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The Theory of Open Quantum Systems
PREFACE ACKNOWLEDGEMENTS PART 1: PROBABILITY IN CLASSICAL AND QUANTUM MECHANICS 1. Classical probability theory and stochastic processes 2. Quantum Probability PART 2: DENSITY MATRIX THEORY 3.
Detection of a SARS-CoV-2 variant of concern in South Africa
TLDR
A newly arisen lineage of SARS-CoV-2 is described that is defined by eight mutations in the spike protein, including three substitutions at residues in its receptor-binding domain that may have functional importance.
Open Quantum Random Walks
A new model of quantum random walks is introduced, on lattices as well as on finite graphs. These quantum random walks take into account the behavior of open quantum systems. They are the exact
The quest for a Quantum Neural Network
TLDR
This article presents a systematic approach to QNN research, concentrating on Hopfield-type networks and the task of associative memory, and outlines the challenge of combining the nonlinear, dissipative dynamics of neural computing and the linear, unitary dynamics of quantum computing.
An introduction to quantum machine learning
TLDR
This contribution gives a systematic overview of the emerging field of quantum machine learning and presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.
Sixteen novel lineages of SARS-CoV-2 in South Africa.
TLDR
The findings show that genomic surveillance can be implemented on a large scale in Africa to identify new lineages and inform measures to control the spread of SARS-CoV-2.
Emergence of a SARS-CoV-2 variant of concern with mutations in spike glycoprotein.
TLDR
A new SARS-CoV-2 lineage is described, characterised by eight lineage-defining mutations in the spike protein, including three at important residues in the receptor-binding domain that may have functional significance.
Circuit-Based Quantum Random Access Memory for Classical Data
TLDR
This work presents a circuit-based flip-flop quantum random access memory to construct a quantum database of classical information in a systematic and flexible way and presents a procedure to convert classical training data for a quantum supervised learning algorithm to a quantum state.
Quantum Computing for Pattern Classification
TLDR
A quantum pattern classification algorithm is introduced that draws on Trugenberger’s proposal for measuring the Hamming distance on a quantum computer and is discussed using handwritten digit recognition as from the MNIST database.
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