 in South Africa: An epidemiological and phylogenetic report.
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Giandhari J, Pillay S, Wilkinson E, Tegally H, Sinayskiy I, Schuld M, Lourenco J, Chimukangara B, Lessells R, Moosa Y, Gazy I, Fish M, Singh L, Khanyile KS, Fonseca V, Giovanetti M, Alcantara LC, Petruccione F, and de Oliveira T
- Abstract
Background: The emergence of a novel coronavirus, SARS-CoV-2, in December 2019, progressed to become a world pandemic in a few months and reached South Africa at the beginning of March. To investigate introduction and understand the early transmission dynamics of the virus, we formed the South African Network for Genomics Surveillance of COVID (SANGS_COVID), a network of ten government and university laboratories. Here, we present the first results of this effort, which is a molecular epidemiological study of the first twenty-one SARS-CoV-2 whole genomes sampled in the first port of entry, KwaZulu-Natal (KZN), during the first month of the epidemic. By combining this with calculations of the effective reproduction number (R), we aim to shed light on the patterns of infections that define the epidemic in South Africa., Methods: R was calculated using positive cases and deaths from reports provided by the four major provinces. Molecular epidemiology investigation involved sequencing viral genomes from patients in KZN using ARCTIC protocols and assembling whole genomes using meticulous alignment methods. Phylogenetic analysis was performed using maximum likelihood (ML) and Bayesian trees, lineage classification and molecular clock calculations., Findings: The epidemic in South Africa has been very heterogeneous. Two of the largest provinces, Gauteng, home of the two large metropolis Johannesburg and Pretoria, and KwaZulu-Natal, home of the third largest city in the country Durban, had a slow growth rate on the number of detected cases. Whereas, Western Cape, home of Cape Town, and the Eastern Cape provinces the epidemic is spreading fast. Our estimates of transmission potential for South Africa suggest a decreasing transmission potential towards R=1 since the first cases and deaths have been reported. However, between 06 May and 18 May 2020, we estimate that R was on average 1.39 (1.04 - 2.15, 95% CI). We also demonstrate that early transmission in KZN, and most probably in all main regions of SA, was associated with multiple international introductions and dominated by lineages B1 and B. The study also provides evidence for locally acquired infections in a hospital in Durban within the first month of the epidemic, which inflated early mortality in KZN., Interpretation: This first report of SANGS_COVID consortium focuses on understanding the epidemic heterogeneity and introduction of SARS-CoV-2 strains in the first month of the epidemic in South Africa. The early introduction of SARS-CoV-2 in KZN included caused a localized outbreak in a hospital, provides potential explanations for the initially high death rates in the province. The current high rate of transmission of COVID-19 in the Western Cape and Eastern Cape highlights the crucial need to strength local genomic surveillance in South Africa., Funding: UKZN Flagship Program entitled: Afrocentric Precision Approach to Control Health Epidemic, by a research Flagship grant from the South African Medical Research Council (MRC-RFA-UFSP-01-2013/UKZN HIVEPI, by the the Technology Innovation Agency and the the Department of Science and Innovation and by National Human Genome Re- search Institute of the National Institutes of Health under Award Number U24HG006941. H3ABioNet is an initiative of the Human Health and Heredity in Africa Consortium (H3Africa).
- Published
- 2020
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26. The future of quantum biology.
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Marais A, Adams B, Ringsmuth AK, Ferretti M, Gruber JM, Hendrikx R, Schuld M, Smith SL, Sinayskiy I, Krüger TPJ, Petruccione F, and van Grondelle R
- Subjects
- Quantum Theory, Biophysics trends, Systems Biology trends
- Abstract
Biological systems are dynamical, constantly exchanging energy and matter with the environment in order to maintain the non-equilibrium state synonymous with living. Developments in observational techniques have allowed us to study biological dynamics on increasingly small scales. Such studies have revealed evidence of quantum mechanical effects, which cannot be accounted for by classical physics, in a range of biological processes. Quantum biology is the study of such processes, and here we provide an outline of the current state of the field, as well as insights into future directions., (© 2018 The Author(s).)
- Published
- 2018
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27. Quantum ensembles of quantum classifiers.
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Schuld M and Petruccione F
- Abstract
Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.
- Published
- 2018
- Full Text
- View/download PDF
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