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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]

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]

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]

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]

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]

An alternative formulation of the no-boundary initial state of the universe in the Euclidean quantum theory of gravity is proposed. Unlike the no-boundary Hartle–Hawking wave function, in which time appears together with macroscopic space–time in the semiclassical approximation, in the proposed formalism, time is present from the very beginning on an equal footing with spatial coordinates. The main element of the formalism is the wave functional, which is defined based on the world histories of the universe. This ensures formal 4D covariance of the theory. The wave functional is defined independently of the wave function as an eigenvector of the action operator. The shape of the Origin region, together with the boundary conditions, is determined by the structure of the total energy of the universe, which includes a 3D-invariant contribution of the expansion energy. The own mass of the universe arises as a non-zero value of the expansion energy in the Origin. [ABSTRACT FROM AUTHOR]

Symmetric derivatives and integrals are extensively studied to overcome the limitations of classical derivatives and integral operators. In the current investigation, we explore the quantum symmetric derivatives on finite intervals. We introduced the idea of right quantum symmetric derivatives and integral operators and studied various properties of both operators as well. Using these concepts, we deliver new variants of Young's inequality, Hölder's inequality, Minkowski's inequality, Hermite–Hadamard's inequality, Ostrowski's inequality, and Gruss–Chebysev inequality. We report the Hermite–Hadamard's inequalities by taking into account the differentiability of convex mappings. These fundamental results are pivotal to studying the various other problems in the field of inequalities. The validation of results is also supported with some visuals. [ABSTRACT FROM AUTHOR]

Fabrication techniques for nitrogen-vacancy centres in diamond require the creation of Frenkel defects (vacancy-interstitial pairs) the components of which can interact with formed NV centres affecting their photophysical properties. Here we use Density Functional Theory simulations of inter-defect electronic and strain interactions to explore how the NV centre and carbon self-interstitial interact in different configurations. We find that hybridization occurs between the NV centre e-orbitals and the carbon self-interstitial when an interstitial is present on the vacancy side of the NV centre. We propose that this phenomenon may explain the fluorescence blinking of NV centres observed during annealing. This article is part of the Theo Murphy meeting issue 'Diamond for quantum applications'. [ABSTRACT FROM AUTHOR]

An intrusion detection method using rough-fuzzy set and parallel quantum genetic algorithm (RFS-QGAID) is proposed in this paper. The RFS-QGAID is applied to solve the serious problems of determining the optimal antibodies subsets used to detect an anomaly. To obtain a simplified antibodies collection for high dimensional Log data sets, RFS is applied to delete the redundant antibody features and obtain the optimal antibodies features combination. Then, the optimal attitudes are entered into the QGA classifier for learning and training in the following stage. At last, the detected Log antigens are fed into RFS-QGAID, and we can classify the intrusion types. With RFS-QGAID, we give the simulations, the results on real Log data sets show that: the higher detection accuracy of RFS-QGAID is higher detection accuracy, but the false negative rate is lower for small samples sets, the adaptive performance is higher than other detection algorithms. [ABSTRACT FROM AUTHOR]

Interpretations of quantum physics are shown to presuppose the reality of consciousness. But if a minimal realism about the external world is true, then the consciousness presupposed by quantum reality cannot be only that of the scientific observer, cannot be only 'local' but must be 'global'. Global consciousness is argued to have all and only the essential properties of God. Quantum reality depends on God's consciousness and the physical world depends on quantum reality. Therefore, the physical world depends on God's consciousness. [ABSTRACT FROM AUTHOR]

Accepted by: Aris Syntetos Can mathematical research from a business school professor that is published in academic journals also substantially improve actual industrial practice in global supply chains? Yes. Can mathematics and operations management help improve fairness in access to organ transplants? Yes, again. Can mathematics developed for business applications facilitate the development of new types of quantum hardware? Also, yes. I have three main messages in this invited essay: (1) free yourself from self-imposed constraints and explore a wider range of new problems and arcane mathematics; (2) be bold and imaginative in developing novel solutions and (3) implement your ideas in practice, scaling them with a channel partner. [ABSTRACT FROM AUTHOR]

The current understanding of long COVID (LC) is still limited. This review highlights key findings regarding the role of gut microbiota, mitochondria, and the main pathophysiological aspects of LC revealed by clinical studies, related to the complex interplay between infection, intestinal dysbiosis, dysfunctional mitochondria, and systemic inflammation generated in a vicious circle, reflecting the molecular and cellular processes from the "leaky gut" to the "leaky electron transport chain (ETC)" into a quantum leap. The heterogeneity of LC has hindered progress in deciphering all the pathophysiological mechanisms, and therefore, the approach must be multidisciplinary, with a special focus not only on symptomatic management but also on addressing the underlying health problems of the patients. It is imperative to further assess and validate the effects of COVID-19 and LC on the gut microbiome and their relationship to infections with other viral agents or pathogens. Further studies are needed to better understand LC and expand the interdisciplinary points of view that are required to accurately diagnose and effectively treat this heterogeneous condition. Given the ability of SARS-CoV-2 to induce autoimmunity in susceptible patients, they should be monitored for symptoms of autoimmune disease after contracting the viral infection. One question remains open, namely, whether the various vaccines developed to end the pandemic will also induce autoimmunity. Recent data highlighted in this review have revealed that the persistence of SARS-CoV-2 and dysfunctional mitochondria in organs such as the heart and, to a lesser extent, the kidneys, liver, and lymph nodes, long after the organism has been able to clear the virus from the lungs, could be an explanation for LC. [ABSTRACT FROM AUTHOR]

With a Nobel Prize for Physics widely viewed as only given for 'settled' science, the award then essentially accepts the validity of universal nonlocality. Other key discoveries and insights in recent years are also progressively pointing to the appearance of our Universe, its energy-matter and space-time, as not being foundational but emerging from deeper, discarnate realms of causation. as digitized and meaningful, in-formation, its manifestation pixelated at the so-named Planck scale of existence. Extending from studies of black holes to the entire Universe, a growing number of cosmologists have also developed the so-named holographic principle, to model the four-dimensional appearance of our Universe (three dimensions of space and one of time) as a holographic projection of its two- dimensional boundary. In framing the emergent cosmology of a nonlocally unified, meaningfully in-formed and holographically manifested Universe, an expansion of the three universal Laws of Thermodynamics to three Laws of Information, or Infodynamics also points the way to reconciling Quantum Theory that describes energy-matter and Relativity Theory that describes space-time and offers too an understanding of how the lifecycle of our Universe flows from its first moment until its last. Treating gravity as an emergent consequence of the in-formational and holographic structure of space- time and describing it as the consequence of the intropy associated with the positions in space-time of massive bodies, also points to the findings of the loss of phenotype identity in zero gravity and the role between gravity and cellular identity and the emergence of symbiogenesis. [ABSTRACT FROM AUTHOR]

A rapidly developing high-amplitude shear deformation anomaly in the upper crustal interval (at depths of up to 10 km), the so-called "intense deformation quantum," with a maximum increase in deformation by two orders of magnitude within half a month, has been identified within a geomechanical model of Southern California by detailed analysis of the local stress–strain features. These "quanta" can be integral elements of the entire deep deformation process associated with seismicity. The quantitative characteristics of deep deformation "quanta" and the conditions for their occurrence are discussed. [ABSTRACT FROM AUTHOR]

In the Bertrand–Edgeworth duopoly game, two players compete in price to capture the market demand of a uniform product. The game is studied from a general perspective, so that players with different production costs and capacity constraints as well as the two more important rules dealing with unsatisfied demand (proportional and efficient) are taken into consideration. A quantization scheme is applied to the game with the aim of improving the results compared to the classic game. The quantum Bertrand–Edgeworth duopoly game is studied in this work via spatial numerical simulation, supporting the results analytically when it is possible. In this context, it is found that high entanglement induces a Pareto optimal solution ruled by the lower capacity of the players. The way in which the players' entanglement acts in the game is examined through simulation, paying special attention to the critical value of entanglement from which the Pareto optimal solution emerges. [ABSTRACT FROM AUTHOR]

Several quantum mechanical problems are studied all of which can be approached using algebraic means. The first problem introduces a large class of Hamiltonian operators which can be related to elements of su(2) or su(1, 1) Lie algebras. A Casimir operator can be obtained and the model can be solved in general by introducing an appropriate basis. The second system involves a collection of lattice spin Hamiltonian models. It is shown how a matrix representation can be determined for these types of models with respect to a specific basis. Using these matrices secular polynomials as functions of the energy eigenvalues and their corresponding eigenvectors are calculated. The last system is similar to the first, but is suited to a particular application The eigenvectors of the model are used used to calculate the Berry phase for these states. [ABSTRACT FROM AUTHOR]

Complex systems, as interwoven miscellaneous interacting entities that emerge and evolve through self-organization in a myriad of spiraling contexts, exhibit subtleties on global scale besides steering the way to understand complexity which has been under evolutionary processes with unfolding cumulative nature wherein order is viewed as the unifying framework. Indicating the striking feature of non-separability in components, a complex system cannot be understood in terms of the individual isolated constituents' properties per se, it can rather be comprehended as a way to multilevel approach systems behavior with systems whose emergent behavior and pattern transcend the characteristics of ubiquitous units composing the system itself. This observation specifies a change of scientific paradigm, presenting that a reductionist perspective does not by any means imply a constructionist view; and in that vein, complex systems science, associated with multiscale problems, is regarded as ascendancy of emergence over reductionism and level of mechanistic insight evolving into complex system. While evolvability being related to the species and humans owing their existence to their ancestors' capability with regards to adapting, emerging and evolving besides the relation between complexity of models, designs, visualization and optimality, a horizon that can take into account the subtleties making their own means of solutions applicable is to be entailed by complexity. Such views attach their germane importance to the future science of complexity which may probably be best regarded as a minimal history congruent with observable variations, namely the most parallelizable or symmetric process which can turn random inputs into regular outputs. Interestingly enough, chaos and nonlinear systems come into this picture as cousins of complexity which with tons of its components are involved in a hectic interaction with one another in a nonlinear fashion amongst the other related systems and fields. Relation, in mathematics, is a way of connecting two or more things, which is to say numbers, sets or other mathematical objects, and it is a relation that describes the way the things are interrelated to facilitate making sense of complex mathematical systems. Accordingly, mathematical modeling and scientific computing are proven principal tools toward the solution of problems arising in complex systems' exploration with sound, stimulating and innovative aspects attributed to data science as a tailored-made discipline to enable making sense out of voluminous (-big) data. Regarding the computation of the complexity of any mathematical model, conducting the analyses over the run time is related to the sort of data determined and employed along with the methods. This enables the possibility of examining the data applied in the study, which is dependent on the capacity of the computer at work. Besides these, varying capacities of the computers have impact on the results; nevertheless, the application of the method on the code step by step must be taken into consideration. In this sense, the definition of complexity evaluated over different data lends a broader applicability range with more realism and convenience since the process is dependent on concrete mathematical foundations. All of these indicate that the methods need to be investigated based on their mathematical foundation together with the methods. In that way, it can become foreseeable what level of complexity will emerge for any data desired to be employed. With relation to fractals, fractal theory and analysis are geared toward assessing the fractal characteristics of data, several methods being at stake to assign fractal dimensions to the datasets, and within that perspective, fractal analysis provides expansion of knowledge regarding the functions and structures of complex systems while acting as a potential means to evaluate the novel areas of research and to capture the roughness of objects, their nonlinearity, randomness, and so on. The idea of fractional-order integration and differentiation as well as the inverse relationship between them lends fractional calculus applications in various fields spanning across science, medicine and engineering, amongst the others. The approach of fractional calculus, within mathematics-informed frameworks employed to enable reliable comprehension into complex processes which encompass an array of temporal and spatial scales notably provides the novel applicable models through fractional-order calculus to optimization methods. Computational science and modeling, notwithstanding, are oriented toward the simulation and investigation of complex systems through the use of computers by making use of domains ranging from mathematics to physics as well as computer science. A computational model consisting of numerous variables that characterize the system under consideration allows the performing of many simulated experiments via computerized means. Furthermore, Artificial Intelligence (AI) techniques whether combined or not with fractal, fractional analysis as well as mathematical models have enabled various applications including the prediction of mechanisms ranging extensively from living organisms to other interactions across incredible spectra besides providing solutions to real-world complex problems both on local and global scale. While enabling model accuracy maximization, AI can also ensure the minimization of functions such as computational burden. Relatedly, level of complexity, often employed in computer science for decision-making and problem-solving processes, aims to evaluate the difficulty of algorithms, and by so doing, it helps to determine the number of required resources and time for task completion. Computational (-algorithmic) complexity, referring to the measure of the amount of computing resources (memory and storage) which a specific algorithm consumes when it is run, essentially signifies the complexity of an algorithm, yielding an approximate sense of the volume of computing resources and seeking to prove the input data with different values and sizes. Computational complexity, with search algorithms and solution landscapes, eventually points toward reductions vis à vis universality to explore varying degrees of problems with different ranges of predictability. Taken together, this line of sophisticated and computer-assisted proof approach can fulfill the requirements of accuracy, interpretability, predictability and reliance on mathematical sciences with the assistance of AI and machine learning being at the plinth of and at the intersection with different domains among many other related points in line with the concurrent technical analyses, computing processes, computational foundations and mathematical modeling. Consequently, as distinctive from the other ones, our special issue series provides a novel direction for stimulating, refreshing and innovative interdisciplinary, multidisciplinary and transdisciplinary understanding and research in model-based, data-driven modes to be able to obtain feasible accurate solutions, designed simulations, optimization processes, among many more. Hence, we address the theoretical reflections on how all these processes are modeled, merging all together the advanced methods, mathematical analyses, computational technologies, quantum means elaborating and exhibiting the implications of applicable approaches in real-world systems and other related domains. [ABSTRACT FROM AUTHOR]

The Quantum cell expansion system manufactured by Terumo-BCT is perhaps the most widely reported Good Manufacturing Practice–compliant bioreactor used for the expansion of adherent cell populations, both for research purposes and clinical cell–based therapies/trials. Although the system was originally designed for adherent cell expansion, more recently suspension cultures and extracellular vesicle manufacturing protocols have been published using the Quantum system. Cell therapy research and regenerative medicine in general is a rapidly expanding field and as such it is likely that the use of this system will become even more widespread and perhaps mandatory, for both research and development and in the clinic. The purpose of this review is to describe, compare and discuss the diverse range of research and clinical applications currently using the Quantum system, which to our knowledge has not previously been reviewed. In addition, current and future challenges will also be discussed. [ABSTRACT FROM AUTHOR]

The Dunkl Laplacian is used to define the Hamiltonian of a modified quantum harmonic oscillator, associated with any finite reflection group. The potential is a sum of the inverse squares of the linear functions whose zero sets are the mirrors of the group's reflections. The symmetric group version of this is known as the Calogero-Moser model of N identical particles on a line. This paper focuses on the group of symmetries of the regular icosahedron, associated to the root system of type H3. Special wavefunctions are defined by a generating function arising from the vertices of the icosahedron and have the key property of allowing easy calculation of the effect of the Dunkl Laplacian. The ground state is the product of a Gaussian function with powers of linear functions coming from the root system. Two types of wavefunctions are considered, inhomogeneous polynomials with specified top-degree part, and homogeneous harmonic polynomials. The squared norms for both types are explicitly calculated. Symmetrization is applied to produce the invariant polynomials of both types, as well as their squared norms. The action of the angular momentum square on the harmonic homogeneous polynomials is determined. There is also a sixth-order operator commuting with the Hamiltonian and the group action. [ABSTRACT FROM AUTHOR]

Multiphoton absorption of entangled photons offers ways for obtaining unique information about chemical and biological processes. Measurements with entangled photons may enable sensing biological signatures with high selectivity and at very low light levels to protect against photodamage. In this paper, we present a theoretical and experimental study of the excitation wavelength dependence of the entangled two-photon absorption (ETPA) process in a molecular system, which provides insights into how entanglement affects molecular spectra. We demonstrate that the ETPA excitation spectrum can be different from that of classical TPA as well as that for one-photon resonant absorption (OPA) with photons of doubled frequency. These results are modeled by assuming the ETPA cross-section is governed by a two-photon excited state radiative linewidth rather than by electron-phonon interactions, and this leads to excitation spectra that match the observed results. Further, we find that the two-photon-allowed states with highest TPA and ETPA intensities have high electronic entanglements, with ETPA especially favoring states with the longest radiative lifetimes. These results provide concepts for the development of quantum light--based spectroscopy and microscopy that will lead to much higher efficiency of ETPA sensors and low- intensity detection schemes. [ABSTRACT FROM AUTHOR]

Embodying the indexical signs is vital to semiosis as a cohesive material agency mediating between consequents and antecedents. One unique factor of biology compared with standard physics and chemistry is the cohesion enabling the biological components, codes and organizations to accommodate themselves with a specific material embodiment. Every individual body is uniquely biological and requires a specific cohesion of material origin for its own sake that could not be found in the non-living material world. The relevant cohesion comes from the exchange interaction of the atomic quantum particles, such as the carbon atoms, which is far greater than the electrons as a common exchange mediator adopted for the spatial cohesion ubiquitous in physics and chemistry. What is specific to the temporal cohesion latent in the atomic exchange is the immutable identity of the individual quantum particle surviving only over a limited time, while being constantly alternated with the new ones of the same kinds in a successive manner. Semiosis is supported by the underlying teleonomic cohesion, such that the preceding temporal cohesion may constantly induce the succeeding similar one ad infinitum. [ABSTRACT FROM AUTHOR]

The strive for efficiency in the generation of terahertz (THz) waves motivates intense research on novel field–matter interactions. Presently, THz generation via quadratic crystals remains the benchmark thanks to its simple and practical deployment. An interesting problem is whether new mechanisms can be exploited to elicit novel generation approaches and forms of control on the THz output in existing systems. THz generation via quantum interference (QI) leverages a third‐order nonlinear response under resonant absorption, and it has been recently explored to access surface generation in centrosymmetric systems. Its deployment in standard THz quadratic sources can potentially create a physical setting with the concurrence of two different mechanisms. Here, THz generation via QI in noncentrosymmetric crystals concurrent with phase‐matched quadratic generation in a bulk‐transmission setting is demonstrated. Beyond investigating a new physical setting, it is demonstrated that conversion efficiencies much larger than those typically associated with the medium become accessible for a typically adopted crystal, ZnTe. An inherent control on the relative amplitude and sign of the two generated THz components is also achieved. This approach provides disruptive boost and management of the optical‐to‐THz conversion performance of a well‐established technology, with significant ramifications in emerging spectroscopy and imaging applications. [ABSTRACT FROM AUTHOR]

Motivated by the generalized uncertainty principle, we derive a discrete picture of the space that respects Lorentz symmetry as well as gauge symmetry by setting an equivalency between the linear Generalized Uncertainty Principle (GUP) correction term and electromagnetic interaction term in the Dirac equation. We derived a wave function solution that satisfies this equivalency. This discreteness may explain the crystal and quasicrystal structures observed in nature at different energy scales. [ABSTRACT FROM AUTHOR]

Reversible quantum circuits are constructed using different types and numbers of quantum gates. Optimizing the number of gates to be used while creating quantum circuits reduces the cost and complexity. It is important to know the number of inputs, outputs, and gates for obtaining and optimizing state tables in reversible quantum circuits. In addition, these parameters are also used to detect faults that may occur in quantum circuits. There is a lack of determination of the input, output, and gate numbers for quantum circuits in the literature. In addition, a limited number of standard quantum circuits are used to test the applications made in the literature. It has been determined that there are very few datasets that can be used in this context. Elimination of these deficiencies in the literature constitutes the aim of our study, and the proposed method constitutes the originality of our study. In this study, the number of gates and inputs were determined by using Yolo (You Only Look Once) based methods. A large dataset consisting of CNOT, Feynman, and Toffoli gates was created using the "MATLAB" and "RCViewer+" programs. In this study, a total of 5000 quantum circuits with 1-8 gate numbers and 3-7 input numbers were created. Gates and inputs are labeled on the obtained datasets. YoloV4, YoloV7, and YoloV7x methods were applied to the tagged datasets with a training and testing ratio of 80:20. 87.1%, 89.7% and 89.3% mAP were calculated for the YoloV4, YoloV7 and YoloV7x methods, respectively. The proposed method was run for 2800 iterations and the best result was obtained with the YoloV7 algorithm. [ABSTRACT FROM AUTHOR]

National Aeronautics and Space Administration (NASA) and The White House have an interest in expanding quantum education in the US. This report summarizes the findings of NASA Space Communications and Navigation (SCaN) program fellowship and is focused on making recommendations regarding improving and expanding quantum education nationwide. To make informed and effective recommendations, qualitative data was collected regarding the current state of quantum education in the US. The information collected from meeting with experts in the fields of quantum and education was assessed before recommendations and suggestions were made. Topics addressed include guidance for selecting locations to initially focus quantum education efforts, potential problems with teachers incorporating quantum in their instructions, potential solutions to those problems, steps to increase quantum education nationwide, potential quantum programs at universities, the quantum job market, quantum demonstrations at the K-12 level, and quantum curriculum. [ABSTRACT FROM AUTHOR]

Low-frequency oscillations (LFO) are a significant problem for multi-machine electrical power system (EPS). These oscillations are undesirable as they reduce the power transfer capability of the transmission line and thus directly influence competitive electrical markets. The power system stabilizers (PSS) play a vital role in damping low-frequency oscillations to improve the dynamic stability of the power system. However, these controllers must be tuned in a coordinated and robust manner for effective performance. PSS tuning is characterized by a complex optimization problem that may involve hundreds of variables. The tuning procedure is modeled as an optimization problem which aims at maximizing the damping ratio coefficients of the closed-loop power system considering multiple operating points. In this context, this article employs and compares three metaheuristics with quantum characteristics, namely quantum particle swarm optimization (QPSO), quantum flower pollination algorithm (QFPA) and quantum gray wolf optimizer (QGWO). The aforementioned metaheuristics are used in PSS tuning in four test systems with 5 generators–7 buses, 10 generators–39 buses, 50 generators–616 buses and 170 generators–3584 buses. The results obtained by the quantum algorithms are compared through statistical indices and boxplot. The optimization results show that the QFPA and QGWO provide better system damping than the QPSO for the large electrical system. [ABSTRACT FROM AUTHOR]

Stunning new observations and theories deepen the concept of causation. Quantum theory is one of the most striking discovery of contemporary physics, and it challenges many facets of older theories, even though it lies on many assumptions that could not be empirically experimented. In this paper, I will recall the views of Hume, Reichenbach and Einstein about the issue of causation, whereas I will focus on causal priority and temporal priority criteria in causation. Nevertheless, one task that I perform is the interpretation of these ideas by arguing them within the quantum scope. That is, trying to manifest Hume, Reichenbach and Einstein views of causation that I think as close, but I shall avoid putting straight cuts on where they differ. Direction of causal processes is dependent on the direction of time in Hume's sense while Reichenbach explained the direction of time with irreversible processes, even if he follows the temporal priority criteria of Hume. It is stated that there is nothing faster than light by Einstein, as a result, a cause could not pass the upper limit of light. Crudely put, their thoughts all differ from the principles of quantum, because causation (causation might denote a different meaning in quantum realm) may be able to travel faster than light and the common cause principle between events is rejected in quantum realm. [ABSTRACT FROM AUTHOR]

Quantum calculus provides a significant generalization of classical concepts and overcomes the limitations of classical calculus in tackling non-differentiable functions. Implementing the q-concepts to obtain fresh variants of classical outcomes is a very intriguing aspect of research in mathematical analysis. The objective of this article is to establish novel Milne-type integral inequalities through the utilization of the Mercer inequality for q-differentiable convex mappings. In order to accomplish this task, we begin by demonstrating a new quantum identity of the Milne type linked to left and right q derivatives. This serves as a supporting result for our primary findings. Our approach involves using the q-equality, well-known inequalities, and convex mappings to obtain new error bounds of the Milne–Mercer type. We also provide some special cases, numerical examples, and graphical analysis to evaluate the efficacy of our results. To the best of our knowledge, this is the first article to focus on quantum Milne–Mercer-type inequalities and we hope that our methods and findings inspire readers to conduct further investigation into this problem. [ABSTRACT FROM AUTHOR]

We construct infinite-dimensional analogues of finite-dimensional simple modules of the nonstandard q -deformed enveloping algebra U q ′ (so n) defined by Gavrilik and Klimyk, and we do the same for the classical universal enveloping algebra U (so n). In this paper we only consider the case when q is not a root of unity, and q → 1 for the classical case. Extending work by Mazorchuk on so n , we provide rational matrix coefficients for these infinite-dimensional modules of both U q ′ (so n) and U (so n). We use these modules with rationalized formulas to embed the respective algebras into skew group algebras of shift operators. Casimir elements of U q ′ (so n) were given by Gavrilik and Iorgov, and we consider the commutative subalgebra Γ ⊂ U q ′ (so n) generated by these elements and the corresponding subalgebra Γ 1 ⊂ U (so n). The images of Γ and Γ 1 under their respective embeddings into skew group algebras are equal to invariant algebras under certain group actions. We use these facts to show that Γ is a Harish-Chandra subalgebra of U q ′ (so n) and Γ 1 is a Harish-Chandra subalgebra of U (so n). [ABSTRACT FROM AUTHOR]

It is fair to say that Bell's Theorem leaves a nasty taste in the mouths of many physicists because test results seem to suggest that non-local, i.e., superluminal, communication of some sort is going on all around us and maybe not just at the quantum level. But Bell's Theorem has been aptly demonstrated to date, and Quantum Mechanics (QM) is, if not the most successful theory devised by man, certainly one of them. To some, and especially to Einstein, one of the least favorable aspects of QM is its statistical nature. This paper embraces QM's statistical nature and employs quantum entanglement to show that superluminal communication may be possible albeit, most likely, initially, at a very low bit rate. [ABSTRACT FROM AUTHOR]

We study a recently proposed modified Schrödinger equation having an added nonlinear term. For the case where a stochastic term is added to the Hamiltonian, the fluctuating response is found to resemble the process of thermalization. Disentanglement induced by the added nonlinear term is explored for a system made of two coupled spins. A butterfly-like effect is found near fully entangled states of the spin–spin system. A limit cycle solution is found when one of the spins is externally driven. [ABSTRACT FROM AUTHOR]

Viral vectors are commonly used to introduce genetic material into cells to modify cell function for a variety of purposes. Manufacture of those modified viruses may use a variety of cell types to generate high titers of viral particles; one of the most common being HEK293 cells. These cells have been modified into different lines aimed at satisfying specific use cases. HEK293T cells, for example, have been modified to include the SV40 large T antigen. Efficient viral particle production by HEK293T cells requires the maintenance of favorable cell culture conditions during expansion and transfection. This protocol describes the use of the Quantum® hollow-fiber bioreactor (HFB) system for the automated expansion of HEK293T cells, and the results derived using the protocol described herein were not compared with those from tissue culture flasks or other expansion platforms, as the parameters described are unique to Quantum's hollow fiber cell expansion environment. The purpose of this protocol is to help users of Quantum to focus on relevant parameters of expansion in the HFB milieu and to provide guidelines for a successful expansion of HEK293T cells in the Quantum system. The steps provided have been optimized to reliably control environmental factors related to glucose, lactate, and pH. Data reflecting this consistency are provided along with procedural time points reflected in text and figure formats. [ABSTRACT FROM AUTHOR]

This work studies the quantum Hotelling game with elastic demand by means of an ad hoc simulation technique that allows to scrutinize how the entanglement of the players induces the emergence of the Pareto optimal solution in Nash equilibrium (NE), even when NE does not exist in the classic game due to the proximity of the players. [ABSTRACT FROM AUTHOR]

In lymphoblastic leukaemia (ALL), the bone marrow naturally produces immature cells. Each year ALL is diagnosed with over 6500 instances, and the trend is still going upward. Technological advancements in AI and big data analytics help doctors and radiologists make accurate and efficient clinical decisions. The proposed method consists of two core steps: segmentation and classification based on the quantum convolutional networks. A three‐dimensional U‐network is proposed having 70 layers that are trained on the optimal hyperparameters, which provides 0.98 dice scores. The four‐qubit quantum transfer learning model is proposed for classifying different types of blood cells. The accuracies achieved are 0.99 on blast cells, 0.99 on Basophils, 0.98 on Eosinophils, 0.97 on Neutrophils, 0.99 on Lymphocytes, and 0.96 on Monocytes. The proposed classification model provides 0.99 average accuracy. [ABSTRACT FROM AUTHOR]

With the improvement of design capability and manufacture technology, the integration as well as the complexity of chips have also increased dramatically, and it becomes critical to realize noninvasive electromagnetic inspection of chips at higher resolutions. In this paper, we propose a submicron‐resolution microwave near‐field imaging system based on the nitrogen‐vacancy center (NV) in diamond, which mainly consists of an optical hardware system and a software control system, and can be used for nondestructive imaging of microwave fields on the surface of integrated circuits with submicron resolution. We applied this technique to scan the surface microwave field distribution of a GaAs single pole double throw switch chip. The scanning imaging system can image the near‐field magnetic distribution for the chip, which provides a new solution for analyzing the current distribution on the chip surface and thus addressing problems such as excessive current or crosstalk. It is worth mentioning that the diamond NV color center‐based sensor has a very high‐spatial resolution and magnetic field detection sensitivity at room temperature, while the dielectric constant of natural diamond is 5.5, which also provides the possibility of diamond as a sensor in complex radiation environments and is an ideal detector material for near‐field imaging of electromagnetic fields. [ABSTRACT FROM AUTHOR]

An efficient algorithm to simulate dynamics of open quantum system is presented. The method describes the dynamics by unraveling stochastic wave functions converging to a density operator description. The stochastic techniques are based on the quantum collision model. Modeling systems dynamics with wave functions and modeling the interaction with the environment with a collision sequence reduces the scale of the complexity significantly. The algorithm developed can be implemented on quantum computers. We introduce stochastic methods that exploit statistical characteristics of the model such as Markovianity, Brownian motion, and binary distribution. The central limit theorem is employed to study the convergence of distributions of stochastic dynamics of pure quantum states represented by wave vectors. By averaging a sample of functions in the distribution we prove and demonstrate the convergence of the dynamics to the mixed quantum state described by a density operator. [ABSTRACT FROM AUTHOR]

A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique. [ABSTRACT FROM AUTHOR]

The world is now turning more and more to ecological, sustainable agriculture, and methods of agricultural production are turning to principles that were in the past, but which are still considered alternatives in most countries of the world. Ecological agriculture realized by methods of biodynamic agriculture, permaculture and quantum agriculture with the use of alternative sources of energy and the preservation of the environment and landscape in general, is becoming a growing need today. The European Commission, following the recommendation of world organizations, prescribed a series of recommendations and developed a series of strategies for each member state, which should reduce the emission of greenhouse gases by 2030 by at least 55%. Significant changes within agricultural production have become a necessary need for sustainable agriculture. The beginnings of different methods of soil cultivation, utilization and recycling (purification) of municipal water, as well as rainwater collection, are just some of the acceptable methods. The appearance of the principles of ecological agriculture, permaculture and quantum agriculture are therefore increasingly becoming the basis of interest and scientific study. The interaction of other sciences within agriculture and food growing is already known, but quantum physics with its laws are increasingly being tried to be implemented in direct methods of agricultural production. What are the differences and what are similarities and how scientists interpret the possible future scenario of sustainable agriculture, as well as the historical development of the mentioned principles and methods, are the topics of this paper. Through a short survey on the knowledge of the term quantum agriculture, more than 70% of respondents of the target group of agronomists and farmers have not encountered this term and do not know the principles of quantum agriculture [ABSTRACT FROM AUTHOR]

The variational quantum algorithm (VQA) is a hybrid classical–quantum algorithm. It can actually run in an intermediate-scale quantum device where the number of available qubits is too limited to perform quantum error correction, so it is one of the most promising quantum algorithms in the noisy intermediate-scale quantum era. In this paper, two ideas for solving the learning with errors problem (LWE) using VQA are proposed. First, after reducing the LWE problem into the bounded distance decoding problem, the quantum approximation optimization algorithm (QAOA) is introduced to improve classical methods. Second, after the LWE problem is reduced into the unique shortest vector problem, the variational quantum eigensolver (VQE) is used to solve it, and the number of qubits required is calculated in detail. Small-scale experiments are carried out for the two LWE variational quantum algorithms, and the experiments show that VQA improves the quality of the classical solutions. [ABSTRACT FROM AUTHOR]

Within the clinical framework of a case of schizophrenic psychosis confronted with the lockdown, some destabilizing discoveries of quantum mechanics and its implications in neuroscience and psychoanalysis are introduced. Through fractal logic, the Author attempts attempt to understand on the one hand the relationship between microscopic phenomena and macroscopic processes of the psyche and on the other how the idea of reality changes through the counterintuitive laws of modern physics. This leads to the unconscious being described as a wave function capable of processing in parallel and simultaneously, countless overlapping variables, of which only the one considered most adaptive in the relationship with the environment is consciously elected. The complexity of the phenomenon of consciousness is studied here both in the interdependence between immaterial processes (energy fields) and material ones (cellular biochemistry), and in its indeterminacy and non-linearity when the reference context changes drastically. [ABSTRACT FROM AUTHOR]

In this paper, by mapping datasets to a set of non-linear coherent states, the process of encoding inputs in quantum states as a non-linear feature map is re-interpreted. As a result of the fact that the radial basis function is recovered when data is mapped to a complex Hilbert state represented by coherent states, non-linear coherent states can be considered as a natural generalisation of the associated kernels. In this paper, as an example of kernels based on non-linear coherent states, we propose kernel functions based on generalized hypergeometric functions, as orthogonal polynomial functions. The suggested kernel is implemented with the support vector machine (SVM) on two well known datasets (make_circles, and make_moons) and outperforms the baselines, even when the level of noise is high. In addition, we study the impact of the geometrical properties of the feature space, obtained by the non-linear coherent states, on the SVM classification task, by considering the Fubiniâ€"Study metric of the associated coherent states. [ABSTRACT FROM AUTHOR]

We explore a nonlinear term that can be added to the Schrödinger equation without violating unitarity of the time evolution. We find that the added term suppresses entanglement, without affecting the evolution of any product state. The dynamics generated by the modified Schrödinger equation is explored for the case of a two-spin 1/2 system. [ABSTRACT FROM AUTHOR]

This paper focused on the efficiency of carbon nitride nanotubes functionalized with alanine amino acid (f-C3NNTs) in thiotepa (TPA) anti-cancerous drug delivery via density functional theory (DFT). Pristine C3NNTs were incorporated for comparison. TPA was found to spontaneously undergo exothermic adsorption onto the nanostructures. The f-C3NNT/TPA complexes showed the highest interaction strength. The adsorption distance of TPA was found to be smaller, with a greater adsorption capacity and solubility on the f-C3NNT surface than on the pristine C3NNT surface. As they were polar, all the complexes were concluded to be insoluble within an aqueous phase. The quantum molecular descriptors revealed the f-C3NNT nanocarriers to be more reactive than the C3NNT carrier. The drug was found to spontaneously and exothermically interact with f-C3NNT. As a result, f-C3NNT would be promising for TPA adsorption in drug delivery applications. [ABSTRACT FROM AUTHOR]

To address the challenge of dense connectors for cryogenic systems, we describe a face-to-face cable connection approach for connecting microwave superconducting stripline cables. The approach provides a space-efficient and reliable interconnect technology for future cryogenic and quantum technology applications at microwave frequencies. A self-aligned assembly was used and was comprised of bottom and top alignment molybdenum pieces and flexible superconducting stripline cables. SU-8, a photodefinable epoxy-based photoresist, was used to form structures defined on the bottom and top Mo pieces to provide alignment and distribute mechanical pressure in the contact regions of the cables. We measured critical transition temperatures in the range of 8.6-9.0 K and an average critical current of 10.2 mA. Time-domain reflectometry measurements at 4.2 K showed a relatively small impedance discontinuity at the connection point and minimal microwave power dependency was observed. This cable-to-cable connection approach for thin, flexible superconducting cables shows promising results as a solution for dense signal integration for microwave and DC interconnects at cryogenic temperatures. [ABSTRACT FROM AUTHOR]

In recent years, the theory of convex mappings has gained much more attention due to its massive utility in different fields of mathematics. It has been characterized by different approaches. In 1929, G. H. Hardy, J. E. Littlewood, and G. Polya established another characterization of convex mappings involving an ordering relationship defined over R n known as majorization theory. Using this theory many inequalities have been obtained in the literature. In this paper, we study Hermite–Hadamard type inequalities using the Jensen–Mercer inequality in the frame of q ˙ -calculus and majorized l-tuples. Firstly we derive q ˙ -Hermite–Hadamard–Jensen–Mercer (H.H.J.M) type inequalities with the help of Mercer's inequality and its weighted form. To obtain some new generalized (H.H.J.M)-type inequalities, we prove a generalized quantum identity for q ˙ -differentiable mappings. Next, we obtain some estimation-type results; for this purpose, we consider q ˙ -identity, fundamental inequalities and the convexity property of mappings. Later on, We offer some applications to special means that demonstrate the importance of our main results. With the help of numerical examples, we also check the validity of our main outcomes. Along with this, we present some graphical analyses of our main results so that readers may easily grasp the results of this paper. [ABSTRACT FROM AUTHOR]

Quantum machine learning (QML) focuses on machine learning models developed explicitly for quantum computers. Availability of the first quantum processor led to further research, particularly exploring possible practical applications of QML algorithms in the remote sensing field. The demand for extensive field data for remote sensing applications has started creating bottlenecks for classical machine learning algorithms. QML is becoming a potential solution to tackle big data problems as it can learn from fewer data. This paper presents a QML model based on a quantum support vector machine (QSVM) to classify Holm Oak trees using PRISMA hyperspectral Imagery. Implementation of quantum models was carried on a quantum simulator and a real-time superconducting quantum processor of IBM. The performance of the QML model is validated in terms of dataset size, overall accuracy, number of qubits, training and predicting speed. Results were indicative that (i) QSVM offered 5% higher accuracy than classical SVM (CSVM) with 50 samples and ≥12 qubits/feature dimensions whereas with 20 samples at 16 Qubits/feature dimension, (ii) training time for QSVM at maximum accuracy was 284 s with 50 samples and with 20 samples was 53.68 s and (iii) predicting time for 400 pixels using the QSVM model trained with 50 samples dataset was 5243 s whereas with 20 samples dataset was 2845 s. Results were indicative that QML offers better accuracy but lack training and predicting speed for hyperspectral data. Another observation is that predicting speed of QSVM depends on the number of samples used to train the model. [ABSTRACT FROM AUTHOR]

In this paper the problem of tomographic reconstruction of states is investigated within the so-called Schwinger’s picture of quantum mechanics in which a groupoid is associated with every quantum system. The attention is focussed on spin tomography: in this context the groupoid of interest is the groupoid of pairs over a finite set. In a nutshell, this groupoid is made up of transitions between all possible pairs of outcomes belonging to a finite set. In addition, these transitions possess a partial composition rule, generalizing the notion of groups. The main goal of the paper consists in providing a reconstruction formula for states on the groupoid-algebra associated with the observables of the system. Using the group of bisections of this groupoid, which are special subsets in one-to-one correspondence with the outcomes, a frame is defined and it is used to prove the validity of the tomographic reconstruction. The special case of the set of outcomes being the set of integers modulo n, with n odd prime, is considered in detail. In this case the subgroup of discrete affine linear transformations, whose graphs are linear subspaces of the groupoid, provides a quorum in close analogy with the continuous case. [ABSTRACT FROM AUTHOR]

We introduce a notion of Kolmogorov complexity of unitary transformation, which can (roughly) be understood as the least possible amount of information required to fully describe and reconstruct a given finite unitary transformation. In the context of quantum computing, it corresponds to the least possible amount of data to define and describe a quantum circuit or quantum computer program. Our Kolmogorov complexity of unitary transformation is built upon Kolmogorov "qubit complexity" of Berthiaume, W. Van Dam and S. Laplante via mapping from unitary transformations to unnormalized density operators, which are subsequently "purified" into unnormalized vectors in Hilbert space. We discuss the optimality of our notion of Kolmogorov complexity in a broad sense. [ABSTRACT FROM AUTHOR]