Search Results (309)

Active control of multi-mode light–matter interactions is crucial for advancing quantum photonic technologies. Although triple-mode plasmon–exciton systems involving two distinct excitonic transitions offer a pathway to multi-level polaritonic states, achieving reversible electrical tuning at room temperature remains challenging. Here, we numerically investigate an electrically tunable triple-mode strong-coupling system comprising a J-aggregate-coated Au@Ag nanorod coupled with monolayer WS${}_2$. The simulated spectra show a UPB–LPB energy separation of approximately 239 meV near the zero-detuning condition. A modest gate voltage (2.0 V to 3.8 V) selectively modulates the middle and lower polariton branches over ∼46 meV, while the upper branch remains largely unaffected. This selective control is elucidated via a triple-mode coupled-oscillator model and Hopfield coefficient analysis, linking the polariton response to the excitonic composition. These results establish a framework for electrically reconfigurable multi-level polaritonic devices, offering potential for ultracompact optical modulators, high-sensitivity multiplexed sensors, and programmable quantum photonic circuits. Full article

In this work, a multi-objective quantum particle swarm optimization (QPSO) algorithm is proposed to address the optimal scheduling of non-dispatchable sources in a microgrid energy management system (MGEMS) for residential areas under utility-induced demand-side management (DSM) programs. While taking economic and environmental aspects into account, the goal is to maximize energy management by integrating a variety of distributed generation (DG) units with an energy storage device. Using real-time meteorological data, two case studies were analyzed and simulated using MATLAB/Simulink R2025b. The simulation results reveal that the optimum optimization outcome among the case studies is obtained at a higher DSM load participation level of 10%. Without the involvement of DSM, MG’s producing units in the first case had the highest carbon emissions of 797.110 kg and an overall operating cost of 267.10 €. Similarly, with the involvement of DSM, the second case had the lowest overall operating cost of 155.01 € and the lowest carbon emissions of 748.731 kg. The second case, which has optimal DG scheduling, is the suggested way to improve microgrid efficiency and provide a dependable power supply with low operating costs and emission reduction. Full article

Understanding the atomic-scale mechanisms of coal pyrolysis is essential for efficient coal utilization and carbon-neutral energy strategies, yet conventional computational approaches often struggle to balance between the high accuracy of quantum-chemical calculations and the efficiency of reactive force fields. To overcome this limitation, we proposed a multiscale computational framework integrating high-throughput density functional theory (DFT) calculations, ReaxFF-based configuration sampling, YARP reaction enumeration, and DPA3-based machine learning potentials (MLPs). Two coal-specific MLPs, DPA3-coal and DPA3-coal@dftb, were constructed and systematically benchmarked on both small molecular systems and larger C20–30 coal fragments extracted from MD simulations. DPA3-coal@dftb model demonstrated significantly improved accuracy over ReaxFF in predicting energies and atomic forces while maintaining good transferability. To balance computational efficiency and accuracy in large-scale simulations, the DPA3-coal model was employed to perform accelerated reactive molecular dynamics simulations of a Solomon-type bituminous coal molecule from 1600 to 2600 K. The simulations revealed temperature-dependent evolution of coke, tar, and gas products, including secondary condensation and deep-cracking processes at elevated temperatures. Higher-level DFT calculations further confirmed the thermodynamic consistency of key reaction pathways involving radical formation, H-transfer, recombination, and CO generation, indicating that coal-specific MLPs provide an effective atomistic tool for investigating mechanistic trends in coal pyrolysis. Full article

We investigate the time evolution of bipartite quantum correlations in the ground-state hyperfine manifold of the hydrogen atom subjected to an external magnetic field and independent Markovian dephasing. Treating the electron–proton spin pair as an effective two-qubit system, we derive the exact solution of the Lindblad master equation for an X-shaped initial state and quantify the dynamics using three complementary measures: entanglement of formation (through concurrence), quantum steering (through the CJWR inequality) and Bell nonlocality (through normalized CHSH violation). The dynamics are obtained within a unified open-system framework that combines hyperfine interaction, Zeeman splitting, and Markovian dissipation in a single analytically solvable Lindblad model, allowing a complete operator-level characterization of the correlation decay. This exact treatment provides a transparent link between the underlying spectral structure of the Hamiltonian and the observed hierarchy in the robustness of quantum correlations. Our results reveal that all three quantities exhibit damped oscillations whose frequency and decay rate are strongly tuned by the proton magnetic parameter through the Zeeman splitting. While entanglement decays relatively quickly, steering persists noticeably longer and Bell nonlocality proves to be the most fragile, confirming the expected hierarchy of quantum correlations under local dephasing. The external magnetic field emerges as a practical control knob that can extend the lifetime of these resources even in the presence of noise. These findings provide a clear physical picture of how hyperfine coupling, Zeeman effects, and environmental fluctuations jointly govern quantum coherence in atomic spin systems, with direct implications for spin-based quantum technologies and fundamental tests of nonlocality in realistic laboratory settings. Full article

We develop an information-geometric framework for describing quantum state-update processes associated with measurement and statistical distinguishability. The approach is based on the quantum relative entropy and the quantum Fisher information metric, which together induce a natural Riemannian geometry on the manifold of quantum states. Using the second-order expansion of relative entropy, we show how the Fisher metric governs the local structure of distinguishability between nearby states and defines a corresponding thermodynamic length. This geometric structure provides an effective description of finite quantum state transitions in terms of fluctuation geometry and information-space distance. The formalism is applied to thermal two-level systems and harmonic oscillator states, illustrating how the Fisher metric encodes susceptibilities, fluctuations, and geometric transition costs. We also discuss the relation between thermodynamic length, dissipation bounds, and optimal paths in state space. Within this framework, wave function collapse is interpreted not as a microscopic dynamical mechanism, but as an effective state-update process that admits a geometric characterization in the manifold of density operators. The resulting perspective unifies concepts from quantum information theory, thermodynamics, and differential geometry within a common operational framework based on statistical distinguishability. Possible connections with quantum speed limits, entanglement geometry, and holographic relations between relative entropy and gravitational dynamics are briefly discussed. Full article

Two indolocarbazole-based host materials, namely, 5,7-di([1,1′:3′,1″-terphenyl]-5′-yl)-3,9-di-tert-butyl-5,7-dihydroindolo[2,3-b]carbazole (InCz-TP) and 4,4′-(3,9-di-tert-butylindolo[2,3-b]carbazole-5,7-diyl)bis(N,N-diphenylaniline) (InCz-TPA), were designed and synthesized for application in red phosphorescent organic light-emitting diodes (OLEDs). In these systems, bulky substituents possessing tailored electron-donating characteristics were incorporated into the indolocarbazole core. The triplet energy levels were experimentally determined to be 2.81 eV and 2.78 eV, indicating that both materials are appropriate host candidates for red phosphorescent OLEDs. Device evaluations revealed that InCz-TPA delivered superior performance compared to InCz-TP, reaching a maximum external quantum efficiency (EQE) of 8.50%, which corresponds to a 36% enhancement. Both devices exhibited an electroluminescence peak at 628 nm along with nearly identical CIE coordinates of (0.678, 0.317) and (0.680, 0.318), suggesting efficient energy transfer between host and dopant. Full article

Molecular-scale detection based on quantum tunnelling is promising for molecular electronics and high-sensitivity analysis, owing to its sensitivity to molecular structure and energy levels. However, conventional two-electrode tunnelling measurements suffer from overlapping conductivity of different molecules, limiting molecular discrimination in complex systems. To address this, we propose an electrochemical-gate-controlled nanoscale tunnelling strategy that expands the two-electrode system to a three-electrode configuration via a tunable gate potential, enabling the differentiation of distinct molecules at near-single-molecule sensitivity. Scanning the gate potential under constant tunnelling bias modulates the alignment between molecular orbitals and the electrode Fermi level, altering the statistical characteristics of molecular tunnelling transport. Experimental results show that target molecules induce a bimodal distribution of tunnelling current (background and molecule-correlated channels), with the second peak exhibiting distinct gate potential dependence. Comparative analysis of ascorbic acid (AA), acetylcholine (ACh), and uric acid (UA) reveals unique trajectories of characteristic peaks with gate potential, forming an electrochemical gate response fingerprint. This gate-dependent conductance trajectory provides a novel statistical dimension for molecular recognition, enabling differentiation of distinct molecules. Full article

While photonic quantum memristors (PQMs) offer promising avenues for neuromorphic computing, their performance is inherently affected by hardware noise, particularly photon loss and phase fluctuations. This study systematically investigates the impact of photon loss and phase fluctuations on PQM dynamics by employing the noisy gates approach, which integrates dissipative effects directly into the device evolution. At the device level, we demonstrate that photon loss alters the dynamic trajectory of individual PQMs. It induces evident deformations in the characteristic pinched hysteresis loops, with the degradation of non-Markovian memory effects being particularly pronounced at shorter integration times. To further evaluate system-level implications, we construct a two-PQM network to execute the NARMA2 time-series prediction task. Under noiseless conditions, the network exhibits strong representation capabilities with a normalized mean square error (NMSE) of 0.0448. However, performance degrades markedly under incoherent evolution; the NMSE increases to 0.1552, 0.2567, and 0.3056 for photon loss probabilities of 0.2, 0.4, and 0.5, respectively. Furthermore, at a high photon loss probability of 0.5, extending the integration time fails to compensate for the degradation and instead exacerbates the prediction error. These findings indicate that photon loss impairs both individual device dynamics and network-level processing, emphasizing the critical need for loss-tolerant architectures in deploying PQM networks. Full article

Post-quantum cryptographic implementations in Internet-of-Things (IoT) devices are significantly threatened by physical side-channel attacks, where practical attack risks are increased by physical accessibility and resource limitations. In particular, recent work has shown that belief propagation-based attacks can recover secret keys from lattice-based digital signatures using only a single side-channel trace of the Number Theoretic Transform (NTT). This work introduces the Quantum-Randomized Number Theoretic Transform (QR-NTT), an implementation-level defense mechanism that integrates quantum-derived entropy directly into the execution flow of lattice-based signature algorithms. Rather than treating randomness as a static input, QR-NTT uses quantum entropy to introduce controlled variability in execution ordering, arithmetic factor usage, and memory access behavior while preserving mathematical correctness and constant-time execution. The proposed framework is designed for embedded platforms and remains compatible with existing post-quantum cryptographic standards and IoT communication protocols. A complete implementation on an ARM Cortex-M4 platform, coupled with commercial quantum random number generator (QRNG) hardware, demonstrates that QR-NTT significantly degrades the effectiveness of template matching and belief propagation attacks. Experimental evaluation shows a reduction in single-trace attack success rates from over 90% to below 3% and an increase of approximately two orders of magnitude in the number of traces required for successful key recovery. These security gains are achieved with moderate overheads of 18.3% in execution time and 1.8 KB of additional memory while remaining well within practical IoT constraints. The results indicate that quantum-derived entropy can be leveraged as a practical implementation-level defense against physical attacks, complementing algorithmic post-quantum security. QR-NTT demonstrates a viable path toward strengthening the real-world resilience of post-quantum IoT systems without sacrificing deployability. Full article

Quantum computing challenges the long-term security assumptions of blockchain systems that rely on classical public-key cryptography, motivating the adoption of post-quantum cryptography and quantum key distribution (QKD). This review maps research fronts at the intersection of blockchain and quantum-safe security, linking threat assumptions to post-quantum mechanisms, blockchain layers, and QKD positioning. Records were retrieved from Scopus and Web of Science using a two-block query and filtered through a PRISMA-guided workflow for bibliometric mapping. The final corpus comprises 648 journal articles and shows accelerated publication growth after 2023, with scientific production concentrated in a small set of leading countries. Keyword structures indicate that IoT-centric deployments dominate the semantic backbone, where authentication and intelligent methods co-occur with blockchain security primitives, while post-quantum and privacy-preserving constructs form a cohesive technical stream. QKD appears as a distinct but more specialized theme, typically discussed at the system level and shaped by infrastructure and scalability constraints. Overall, the literature is moving from conceptual risk articulation toward engineering integration; however, progress is limited by inconsistent reporting of threat models, post-quantum parameter sets, and ledger-level cost trade-offs, highlighting the need for auditable and reproducible evaluation. Full article

A graph-theoretical approach to the analysis of motion and rest in many-body systems is developed. Point bodies are represented as vertices of a complete bi-colored graph, termed the motion–rest graph (MRG). Two vertices are connected by a rust-colored edge when the corresponding bodies are at rest relative to each other; that is, when their mutual distance remains constant in time, bodies moving relative to each other are connected by a cyan edge. It is shown that the logical structure of the relation “to be at rest relative to each other” determines the combinatorial structure of the graph. For one-dimensional motion in classical mechanics and special relativity, this relation is reflexive, symmetric, and transitive, and therefore defines an equivalence relation. As a result, rust edges form disjoint complete cliques corresponding to rest-clusters, and the MRG becomes a semi-transitive complete bi-colored graph that is completely determined by the partition of the bodies into equivalence classes. It is proven that any such graph on five vertices necessarily contains a monochromatic triangle. For two- and three-dimensional motion, the transitivity of relative rest generally fails because constant mutual distance does not imply an equality of velocities in the presence of rotational degrees of freedom. In this case, the MRG is non-transitive, and the Ramsey threshold becomes the classical value R(3, 3) = 6. The approach is extended to mixed sets containing moving bodies and reference points, including the center of mass of the system. Generalizations to general relativity and quantum mechanics are also discussed. In general relativity, transitivity of relative rest is generically lost because global rigid congruences do not generally exist. In quantum mechanics, exact transitivity survives only at the level of idealized delocalized eigenstates, whereas for physically realizable localized states, the notion of mutual rest becomes only approximate. The results demonstrate that the interplay between kinematics, logical properties of relational motion, and Ramsey-type combinatorial constraints gives rise to unavoidable ordered substructures in many-body systems. Full article

Consciousness presents a structural puzzle: a unified, context-sensitive, globally integrated mode of experience emerging from distributed neural dynamics. While classical neuroscience has mapped synaptic, oscillatory, and network-level mechanisms with increasing precision, debate persists as to whether classical formalisms fully capture the integrative and contextual features of conscious processing. This review examines whether quantum principles offer explanatory leverage in two distinct senses: as formal mathematical frameworks for modeling contextual cognition, and as mechanistic hypotheses proposing biologically instantiated non-classical states. We surveyed empirical and theoretical developments spanning zero-quantum-coherence in MRI signals, entanglement-structured learning paradigms, quantum-inspired computational models, and proposed neural substrates, including microtubules, nuclear spins, and photonic architectures. Although certain findings have been interpreted as consistent with a non-classical structure, no study to date has demonstrated entanglement, long-lived coherence, or collapse dynamics in neural tissue under operational criteria comparable to those used in controlled quantum systems. Replication remains limited, biological entanglement witnesses are not yet established, and nonlinear classical dynamics can reproduce many putative quantum signatures. Accordingly, the decisive question is not whether the brain is quantum, but whether its dynamics exceed the explanatory reach of rigorously defined classical models. Progress hinges on replication, adversarial scrutiny, and operational criteria precise enough to discriminate genuine non-classical correlations from classical complexity. Whether quantum mechanisms ultimately prove necessary or refined classical models remain sufficient, this inquiry compels a deeper understanding of integration, contextuality, and the physical constraints shaping conscious experience. Full article

Reliable video transmission over error-prone channels remains a significant challenge due to the inherent trade-off between compression efficiency and noise resilience in conventional systems. To address these issues, this paper introduces a novel quantum Fourier transform (QFT)-based framework that integrates video compression and transmission within a unified quantum frequency-domain representation. The framework converts video data into a classical bitstream and maps it onto multi-qubit quantum states with variable encoding sizes (n), enabling flexible control over compression levels. Through the application of the QFT, these states are transformed into the frequency domain, where only selected coefficients are transmitted to reduce bandwidth requirements. At the receiver, the transmitted components are used to reconstruct the full representation, followed by inverse transformation and decoding to recover the video sequence. The performance of the proposed framework is evaluated using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and video multi-method assessment fusion (VMAF). The results demonstrate that increasing the number of qubits enables exponential compression, achieving ratios up to $2^n:1$, while maintaining high reconstruction quality under ideal transmission conditions. However, higher-qubit configurations exhibit increased sensitivity to channel noise, leading to a more rapid degradation as the signal-to-noise ratio decreases. In contrast, lower-qubit configurations provide improved robustness, maintaining more stable reconstruction quality under noisy conditions, albeit with reduced compression efficiency. Among the evaluated configurations, the two-qubit system achieves an effective trade-off, providing a compression ratio of $4:1$ while maintaining strong visual and structural fidelity along with enhanced resilience to channel impairments. Full article

We investigate the optical response properties of an atom-assisted spinning optomechanical system, in which a spinning optical resonator is coupled simultaneously to a two-level atomic ensemble and a mechanical resonator driven by a weak pump field. Remarkably, we demonstrate that by simply reversing the rotation direction, the system can be switched between a low-absorption electromagnetic and optomechanically induced transparency state and a high-absorption state, constituting a form of non-reciprocal optical control at the quantum level. Furthermore, by tuning the phase difference between the mechanical pump and the probe field, direction-dependent switching between absorption and gain is achieved. These non-reciprocal effects originate from the Sagnac-induced frequency shift in the optical mode, which leads to distinct optomechanical and atom–cavity couplings for opposite spinning directions. We also show that the absorption spectrum can be modulated by the angular velocity and the atomic number. Our results indicate that the optical properties of the hybrid system can be manipulated via the angular velocity, phase difference, and atom number, with potential applications in chiral photonic communications. Full article

The temporal stability and reproducibility of qubit parameters are critical for the long-term operation and maintenance of superconducting quantum processors. In this work, we present a comprehensive longitudinal characterization of 27 frequency-tunable transmon qubits spanning over one year across four thermal cycles. Our results establish a distinct hierarchy of stability for superconducting hardware. We find that the intrinsic device parameters determining the qubit frequency and the baseline energy relaxation times ($T_1$) exhibit high robustness against thermal stress, characterized by frequency deviations typically confined within 0.5% and non-degraded coherence baselines. In stark contrast, the environmental variables, specifically the background magnetic flux offsets and the microscopic landscape of two-level system (TLS) defects, undergo a significant stochastic reconfiguration after each cycle. By employing frequency-dependent relaxation spectroscopy and a quantitative metric, the $T_1$ Spectral Topography Fidelity, we demonstrate that thermal cycling acts as a “hard reset” for the local defect environment. This process introduces a level of spectral randomization equivalent to thousands of hours of continuous low-temperature evolution. These findings confirm that while the fabrication quality is preserved, the specific noise realization is statistically distinct for each thermal cycle, necessitating automated recalibration strategies for large-scale quantum systems. Full article