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The correlations between physical systems provide significant information about their collective behaviour – information that is used as a resource in many applications, e.g. communication protocols. However, when it comes to the exploitation of such correlations in the quantum world, identification of the associated ‘resource’ is extremely challenging and a matter of debate in the quantum community. This dissertation describes three key results on the identification, detection, and quantification of quantum correlations. It starts with an extensive and accessible introduction to the mathematical and physical grounds for the various definitions of quantum correlations. It subsequently focusses on introducing a novel unified picture of quantum correlations by taking a modern resource-theoretic position. The results show that this novel concept plays a crucial role in the performance of collaborative quantum computations that is not captured by the standard textbook approaches. Further, this new perspective provides a deeper understanding of the quantum-classical boundary and paves the way towards establishing a resource theory of quantum computations.
This thesis uses high-precision single-photon experiments to shed new light on the role of reality, causality, and uncertainty in quantum mechanics. It provides a comprehensive introduction to the current understanding of quantum foundations and details three influential experiments that significantly advance our understanding of three core aspects of this problem. The first experiment demonstrates that the quantum wavefunction is part of objective reality, if there is any such reality in our world. The second experiment shows that quantum correlations cannot be explained in terms of cause and effect, even when considering superluminal influences between measurement outcomes. The final experiment in this thesis demonstrates a novel uncertainty relation for joint quantum measurements, where the textbook relation does not apply.
This Worldwide List of Alternative Theories and Critics (only avalailable in english language) includes scientists involved in scientific fields. The 2023 issue of this directory includes the scientists found in the Internet. The scientists of the directory are only those involved in physics (natural philosophy). The list includes 9700 names of scientists (doctors or diplome engineers for more than 70%). Their position is shortly presented together with their proposed alternative theory when applicable. There are nearly 3500 authors of such theories, all amazingly very different from one another. The main categories of theories are presented in an other book of Jean de Climont THE ALTERNATIVE THEORIES
Traditional remedial technologies can be cost-prohibitive and sometimes contribute to environmental contamination themselves. In order to better manage the issues of global pollution, phytoremediation, a plant-based cleanup method, has gained attention as an efficient, affordable, and environmentally sustainable alternative to traditional remedial technologies for the cleanup of a variety of hazardous pollutants. The demand for advanced technologies having potential to sustainably manage waste and pollutants in the environment will help to continue the quest for more novel treatment methods. Sustainable Management of Environmental Pollutants through Phytoremediation discusses all the aspects of sustainable environmental management through phytoremediation, making it a valuable resource for both academics and researchers in developing and developed countries. Examines technology advancements made toward the recycling and management of waste. Designed in a way to cover scientific principles, modeling and methods, designs, and reference data. Discusses the utilization of waste for renewable energy for economic growth and further social benefits.
Vols. for 1963- include as pt. 2 of the Jan. issue: Medical subject headings.
A self-contained, graduate-level textbook that develops from scratch classical results as well as advances of the past decade.
Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.