Search Results (689)

We present a graphical framework to represent entanglement in three-qubit states. The geometry associated with each entanglement class and type is analyzed, revealing distinct structural features. We explore the connection between this geometric perspective and the tangle, deriving bounds that depend on the entanglement class. Based on these insights, we conjecture a purely geometric expression for both the tangle and Cayley’s hyperdeterminant for non-generic states. As an application, we analyze the energy eigenstates of physical Hamiltonians, identifying the sufficient conditions for genuine tripartite entanglement to be robust under symmetry-breaking perturbations and level repulsion effects. Full article

Ultra-high-molecular-weight polyethylene (UHMWPE) is a pivotal material in engineering and biomedical applications due to its exceptional mechanical strength, wear resistance, and impact performance. However, its extreme melt viscosity, caused by extensive chain entanglements, severely limits processability via conventional melt-processing techniques. Recent advances in catalytic synthesis have enabled the production of disentangled UHMWPE (dis-UHMWPE), which exhibits enhanced processability while retaining superior mechanical properties. Notably, heterogeneous catalytic systems, utilizing supported fluorinated bis (phenoxy-imine) titanium (FI) catalysts, polyhedral oligomeric silsesquioxanes (POSS)-modified Z-N catalysts, and other novel catalysts, have emerged as promising solutions, combining structural control with industrial feasibility. Moreover, optimizing polymerization conditions further enhances chain disentanglement while maintaining ultra-high molecular weights. These systems utilize nanoscale supports and ligand engineering to spatially isolate active sites, tailor the chain propagation/crystallization kinetics, and suppress interchain entanglement during polymerization. Furthermore, characterization techniques such as melt rheology and differential scanning calorimetry (DSC) provide critical insights into chain entanglement, revealing distinct reorganization kinetics and bimodal melting behavior in dis-UHMWPE. This development of hybrid catalytic systems opens up new avenues for solid-state processing and industrial-scale production. This review highlights recent advances concerning interaction between catalyst design, polymerization control, and material performance, ultimately unlocking the full potential of UHMWPE for next-generation applications. Full article

We present a numerical investigation comparing two entanglement generation protocols in finite XX spin chains with varying spin magnitudes ($s=1/2,1,3/2$). Protocol 1 (P1) relies on staggered couplings to steer correlations toward the ends of the chain. At the same time, Protocol 2 (P2) adopts a dual-port architecture that uses optimized boundary fields to mediate virtual excitations between terminal spins. Our results show that P2 consistently outperforms P1 in all spin values, generating higher-fidelity entanglement in shorter timescales when evaluated under the same system parameters. Furthermore, P2 exhibits superior robustness under realistic imperfections, including diagonal and off-diagonal disorder, as well as dephasing noise. To further assess the resilience of both protocols in experimentally relevant settings, we employ the pseudomode formalism to characterize the impact of non-Markovian noise on the entanglement dynamics. Our analysis reveals that the dual-port mechanism (P2) remains effective even when memory effects are present, as it reduces the excitation of bulk modes that would otherwise enhance environment-induced backflow. Together, the scalability, efficiency, and noise resilience of the dual-port approach position it as a promising framework for entanglement distribution in solid-state quantum information platforms. Full article

Preserving quantum entanglement in multipartite systems under environmental decoherence is a critical challenge for quantum information processing. In this work, we investigate the dynamics of W-type entanglement in a system of three photons, focusing on the effects of Markovian and non-Markovian decoherence regimes. Using the lower bound of concurrence (LBC) as a measure of entanglement, we analyze the time evolution of the LBC for photons initially prepared in a W state under the influence of dephasing noise. We explore the dependence of entanglement dynamics on system parameters such as the dephasing angle and refractive-index difference, alongside environmental spectral properties. Our results, obtained within experimentally feasible parameter ranges, reveal how the enhancement of entanglement preservation can be achieved in Markovian and non-Markovian regimes according to the system parameters. These findings provide valuable insights into the robustness of W-state entanglement in tripartite photonic systems and offer practical guidance for optimizing quantum protocols in noisy environments. Full article

The entanglement entropy of a random tensor network (RTN) is studied using tools from free probability theory. Random tensor networks are simple toy models that help in understanding the entanglement behavior of a boundary region in the anti-de Sitter/conformal field theory (AdS/CFT) context. These can be regarded as specific probabilistic models for tensors with particular geometry dictated by a graph (or network) structure. First, we introduce a model of RTN obtained by contracting maximally entangled states (corresponding to the edges of the graph) on the tensor product of Gaussian tensors (corresponding to the vertices of the graph). The entanglement spectrum of the resulting random state is analyzed along a given bipartition of the local Hilbert spaces. The limiting eigenvalue distribution of the reduced density operator of the RTN state is provided in the limit of large local dimension. This limiting value is described through a maximum flow optimization problem in a new graph corresponding to the geometry of the RTN and the given bipartition. In the case of series-parallel graphs, an explicit formula for the limiting eigenvalue distribution is provided using classical and free multiplicative convolutions. The physical implications of these results are discussed, allowing the analysis to move beyond the semiclassical regime without any cut assumption, specifically in terms of finite corrections to the average entanglement entropy of the RTN. Full article

With the widespread adoption of mobile devices and the increasing availability of user trajectory data, accurately predicting the next location a user will visit has become a pivotal task in location-based services. Despite recent progress, existing methods often fail to effectively disentangle the diverse and entangled behavioral signals, such as collaborative user preferences, global transition mobility patterns, and geographical influences, embedded in user trajectories. To address these challenges, we propose a novel framework named Multi-Perspective Hypergraphs with Contrastive Learning (MPHCL), which explicitly captures and disentangles user preferences from three complementary perspectives. Specifically, MPHCL constructs a global transition flow graph and two specialized hypergraphs: a collective preference hypergraph to model collaborative check-in behavior and a geospatial-context hypergraph to reflect geographical proximity relationships. A unified hypergraph representation learning network is developed to preserve semantic independence across views through a dual propagation mechanism. Furthermore, we introduce a cross-view contrastive learning strategy that aligns multi-perspective embeddings by maximizing agreement between corresponding user and location representations across views while enhancing discriminability through negative sampling. Extensive experiments conducted on two real-world datasets demonstrate that MPHCL consistently outperforms state-of-the-art baselines. These results validate the effectiveness of our multi-perspective learning paradigm for next-location prediction. Full article

This research investigates a novel hybrid E-glass fiber coated with a thin amorphous carbon (coke) layer, referred to as GF@C, designed to enhance the affinity of fiber with a polymer matrix. Acrylonitrile butadiene styrene (ABS), an engineering thermoplastic, was selected as the matrix to form the composite. The carbon coating was produced by pyrolyzing a lubricant oil (Lo) layer applied to the glass fiber strands. To promote the formation of graphite crystallites during carbonization, a small amount (x wt.% of Lo) of coronene (Cor) was added to Lo as a dopant. The resulting doped fibers, denoted GF@C_Lo-Cor(x%), were embedded in ABS at 70 wt.%, leading to significant improvements in mechanical properties. At the optimal doping level (x = 5), the composite achieved a Young’s modulus of 1.02 GPa and a tensile strength of 6.96 MPa, substantially higher than the 0.4 GPa and 3.81 MPa observed for the composite with the pristine GF. This enhancement is attributed to a distribution of graphite crystallites and their graphitization extent in the carbon coating, which improves interfacial bonding and increases chain entanglement. Additionally, GF@C_Lo-Cor(x%)–ABS composites (x = 0 and 5) exhibit significantly higher dielectric constant–temperature profiles than GF–ABS, attributed to the formation of diverse chain adsorption states on the C-coating. Full article

We introduce a quantum integrated-information measure $\Phi$ for multipartite states within the Relational Quantum Dynamics (RQD) framework. $\Phi \left(\rho \right)$ is defined as the minimum quantum Jensen–Shannon distance between an n-partite density operator $\rho$ and any product state over a bipartition of its subsystems. We prove that its square root induces a genuine metric on state space and that $\Phi$ is monotonic under all completely positive trace-preserving maps. Restricting the search to bipartitions yields a unique optimal split and a unique closest product state. From this geometric picture, we derive a canonical entanglement witness directly tied to $\Phi$ and construct an integration dendrogram that reveals the full hierarchical correlation structure of $\rho$. We further show that there always exists an “optimal observer”—a channel or basis—that preserves $\Phi$ better than any alternative. Finally, we propose a quantum Markov blanket theorem: the boundary of the optimal bipartition isolates subsystems most effectively. Our framework unites categorical enrichment, convex-geometric methods, and operational tools, forging a concrete bridge between integrated information theory and quantum information science. Full article

Aqueous solutions are not homogeneous and could be considered supramolecular systems. They can emit electromagnetic waves. Electromagnetic emission from one supramolecular system (“source”) can be received by another supramolecular system (“receiver”) without direct contact (distantly). This process represents a transfer of a “molecular signal” and causes changes in conformation and symmetry of the “receiver”. The aim of the current work is to theoretically describe such changes primarily using a solution of the chiral protein interferon-gamma (IFNγ) as an example. We provide theoretical evidence that supramolecular systems of highly diluted (HD) aqueous solutions formed by self-assembly after mechanical activation generate a stronger molecular signal compared to non-activated solutions, due to their higher energy-saturated state. Additionally, molecular signals cause supramolecular systems with complex (including chiral) structures to undergo easier changes in conformation and symmetry compared to simpler systems, enhancing their biological activity. Using statistical physics, we obtained the parameter Ic, characterizing the magnitude of conformational and symmetry changes in supramolecular (including chiral) systems caused by molecular signals. In quantum information science, there is an analogue of the parameter Ic, which characterizes the entanglement depth of quantum systems. This study contributes to the understanding of the physico-chemical basis of distant molecular interactions and opens up new possibilities for controlling the properties of complex biological and chemical systems. Full article

Hyperspectral imaging (HSI) plays a pivotal role in modern agriculture by capturing fine-grained spectral signatures that support crop classification, health assessment, and land-use monitoring. However, the transition from raw spectral data to reliable semantic understanding remains challenging—particularly under fragmented planting patterns, spectral ambiguity, and spatial heterogeneity. To address these limitations, we propose UniHSFormer-X, a unified transformer-based framework that reconstructs agricultural semantics through prototype-guided token routing and hierarchical context modeling. Unlike conventional models that treat spectral–spatial features uniformly, UniHSFormer-X dynamically modulates information flow based on class-aware affinities, enabling precise delineation of field boundaries and robust recognition of spectrally entangled crop types. Evaluated on three UAV-based benchmarks—WHU-Hi-LongKou, HanChuan, and HongHu—the model achieves up to 99.80% overall accuracy and 99.28% average accuracy, outperforming state-of-the-art CNN, ViT, and hybrid architectures across both structured and heterogeneous agricultural scenarios. Ablation studies further reveal the critical role of semantic routing and prototype projection in stabilizing model behavior, while parameter surface analysis demonstrates consistent generalization across diverse configurations. Beyond high performance, UniHSFormer-X offers a semantically interpretable architecture that adapts to the spatial logic and compositional nuance of agricultural imagery, representing a forward step toward robust and scalable crop classification. Full article

Photonic quantum metrology has emerged as a leading platform for quantum-enhanced precision measurements. By taking advantage of quantum resources such as entanglement, quantum metrology enables parameter estimation with sensitivities surpassing classical limits. In this review, we describe the basic tools and recent experimental progress in the determination of an optical phase with a precision that may exceed the shot-noise limit, enabled by the use of nonclassical states of light. We review the state of the art and discuss the challenges and trends in the field. Full article

Users of quantum mechanics are familiar with the concept of a statistical mixture as introduced by von Neumann, and with the use of a density operator in that context. A density operator may also be used in another situation, introduced by Landau, with a transient coupling between the two parts of a quantum bipartite system. But more than fifty years after a clarifying work by Feynman on the subject, a confusion still persists about what we call the Landau-Feynman situation. In this paper we establish that, when facing that situation, the right concept to be used is not the one of a mixed state - be it qualified as proper or improper -, but the one of entanglement. Full article

Quantum repeaters are integral systems to quantum computing and quantum communication as they allow the transfer of information between qubits, particularly over long distances. Because of the “no-cloning theorem,” which says that general quantum states cannot be directly copied, one cannot perform signal amplification in the usual way. The standard approach uses entanglement swapping, in which quantum states are teleported from one (short) segment to the next, using at each step a shared entangled pair. This is the job of the repeater. In general, this requires reliable quantum memories and shared entanglement resources, which are vulnerable to noise and decoherence. It is also difficult to manually create and implement the quantum algorithm for the swap circuit as the size of the system increases. Here, we propose a different approach: to use machine learning to train a repeater node. To demonstrate the feasibility of this method, the system is simulated in MATLAB 2022a. Training is conducted for a system of 2 qubits. It is then scaled up, with no additional training, to systems of 4, 6, and 8 qubits using transfer learning. Finally, the systems are tested in noisy conditions. The results show that the scale-up is very effective and relatively easy, and the effects of noise and decoherence are $reduced$ as the size of the system increases. Full article

This paper elucidates the Lorentz group, a fundamental subgroup of the Poincaré group. The orbits and little groups associated with the Lorentz group are described in detail, along with their corresponding properties. The Poincaré group is presented. Another fundamental aspect of the Poincaré group is Wigner’s little groups obtained from this group. An in-depth discussion on the cases of both massive and massless relativistic particles within the context of little groups is given. Our examination extends to the properties of various special groups associated with the Poincaré group. Applications of these groups are elaborated by physical examples taken from high-energy physics and optics from both classical and quantum domains. Specifically, covariant harmonic oscillators including entangled states, proton form factors, and the parton picture as proposed by Feynman are discussed. In this context, laser cavities and shear states are also addressed. We lay out the underlying mathematics that connects these apparently disparate realms of physics. Full article

We study the entanglement dynamics of a pair of non-identical interacting atoms (qubits) coupled off-resonance to a single-mode cavity radiation field and exposed to dephasing environments. The qubits are studied starting from various initial states that are disentangled from an initially coherent field. The system models the basic building units of quantum information processing (QIP) platforms under the realistic considerations of asymmetry and external environmental influences. We investigate how introducing a radiation field alters the system’s entanglement dynamics in the presence of dephasing environments, and how it impacts the effects of the dephasing environments themselves. The work examines the problem under various settings of inter-qubit interactions, which are now experimentally controllable in some of the newly engineered artificial qubit systems. We illustrate that only upon introducing the radiation field, the system suffers a terminal disentanglement (followed by no revivals) in a finite time. This behavior is exacerbated when the atoms’ interaction with the field is stronger. Moreover, the effects of the field’s intensity and the atoms’ detunings are vastly sensitive to the choice of the initial state. We also demonstrate that the closer the atoms’ transition frequencies are to resonance with the field, the more pronounced are the effects of strengthening the independent dephasing environments corresponding to some initial states. Those states also suffered a greater reduction in entanglement content when the qubits with stronger atom–field interaction strength were influenced by a stronger independent dephasing environment. In addition, we examined the ability of the correlated dephasing environment to induce a noise-enhanced efficiency in the presence of an external radiation field. We showed that the radiation field could play a decisive role in enabling or restricting noise-enhanced efficiency, but one that is also highly sensitive to the system’s initial state. Full article