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The question of whether Everettian quantum mechanics (EQM) justifies the existence of metaphysical indeterminacy has recently come to the fore. Metaphysical indeterminacy has been argued to emerge from three sources: coherent superpositions, the indefinite number of branches in the quantum multiverse and the nature of these branches. This paper reviews the evidence and concludes that those arguments don't rely on EQM alone and rest on metaphysical auxiliary assumptions that transcend the physics of EQM. We show how EQM can be ontologically interpreted without positing metaphysical indeterminacy by adopting a deflationary attitude towards branches. Two ways of developing the deflationary view are then proposed: one where branches are eliminated, and another where they are reduced to the universal quantum state.

Clustering algorithms are at the basis of several technological applications, and are fueling the development of rapidly evolving fields such as machine learning. In the recent past, however, it has become apparent that they face challenges stemming from datasets that span more spatial dimensions. In fact, the bestperforming clustering algorithms scale linearly in the number of points, but quadratically with respect to the local density of points. In this work, we introduce qCLUE, a quantum clustering algorithm that scales linearly in both the number of points and their density. qCLUE is inspired by CLUE, an algorithm developed to address the challenging time and memory budgets of Event Reconstruction (ER) in future High-Energy Physics experiments. As such, qCLUE marries decades of development with the quadratic speedup provided by quantum computers. We numerically test qCLUE in several scenarios, demonstrating its effectiveness and proving it to be a promising route to handle complex data analysis tasks - especially in high-dimensional datasets with high densities of points. [ABSTRACT FROM AUTHOR]

We report on the design and simulation of strip-loaded nanophotonic interfaces aimed at improving resonant coupling and photon routing efficiency. In our design, the guided mode is confined within a plane by a high-index thin film and is loosely confined laterally by a lower index strip. Using a hydrogen silsesquioxane (HSQ) strip, titanium dioxide core, and silicon dioxide substrate, we optimise the waveguide dimensions for maximum lateral confinement of light. Specifically, we propose a polymer-based Bragg grating cavity and ring resonator that achieve near-optimal mode volumes and high Q-factors. These may be further developed to achieve the even higher Q-factors demanded by quantum technologies. Our calculations suggest that a quantum dot embedded in a cavity with a mode volume of Veff ~ 7.0(λ/n)³ and a Q-factor of 7,000 can produce photons with 97% indistinguishability at 4K. Additionally, we investigate directional couplers for efficient photon routing, comparing photonic and plasmonic material structures. While pure photonic structures demonstrate lower loss and improved quality factors, they face practical limitations in terms of bending radius. Conversely, plasmonic structures offer shorter bending radii but higher propagation losses. This research lays the groundwork for future nanophotonic designs, aiming to enhance photon generation and routing capabilities for quantum optical applications. [ABSTRACT FROM AUTHOR]

Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is proposed. The algorithm introduces a quantum bit to initialize the population, which is then updated by the quantum rotation gate to enhance the convergence of the artificial bee colony algorithm (ABC). Sin chaos is introduced to process the individual positions, which improves the randomness and ergodicity of initializing individuals and results in a more diverse initial population. To overcome the upper limits of visiting target individual positions, Cauchy factor is used to mutate individuals for solving falling into local optimum problems. To evaluate the performance of the SCQABC, 20 classical benchmark functions and CEC-2017 are used. The practical engineering problems are also used to verify the practicability of SCQABC algorithm. Moreover, the experimental results will be compared with other well-known and progressive algorithms. According to the results, the SCQABC improves by 64.93% compared with the ABC and also has corresponding improvement compared with other algorithms. Its successful application to the robot gripper problem highlights its effectiveness in solving constrained optimization problems. [ABSTRACT FROM AUTHOR]

The primary emphasis of the present study is to introduce some novel characterizations of the interval-valued (I. V) right symmetric quantum derivative and antiderivative operators relying on generalized Hukuhara difference. To continue the study, we start with the concept of symmetric differentiability in the interval-valued sense and explore some important properties. Furthermore, through the utilization of the (I. V) symmetric derivative operator, we develop the right-sided (I. V) integral operator and explore its key properties. Also, we establish various (I. V) trapezium-like inequalities by considering the newly proposed operators and support line. Later on, we deliver another proof of the trapezium inequality through an analytical approach. Also, we present the numerical and visual analysis for the verification of our results. [ABSTRACT FROM AUTHOR]

Background: The variational method, a quantum mechanical approach, estimates effective charge distributions and ground-state energy by minimizing the Hamiltonian's expectation value using trial wave functions with adjustable parameters. This method provides valuable insights into system behavior and is widely used in theoretical chemistry and physics. This paper aims to investigate ground-state energies and isoelectronic sequences using the variational method, introducing a novel approach for analyzing multi-electron systems. This technique allows for determining effective charge values and ground-state energies for 2–5 electrons sequence up to Z ≤ 20. Hydrogenic wave functions are used as a trial wave function to calculate effective charge in 1 s, 2 s, and 2p states. Two varying parameters were used to calculate an approximate wave function for the system. These values are then used in non-relativistic Hamiltonian with electron–electron interaction terms to calculate the ground-state energy of an atom. Result: The results align with the reported experimental values, showing a marginal 1% error. Conclusion: A Python algorithm is established based on the variational principle. It was found that, based on a few selected parameters in scripting the program, a very promising result was obtained. Furthermore, adding more variational parameters can minimize the difference between experimental and theoretical values, and this technique can be extended to elements with higher atomic numbers. [ABSTRACT FROM AUTHOR]

We provide in this paper a concrete method for training a quantum neural network to maximize the relevant information about a property that is transmitted through the network. This is significant because it gives an operationally well founded quantity to optimize when training autoencoders for problems where the inputs and outputs are fully quantum. We provide a rigorous algorithm for computing the value of the quantum information bottleneck quantity within error ε that requires O (log 2 ⁡ (1 / ϵ) + 1 / δ 2) queries to a purification of the input density operator if its spectrum is supported on { 0 } ⋃ [ δ , 1 − δ ] for δ > 0 and the kernels of the relevant density matrices are disjoint. We further provide algorithms for estimating the derivatives of the QIB function, showing that quantum neural networks can be trained efficiently using the QIB quantity given that the number of gradient steps required is polynomial. [ABSTRACT FROM AUTHOR]

In the era of digital proliferation, individuals opt for cloud servers to store their data due to the diverse advantages they offer. However, entrusting data to cloud servers relinquishes users' control, potentially compromising data confidentiality and integrity. Traditional auditing methods designed to ensure data integrity in cloud servers typically depend on Trusted Third Party Auditors. Yet, many of these existing auditing approaches grapple with intricate certificate management and key escrow issues. Furthermore, the imminent threat of powerful quantum computers poses a risk of swiftly compromising these methods in polynomial time. To overcome these challenges, this paper introduces a Quantum‐based Secure Key Communication and Key Generation Scheme QSKCG for Outsourced Data in the Cloud. Leveraging Elliptic Curve Cryptography, the BB84 secure communication protocol, certificateless signature, and blockchain network, the proposed scheme is demonstrated through security analysis, affirming its robustness and high efficiency. Additionally, performance analysis underscores the practicality of the proposed scheme in achieving post‐quantum security in cloud storage. [ABSTRACT FROM AUTHOR]

Context: Direct DFT evaluation of negatively charged or highly π-delocalized systems is intrinsically problematic. Single ionized (anionic) forms of β-diketones combine both these issues. In this article, we have summarized and analyzed the experimental and theoretical spectral dataset for anionic forms of different substituted perfluorinated β-diketones that were obtained by our team during the past few years. Using previously collected experimental data, spectra of anion of diketones containing phenol, 2-furoyl, 2-thiophen, 2-selenophen, 2-tellorphe, 2-pyridin, 2-naphthyl, and N-methyl-pyrrole substituted rings were considered and chosen for the simulation. The X3LYP density functional demonstrates good results in both ultraviolet and visible parts of the diketones spectra. Minnesota family show predictable ability within the reasonable errors. The linear correlation between the Hartree–Fock exchange and the errors of estimation has been observed. Density functionals with a low contribution of HF weight (0–30%) provide better prediction accuracy. Methods: Quantum chemistry calculations were performed under eighteen density functionals (Slater, Becke, OPTX, LYP, PW91C, BPE0, B3LYP, CAM-B3LYP, X3LYP, TPSS, revTPSS, TPSSh, M06-L, M06, M06-2X, M06-HF, M11-L, MN15-L), paired with the SMD solvation model implemented in GAMESS US program package. Def2-SVP basis set functions were applied to light atoms, and CRENBL effective core potential was used to Tellurium. [ABSTRACT FROM AUTHOR]

Matrices are built and designed by applying procedures from lower order matrices. Matrix tensor products, direct sums or multiplication of matrices are such procedures and a matrix built from these is said to be a separable matrix. A non-separable matrix is a matrix which is not separable and is often referred to as an entangled 'matrix. The matrices built may retain properties of the lower order matrices or may also acquire new desired properties not inherent in the constituents. Here design methods for non-separable matrices of required types are derived. These can retain properties of lower order matrices or have new desirable properties. Infinite series of required type non-separable matrices are constructible by the general methods. Non-separable matrices of required types are required for applications and other uses; they can capture the structure in a unique way and thus perform much better than separable matrices. General new methods are developed with which to construct multidimensional entangled paraunitary matrices; these have applications for wavelet and filter bank design. The constructions are used to design new systems of non-separable unitary matrices; these have applications in quantum information theory. Some consequences include the design of full diversity constellations of unitary matrices, which are used in MIMO systems, and methods to design infinite series of special types of Hadamard matrices. [ABSTRACT FROM AUTHOR]

The primary emphasis of the present study is to introduce some novel characterizations of the interval-valued $ (\mathcal{I}.\mathcal{V}) $ right symmetric quantum derivative and antiderivative operators relying on generalized Hukuhara difference. To continue the study, we start with the concept of symmetric differentiability in the interval-valued sense and explore some important properties. Furthermore, through the utilization of the $ (\mathcal{I}.\mathcal{V}) $ symmetric derivative operator, we develop the right-sided $ (\mathcal{I}.\mathcal{V}) $ integral operator and explore its key properties. Also, we establish various $ (\mathcal{I}.\mathcal{V}) $ trapezium-like inequalities by considering the newly proposed operators and support line. Later on, we deliver another proof of the trapezium inequality through an analytical approach. Also, we present the numerical and visual analysis for the verification of our results.

Abstract Background The variational method, a quantum mechanical approach, estimates effective charge distributions and ground-state energy by minimizing the Hamiltonian's expectation value using trial wave functions with adjustable parameters. This method provides valuable insights into system behavior and is widely used in theoretical chemistry and physics. This paper aims to investigate ground-state energies and isoelectronic sequences using the variational method, introducing a novel approach for analyzing multi-electron systems. This technique allows for determining effective charge values and ground-state energies for 2–5 electrons sequence up to Z ≤ 20. Hydrogenic wave functions are used as a trial wave function to calculate effective charge in 1 s, 2 s, and 2p states. Two varying parameters were used to calculate an approximate wave function for the system. These values are then used in non-relativistic Hamiltonian with electron–electron interaction terms to calculate the ground-state energy of an atom. Result The results align with the reported experimental values, showing a marginal 1% error. Conclusion A Python algorithm is established based on the variational principle. It was found that, based on a few selected parameters in scripting the program, a very promising result was obtained. Furthermore, adding more variational parameters can minimize the difference between experimental and theoretical values, and this technique can be extended to elements with higher atomic numbers.

The security challenges confronting the space-ground integrated network in 6G era were mainly disscussed, and an innovative scheme based on quantum security was proposed. Firstly, the developmental backdrop and evolving trends of the integrated network’s architecture were outlined, highlighting the distinct features and application contexts of both the transparent forwarding and on-board regeneration frameworks. Secondly, the security challenges faced by the space-ground integrated network were analyzed, especially the risks of data transmission security and access authentication. In response to these challenges, the innovative solutions were proposed to ensure the security of the space-ground integrated network based on quantum technology. By leveraging the unconditional security of quantum communication and the advantages of post-quantum cryptographic algorithms, end-to-end secure communication protection for the space-ground integrated network was provided. Finally, the feasibility of using quantum technology was verified to ensure the security of the space-ground integrated network.

Photonics offers unique capabilities for quantum information processing (QIP) such as room-temperature operation, the scalability of nanophotonics, and access to ultrabroad bandwidths and consequently ultrafast operation. Ultrashort pulse sources of quantum states in nanophotonics are an important building block for achieving scalable ultrafast QIP; however, their demonstrations so far have been sparse. Here, we demonstrate a femtosecond biphoton source in dispersion-engineered periodically poled lithium niobate nanophotonics. We measure 17 THz of bandwidth for the source centered at 2.09 µm, corresponding to a few optical cycles, with a brightness of 8.8 GHz/mW. Our results open new paths toward realization of ultrafast nanophotonic QIP.

Game theory has been studied extensively in recent centuries as a set of formal mathematical strategies for optimal decision making. This discipline improved its efficiency with the arrival, in the 20th century, of digital computer science. However, the computational limitations related to exponential time type problems in digital processors, triggered the search for more efficient alternatives. One of these choices is quantum computing. Certainly, quantum processors seem to be able to solve some of these complex problems, at least in theory. For this reason, in recent times, many research works have emerged related to the field of quantum game theory. In this paper we review the main studies about the subject, including operational requirements and implementation details. In addition, we describe various quantum games, their design strategy, and the used supporting tools. We also present the still open debate linked to the interpretation of the transformations of classical algorithms in fundamental game theory to their quantum version, with special attention to the Nash equilibrium.

Quantum-dot cellular automata (QCA) is based on traditional cellular automata models in which each cell has one of the finite distinct states at any given time. Cellular automata refer to the use of Coulombic contact rather than wires to connect cells. The discrete character of cellular automata and quantum mechanics have been exploited to develop nano-scale devices that can conduct computations with high switching frequency and low power dissipation. QCA has been utilized in many categories for fault-tolerant and reversible logic computing. On the other hand, the morphology algorithm is an area of image processing that provides a unified methodology and strong tools for image/video processing applications. This work proposes a unique fault-tolerant approach based on QCA to execute the morphological operations in image processing. Two fault-tolerant 5-input majority gates are employed in the implemented QCA architecture, and a control line is used to accomplish dilation and erosion. To assess the capabilities of the proposed fault-tolerant circuit for morphological procedures, its performance is analyzed and confirmed. The proposed design is compared to earlier results, which significantly improve fault-tolerant ability. [ABSTRACT FROM AUTHOR]

Quantum computing is described as a process by which a system calculates output. Quantum physics usually refers to the smallest discrete unit of any property; the basic unit of data in quantum is the qubit; this qubit unit is equivalent to the bit unit in classical neural networks. Quantum deep learning combines quantum computing with deep learning to reduce training time for neural networks, which has proven effective in solving some intractable problems on classical computers. Quantum deep learning has proven effective in solving some intractable problems on classical computers. A quantum network can benefit from quantum information flow because it is a more efficient framework than classical systems. Each quantum deep learning consists of a quantum gate. In this review, we provide a comprehensive review of recent studies that include different quantum deep learning applications, including healthcare, handwriting, and many others. Also, methodologies, problems, main datasets, results, strengths, limitations, and challenges are included in this review. [ABSTRACT FROM AUTHOR]

Objectives To introduce quantum computing technologies as a tool for biomedical research and highlight future applications within healthcare, focusing on its capabilities, benefits, and limitations. Target Audience Investigators seeking to explore quantum computing and create quantum-based applications for healthcare and biomedical research. Scope Quantum computing requires specialized hardware, known as quantum processing units, that use quantum bits (qubits) instead of classical bits to perform computations. This article will cover (1) proposed applications where quantum computing offers advantages to classical computing in biomedicine; (2) an introduction to how quantum computers operate, tailored for biomedical researchers; (3) recent progress that has expanded access to quantum computing; and (4) challenges, opportunities, and proposed solutions to integrate quantum computing in biomedical applications. [ABSTRACT FROM AUTHOR]

We propose terminology to classify interpretations of quantum mechanics and models that modify or complete quantum mechanics. Our focus is on models which have previously been referred to as superdeterministic (strong or weak), retrocausal (with or without signalling, dynamical or non-dynamical), future-input-dependent, atemporal and all-at-once, not always with the same meaning or context. Sometimes, these models are assumed to be deterministic, sometimes not, the word deterministic has been given different meanings, and different notions of causality have been used when classifying them. This has created much confusion in the literature, and we hope that the terms proposed here will help to clarify the nomenclature. The general model framework that we will propose may also be useful to classify other interpretations and modifications of quantum mechanics. This document grew out of the discussions at the 2022 Bonn Workshop on Superdeterminism and Retrocausality. [ABSTRACT FROM AUTHOR]

The Orch-Or model cannot explain the reason for the observation of non-human intelligence contacts by dying or comatose patients, and furthermore, it ignores the immortality of human beings. Therefore, a new model is presented to answer all questions and find a solution for human immortality. In this model, intelligence is a quantum particle that by acceleration of a photon in every spark; comes from the black and white box regions of curved space time of Penrose's diagram and by connecting to particles; makes them intelligent. Like the Higgs boson, which binds to particles and gives them mass. The basic intelligence of every human enters our space-time region when the primary cell is born and remains free even after death. Free intelligences interact with the brain and penetrate into the brains of the patient and induce non-human intelligence contacts. On the other hand, the brains of healthy people do not have empty space to store information of free intelligence due to high activity, and therefore healthy people do not see non-human intelligence. This model not only explains the reason for the contact with non-human intelligence for dying patients, but also provides solutions for the treatment of incurable diseases by finding rogue intelligences in the human body. [ABSTRACT FROM AUTHOR]

Copyright of Strategic Universe Journal / Univers Strategic is the property of Dimitrie Cantemir Christian University, Institute for Security Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article explores the use of art to make quantum mechanics more intuitive and accessible. The authors organized a hackathon event where participants created artworks that illustrate quantum principles such as superposition and entanglement. Examples of these artworks include a quantum mosaic, quantum vortex art, and musical compositions. The authors argue that these artistic representations help to make quantum concepts more understandable. The article also discusses other projects that explore the intersection of quantum science and art, including generating art based on quantum architectures and using quantum principles to enhance machine learning visualization. The text raises questions about the definition of art and includes the perspective of a Japanese contemporary artist. Overall, these projects aim to make quantum concepts more accessible through artistic expression. [Extracted from the article]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Clustering algorithms are at the basis of several technological applications, and are fueling the development of rapidly evolving fields such as machine learning. In the recent past, however, it has become apparent that they face challenges stemming from datasets that span more spatial dimensions. In fact, the best-performing clustering algorithms scale linearly in the number of points, but quadratically with respect to the local density of points. In this work, we introduce qCLUE, a quantum clustering algorithm that scales linearly in both the number of points and their density. qCLUE is inspired by CLUE, an algorithm developed to address the challenging time and memory budgets of Event Reconstruction (ER) in future High-Energy Physics experiments. As such, qCLUE marries decades of development with the quadratic speedup provided by quantum computers. We numerically test qCLUE in several scenarios, demonstrating its effectiveness and proving it to be a promising route to handle complex data analysis tasks – especially in high-dimensional datasets with high densities of points.

We report on the design and simulation of strip-loaded nanophotonic interfaces aimed at improving resonant coupling and photon routing efficiency. In our design, the guided mode is confined within a plane by a high-index thin film and is loosely confined laterally by a lower index strip. Using a hydrogen silsesquioxane (HSQ) strip, titanium dioxide core, and silicon dioxide substrate, we optimise the waveguide dimensions for maximum lateral confinement of light. Specifically, we propose a polymer-based Bragg grating cavity and ring resonator that achieve near-optimal mode volumes and high Q-factors. These may be further developed to achieve the even higher Q-factors demanded by quantum technologies. Our calculations suggest that a quantum dot embedded in a cavity with a mode volume of Veff∼7.0λ/n3 and a Q-factor of 7,000 can produce photons with 97% indistinguishability at 4K. Additionally, we investigate directional couplers for efficient photon routing, comparing photonic and plasmonic material structures. While pure photonic structures demonstrate lower loss and improved quality factors, they face practical limitations in terms of bending radius. Conversely, plasmonic structures offer shorter bending radii but higher propagation losses. This research lays the groundwork for future nanophotonic designs, aiming to enhance photon generation and routing capabilities for quantum optical applications.

ABSTRACTSelenium is a chemical element from the chalcogenide group (Oxygen, Sulfur, Selenium and Tellurium), which has been defined as a critical element by the American Physical Society and the Materials Research Society. Indeed, selenium is used abundantly in nanotechnologies but its scarcity on Earth put its sustainability at risk. This review highlights the properties of selenium at the nanoscale and illustrates some of its most important applications in medicine, agriculture, catalysis, opto-electronics, photovoltaics, piezo-electrics and quantum electronics.

The development of single‐platform qubits, predominant for most of the last few decades, has driven the progress of quantum information technologies but also highlighted the limitations of various platforms. Some inherent issues, such as charge/spin noise in materials hinder certain platforms, while increased decoherence upon attempts to scale up severely impacts qubit quality and coupling on others. In addition, a universal solution for coherent information transfer between quantum systems remains lacking. By combining one or more qubit platforms, one could potentially create new hybrid platforms that might alleviate significant issues that current single‐platform qubits suffer from, and in some cases, even facilitate the conversion of static to flying qubits on the same hybrid platform. While nascent, this is an area of rising importance that could shed new light on robust and scalable qubit development and provide new impetus for research directions. Here, the requirements for hybrid systems are defined with artificial atoms in the solid state, exemplified with systems that are proposed or attempted, and conclude with the outlook for such hybrid quantum systems. [ABSTRACT FROM AUTHOR]

Wafer‐scale heteroepitaxial diamond thin films demonstrate multiple advantages for a further development of integrated optical and quantum devices based on impurity–vacancy color centers. The main obstacle here is a high structural defect density characteristic for heteroepitaxial epilayers. In this work, technological methods of stress control, NV formation, and defect density reduction in diamond epilayers, which are based on principles of epitaxial lateral overgrowth (ELO) are reported on. Herein, material and quantum properties of NV‐doped diamond thin films obtained by patterned nucleation growth and by ELO of microstructured epilayers, are compared. It is demonstrated that a combination of both methods might have a significant potential for the wafer‐scale production of heteroepitaxial diamond for quantum devices. [ABSTRACT FROM AUTHOR]

This study uses Raina's function to obtain a new coordinated p q -integral identity. Using this identity, we construct several new p q -Simpson's type inequalities for generalized convex functions on coordinates. Setting p 1 = p 2 = 1 in these inequalities yields well-known quantum Simpson's type inequalities for coordinated generalized convex functions. Our results have important implications for the creation of post quantum mathematical frameworks. [ABSTRACT FROM AUTHOR]

Quantum control allows us to address the problem of engineering quantum dynamics for special purposes. While recently the field of quantum batteries has attracted much attention, optimization of their charging has not benefited from the quantum control methods. Here we fill this gap by using an optimization method. We apply for the first time the convergent iterative method for the control of the population of a bipartite quantum system in two cases, starting with a qubit-qubit case. The quantum charger-battery system is considered here, where the energy is pumped into the charger by an external classical electromagnetic field. Secondly, we systematically extend our investigation to a second case involving two harmonic oscillators in the Gaussian regime, presenting an original formulation of the method. In both cases, the charger is considered to be an open dissipative system, as its interaction with the drive may require a more pronounced exposure to general interaction with environment. A key consideration in our optimization strategy is the practical concern of turning the charging external field on and off. We find that optimizing the pulse shape yields a substantial enhancement in both the power and efficiency of the charging process compared to a sinusoidal drive. The harmonic oscillator configuration of quantum batteries is particularly intriguing, as the optimal driving pulse remains effective regardless of the environmental temperature. This study introduces a novel approach to quantum battery charging optimization, opening avenues for enhanced performance in real-world applications. [ABSTRACT FROM AUTHOR]

Kierkegaard and Wittgenstein: the harmony of faith with the paradoxes of contemporary epistemology The aim of the article is, with reference to the still current and discussed problem of the relationship between reason and religious faith, to point out the surprising harmony of the philosophical positions of Kierkegaard and Wittgenstein, emphasizing the a-rationality of belief in an infinite God and in his �€�absurd' incarnation into the �€�finite', with experimentally proven �€�irrational' conclusions of modern quantum philosophy, focusing on ironic �€�irrationality' of modern science. Kierkegaard's underlining of the subjectivity of belief in the �€�absolute paradox' is in strong congruence with Wittgenstein's �€�necessary silence' before the �€�Ineffable', mystical and transcendent �€�Point beyond the world', �€�Infinity' that reveals itself through the miraculously created universe. The common denominator of both philosophical positions is the mandatory prioritization of orthopraxis over orthodoxy in matters of subjective faith and over objective rational �€�proofs' of divine existence. [ABSTRACT FROM AUTHOR]

Fast settling phase locked loops (PLLs) play a pivotal role in many applications requiring rapid attainment of a stable frequency and phase. In modern communication standards, these PLLs are extensively utilized to guarantee precise compliance with dynamic resource allocation requirements. In processors, these PLLs manage dynamic voltage frequency scaling. Moreover, the fast-settling PLLs expedite the scanning of frequency spectra in sophisticated electronic radar set-ups, proving particularly advantageous for imaging and scanning radar applications. The rapid response exhibited by these PLLs is also harnessed in quantum technologies, catering to the urgent need for precise frequency adjustments to manipulate qubit states effectively. The strategies employed to attain fast-settling PLLs are primarily classified into five broad techniques in this article: enhanced phase frequency detection, hybrid multiple subsystems, VCO start-up, gear shift, and look-up table or finite state machine. This article explores the fundamental operational principles encompassing these techniques and presents optimal settling times for each method reported in the literature. Finally, the architectures utilizing these techniques will be evaluated based on their figure of merit (FoM), settling time, and tuning range. [ABSTRACT FROM AUTHOR]

Recently, low-cost, low-power sensors have made wireless communication possible for Wireless Sensor Networks (WSNs), which are growing in popularity and bringing about tremendous improvement. Nevertheless, their autonomy eventually wanes because WSN nodes run entirely on batteries. The network lifetime is affected by this energy constraint. Therefore, the proposed method uses Quantum-based Optical sensors with the CUCKOO-ANN method for Enhanced Energy-Efficient Wireless Communication to overcome the existing issues. Initially, the Quantum-based optical sensors helped collect the data from the location and were also used for energy-efficient communication. It transfers the data very quickly and securely without any interruption. Next, the CUCKOO-ANN method optimises the real-time data and provides efficiency. Therefore, it helps to find an optimal route between the user and the Base station. The concept that has been presented exhibits excellent potential as it can efficiently fulfil customer service level agreements, lower average execution times, and improve data centre energy efficiency. Experimental results demonstrate that the proposed energy-efficient method minimises energy consumption, optimises data transmission, improves the network lifetime, and enhances the scalability of the network. [ABSTRACT FROM AUTHOR]

The promise of digital technology to greatly improve the efficiency of sorting and processing facilities of the future has not yet been fully realised. Improved sensor-based material flow characterization methods may pave the way for new sensor applications including adaptive plant management, increased sensor-based sorting, and more far-reaching data utilisations throughout the value chain. Using quantum remote sensors, this research proposes a novel deep learning model-based technique for evaluating healthcare data. In this scenario, healthcare data from quantum far-field sensors is collected and analysed using fuzzy K clustering-based kernel convolutional transfer Bayesian neural networks. Experimental evaluations of various detected signal data are analysed in terms of accuracy, precision, recall, and root-mean-squared error. In addition, we demonstrate that the proposed approach has reasonable computation speeds, meeting the requirements of real-time node processing on smartphones and a wearable sensor platform. [ABSTRACT FROM AUTHOR]