We demonstrate a toroidal classification for quantum spin systems, revealing an intrinsic geometric duality within this structure. Through our classification and duality, we reveal that various bipartite quantum featu...
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We demonstrate a toroidal classification for quantum spin systems, revealing an intrinsic geometric duality within this structure. Through our classification and duality, we reveal that various bipartite quantum features in magnon systems can manifest equivalently in both bipartite ferromagnetic and antiferromagnetic materials, based upon the availability of relevant Hamiltonian parameters. Additionally, the results highlight the antiferromagnetic regime as an ultrafast dual counterpart to the ferromagnetic regime, both exhibiting identical capabilities for quantum spintronics and technological applications. Concrete illustrations are provided, demonstrating how splitting and squeezing types of two-mode magnon quantum correlations can be realized across ferro- and antiferromagnetic regimes.
This paper presents a tool for automatic and interactive visualization of game plots, which can be used to check whether the designers’ work meets the constraints of the world, to help testers control played game sto...
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Current review papers in the area of Affective Computing and Affective Gaming point to a number of issues with using their methods in out-of-the-lab scenarios, making them virtually impossible to be deployed. On the c...
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This paper concerns the computer-aided process of designing architectural objects in the form of 3D primitive configurations following the general-to-detail principle. Design objects are represented by labeled specifi...
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The internal structure of nucleons and interactions between their components have been debated ever since the existence of quarks was postulated. A lot of experimental evidence has been gathered, indicating that nucle...
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KLOE and KLOE-2 data (almost 8 fb−1) constitute the largest sample ever collected at an electron-positron collider operating at the φ peak resonance. In total it corresponds to the production of about 24 billion of ...
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In this work we present the concept and verification of the ULE cavity based clean-up system for a remote terminal of an optical atomic clock. Thanks to this solution the short-term stability of the signal provided by...
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We discuss two approaches which, by applying the screening method, permit one to include the long range proton-proton (pp) Coulomb force in proton-deuteron (pd) momentum-space scattering calculations. In the first one...
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We discuss two approaches which, by applying the screening method, permit one to include the long range proton-proton (pp) Coulomb force in proton-deuteron (pd) momentum-space scattering calculations. In the first one, based on the Alt-Grassberger-Sandhas (AGS) equation, presented in Phys. Rev. C 71, 054005 (2005) and Phys. Rev. C 72, 054004 (2005), one needs to renormalize elastic scattering amplitude before calculating observables. In the second treatment, proposed by us in Eur. Phys. J. A 41, 369 (2009), Eur. Phys. J. A 41, 385 (2009), and arXiv:2310.03433 [***], this renormalization is avoided. For the proton induced deuteron breakup reaction both approaches require renormalization of the corresponding transition amplitudes. We derive the basic equations underlying both methods under the assumption that all contributing partial wave states are included and explain why in our approach renormalization of the elastic scattering amplitude is superfluous. We show that in order to take into account in the screening limit all partial waves it is required that four additional terms, based on the three-dimensional and partial-wave projected pp Coulomb t matrices, identical for both approaches, must appear in transition amplitudes. We investigate the importance of these terms for elastic pd scattering below the breakup threshold.
Experiments in affective computing are based on stimulus datasets that, in the process of standardization, receive metadata describing which emotions each stimulus evokes. In this paper, we explore an approach to crea...
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This work presents the problem of learning an unknown von Neumann measurement of dimension d using indefinite causal structures. In the considered scenario, we have access to N copies of the measurement. We use the fo...
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This work presents the problem of learning an unknown von Neumann measurement of dimension d using indefinite causal structures. In the considered scenario, we have access to N copies of the measurement. We use the formalism of process matrices to store information about the given measurement that later will be used to reproduce its best possible approximation. Our goal is to compute the maximum value of the average fidelity function Fd(N) of our procedure. We prove that Fd(N)=1−Θ(1N2) for arbitrary but fixed dimension d. Furthermore, we present the SDP program (semi-definite program) for computing Fd(N). Basing on the numerical investigation, we show that for the qubit von Neumann measurements using indefinite causal learning structures provide better approximation than quantum networks, starting from N≥3.
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