This publication is based on studies of The Moravian Masaryk University, Professor RNDr. Josef Havel, Dr.Sc., from Department of Analytical Chemistry and Department of Physical Electronics. The significant subject of the work is an application of ANN (ARTIFICIAL NEURAL NETWORK, with MALDI–TOF SPECTROMETRY, HPLC (High Pressure Liquid Chromatography), Electrophoresis and Research of AAS (ATOMIC ABSORPTION SPECTROSCOPY). This text says about research of neutrinos and photons, wimp’s like an application of construction of QUANTUM ANN COMPUTER. The Article consists of study an introduction of Author and Prof. RNDr. J. Havel, Dr.Sc. theme of mineralogy and biology and geography and scientific cooperation from the year 2004, study generally could be known like soil computation. The second part of this publication is dedicated to short History of Computational Science. The third part says about Highlights of this article, concretely Author’s sketches of a quantum computer. The fourth part talks about results from continual measuring of statistical data from project SAGE 37Ar neutrino source experiment (SAGE -> SOVIET–AMERICAN– 71GERMANIUM–71GALIUM EXPERIMENT IN CAUCASCUS BAKSAN. Fifth part is connected with METHODS (KATRIN AND TROITSK NU–MASS). Sixth part is focused to conclusions of Research of neutrinos and other quanticles, namely photon proton, photon neutrino, neutrino photon and wimp’s.
This publication is based on studies of The Moravian Masaryk University, Professor RNDr. Josef Havel, Dr.Sc., from Department of Analytical Chemistry and Department of Physical Electronics. The significant subject of the work is an application of ANN (ARTIFICIAL NEURAL NETWORK, with MALDI–TOF SPECTROMETRY, HPLC (High Pressure Liquid Chromatography), Electrophoresis and Research of AAS (ATOMIC ABSORPTION SPECTROSCOPY). This text says about research of neutrinos and photons, wimp’s like an application of construction of QUANTUM ANN COMPUTER. The Article consists of study an introduction of Author and Prof. RNDr. J. Havel, Dr.Sc. theme of mineralogy and biology and geography and scientific cooperation from the year 2004, study generally could be known like soil computation. The second part of this publication is dedicated to short History of Computational Science. The third part says about Highlights of this article, concretely Author’s sketches of a quantum computer. The fourth part talks about results from continual measuring of statistical data from project SAGE 37Ar neutrino source experiment (SAGE -> SOVIET–AMERICAN– 71GERMANIUM–71GALIUM EXPERIMENT IN CAUCASCUS BAKSAN. Fifth part is connected with METHODS (KATRIN AND TROITSK NU–MASS). Sixth part is focused to conclusions of Research of neutrinos and other quanticles, namely photon proton, photon neutrino, neutrino photon and wimp’s.
This thesis investigates the application of machine-learning methods in the context of quantum computing and neutrino physics, with particular emphasis on the construction of effective representations for complex, high-dimensional data. The first part of the work is devoted to Quantum Extreme Learning Machines (QELMs), a hybrid quantum--classical framework in which classical data are encoded into quantum states and processed through fixed quantum dynamics, while learning is performed by a classical readout layer. Within this framework, we analyze the role of encoding strategies, feature-reduction methods, Hamiltonian structure, and measurement, with particular focus on the relationship between quantum dynamics, expressivity, entanglement, and classical simulability. The second part of the thesis concerns the application of deep learning to the analysis of images produced by water Cherenkov detectors in neutrino physics. Convolutional architectures, including residual networks, are developed for the classification of complex events in realistic simulated datasets, showing that such models can effectively extract relevant information from detector data. Taken together, these results highlight the potential of machine learning, in both its classical and quantum forms, as a powerful framework for the analysis of complex data in fundamental physics, while also outlining relevant challenges and directions for future research.
Artificial neural networks (ANNs) and quantum computing (QC) have both received rapidly increasing interest in recent years for several reasons. Their roles in the mechanics community remain a subject of ongoing debate and research. ANNs have successfully been employed on problems previously considered unsolvable ( Silver et al. 2016 ), as well as on practical and illustrative problems especially in the image recognition domain ( Szegedy et al. 2013 , among others). The most straightforward way to interpret the behavior of ANNs is focusing on their ability to approximate any continuous function to arbitrary precision ( Kreinovich 1991 ). The adjustment to a given function happens automatically except for hyperparameters, such as the number of hidden layers or the learning rate, which remain constant during training. This makes them a perfect tool for reducing computational cost in otherwise time-consuming simulations. Quantum computing on the other hand has received some publicity in 2018 mainly because of a paper published by Google Quantum A.I. Lab scientists defining quantum supremacy ( Boixo & Isakov 2018 ), where it is shown that a fully working universal quantum computer with only 50 qubits will outperform any near-term realizable classical supercomputer on certain tasks. This publication was followed up by Google revealing Bristlecone ( Kelly 2018 ), a quantum processor consisting of 72 qubits. While many interesting quantum algorithms have proven to run polynomially or even exponentially faster than their classical analogons ( Montanaro 2016 ), these results only hold true for a decoherence-free machine. Therefore, to actually achieve quantum supremacy , quantum error correction schemes need to be implemented, requiring several physical qubits to make up a single logical qubit suitable for computation.
Much has been talked about quantum computers. Today they are working on difficult simulations, so we can already congratulate the college of researchers at the Oak Ridge National Laboratory as the world's first to successfully simulate an atomic nucleus using a quantum computer. The results that were published in the physical examination letters demonstrate the huge capacity of quantum systems to calculate the problems of nuclear physics and to be able to be successfully used in the future for special and complex simulations. It is already known that quantum calculus, which is based on the quantum principles of matter and which was proposed by American physicist Richard Feynman in the early 1980s, unlike the ordinary bits of normal computers today, uses the qubit units used by quantum computers to store information in two-state systems, such as electrons or photons, considered simultaneously in all possible quantum states (essentially a phenomenon known as overlapping). In October 2017, a multidisciplinary ORNL team began working on codes to perform various simulations on quantum computers such as IBM QX5 and Righetti 19Q within a DOE Quantum Testbed Pathfinder project in an effort to verify and validate various scientific applications on different types of quantum hardware. Today, quantum computers have potential applications in cryptography, artificial intelligence and weather forecasting, as each additional qubit clings in some way inextricably from the others, thus exponentially increasing the number of possible results for the final measured state. Obviously, this huge benefit also has negative effects on the system, because errors can in turn exponentially increase the size of the problem and the final results even. However, it is hoped that in the future on the basis of much-improved hardware, researchers will be able to solve problems that cannot be solved today using current calculation methods. In the present paper the authors propose an original method of rapid theoretical determination with great precision of the dimensions of all elementary particles, the method which, although predicting results similar to those obtained by other theoreticians, still has the advantage of its simplicity, of its general applicability, that is of its universality and especially this new method is a precious tool in the work of scientists worldwide to understand matter at its elementary level. Even though we would have expected that the size of a deuteron would be larger (double) than that of one component particle (of a proton or neutron), at the same velocity, the theory (20-21) demonstrates the opposite. By fusing two free nucleons, a proton and a neutron, a deuterium nucleus is obtained with smaller dimensions than its two components, which makes the energy of rejection between two Deuteron cores much larger than we would have waited. That's why the fusion reaction between two Deuterons is even more difficult than was suspected, even when they are accelerating.
Artificial neural networks (ANNs) have gained significant attention in various fields due to their ability to learn from data and extract meaningful patterns. Quantum machine learning (QML) has opened new opportunities for ANNs, with the advent of quantum computing. This chapter provides an introduction to the use of ANNs in QML, discussing fundamentals of QML and quantum mechanics, adaptability of ANN components, quantum-inspired classical ANNs, quantum perceptrons, activation functions, quantum hidden layers, backpropagation, quantum convolutional neural networks (CNNs), and quantum feed-forward neural networks (FNNs). The chapter also discusses the potential impact of quantum ANNs on industries and research disciplines, including quantum chemistry, quantum error correction, and quantum machine learning for quantum computers. The chapter also discusses limitations of quantum ANNs, such as noise and the need for large-scale quantum processors, and future directions for quantum ANNs, such as hybrid models and quantum neural networks with machine learning acceleration.
Recently, there has been a rapid increase interest in quantum computers. Their work is based on the use of quantum-mechanical phenomena such as superposition and entanglement for computing to transform input data into outputs, which can actually provide effective performance 3-4 orders of magnitude higher than any modern computing devices. In the past few decades, there has been an acute problem of creating a quantum computer that uses quantum mechanical effects for computations. Currently, there are prototypes of devices of this class, but it is not yet possible to achieve an effective solution to the planned tasks with their help due to a number of difficulties in implementation. However, there is a mathematical apparatus that describes the behavior of quantum particles using a wave function. Hence, it becomes possible to simulate quantum computations on the classical architecture and check the reliability of algorithms belonging to the NP class, which are theoretically executed on a quantum computer in polynomial time and which cannot be solved on classical computers within an acceptable time frame. Effectively simulate the operation of a quantum system on devices with classical architecture is impossible, it remains to propose methods for acceleration of quantum computing on classical systems.
This Tech–Science text of article dedicated to the QUANTUM ACOUSTIC Sensu Stricto says about phonons, the sound quazi–particles elements, which are very interesting in nano, micro, makro scales of nature. This New Concept of QUANTUM ACOUSTIC PHYSICS OR QUANTUM PHYSICS OF SOUND IS ILLUSTRATED ON EMPIRIO–CRITICISM PHILOSOPHY OF EXPERIMENTAL PHYSICS OF MORAVIAN BORN PROFESSOR ERNST MACH, the text operated with subjects like MACH’S NUMBER, MACH’S CONE, MACH’S PRINCIPLE, MACH’S WAVEMACHINE. The Highlights is focused on Author’s Sketches in problematic connected with AERODYNAMICS AND AERONAUTICS.