Search Results (564)

The emergence of quantum computing poses significant threats to conventional public-key cryptography, driving the urgent need for quantum-resistant cryptographic solutions. While quantum key distribution addresses secure key exchange, its dependency on symmetric keys and point-to-point limitations present scalability constraints. Quantum Asymmetric Encryption (QAE) offers a promising alternative by leveraging quantum mechanical principles for security. This paper presents the first practical implementation of a QAE protocol on IBM Quantum devices, building upon the theoretical framework originally proposed by Yoon et al. We develop a generalized Grover Search Algorithm (GSA) framework that supports non-standard initial quantum states through novel diffusion operator designs, extending its applicability beyond idealized conditions. The complete QAE protocol, including key generation, encryption, and decryption stages, is translated into executable quantum circuits and evaluated on both IBM Quantum simulators and real quantum hardware. Experimental results demonstrate significant scalability challenges, with success probabilities deteriorating considerably for larger systems. The 2-qubit implementation achieves near-perfect accuracy (100% on the simulator, and 93.88% on the hardware), while performance degrades to 78.15% (simulator) and 45.84% (hardware) for 3 qubits, and declines critically to 48.08% (simulator) and 7.63% (hardware) for 4 qubits. This degradation is primarily attributed to noise and decoherence effects in current Noisy Intermediate-Scale Quantum (NISQ) devices, highlighting the limitations of single-iteration GSA approaches. Our findings underscore the critical need for enhanced hardware fidelity and algorithmic optimization to advance the practical viability of quantum cryptographic systems, providing valuable insights for bridging the gap between theoretical quantum cryptography and real-world implementations. Full article

We address the inverse spectral problem for a PT-symmetric Dirac operator with discontinuity conditions imposed along the entire real axis—a configuration that has not been explicitly solved in prior literature. Our approach constructs fundamental solutions via convergent recursive series expansions and establishes their linear independence through a constant Wronskian. We derive explicit formulas for transmission and reflection coefficients, assemble them into a PT-symmetric scattering matrix, and demonstrate how both spectral and scattering data uniquely determine the underlying complex-valued, discontinuous potentials. Unlike classical treatments, which assume smoothness or limited discontinuities, our framework handles full-axis discontinuities within a non-Hermitian setting, proving uniqueness and providing a constructive recovery algorithm. This method not only generalizes existing inverse scattering theory to PT-symmetric discontinuous operators but also offers direct applicability to optical waveguides, metamaterials, and quantum field models where gain–loss mechanisms and zero-width resonances are critical. Full article

In this study, we present the development of a high-average-power, exceptionally stable extreme-ultraviolet (EUV) laser source based on a high-order harmonic generation (HHG) technique. The spectrum of an ytterbium-doped laser is broadened through self-phase modulation (SPM) in a gas-filled hollow fiber and compressed down to 25.3 fs for efficient harmonic generation. The high harmonics are generated in a krypton (Kr) gas cell, delivering a total power of 241 μW within the 30–60 nm spectral range, corresponding to a single harmonic output of 166 μW at a central wavelength of 46.8 nm. Notably, the system demonstrates good power stability with a root-mean-square (RMS) deviation of only 1.95% over 12 h of continuous operation. This advanced light source holds great potential for applications in nano- and quantum-material development and in semiconductor wafer defect detection. Future work aims to further enhance the output power in the 30–60 nm band to the milliwatt level, which would significantly bolster scientific research and technological development in related fields. Full article

Photocatalytic hydrogen evolution from water is a sustainable approach to producing clean energy and promoting sustainable development. However, the low efficiency of photocatalysts under visible light and even near-infrared irradiation remains the current bottleneck in photocatalytic overall water splitting (POWS). Herein, we introduce an Al₂O₃/InP/Al photocatalyst with high efficiency and stability, which achieved an apparent quantum efficiency of 0.97% at 500 nm and operated for 10 h without significant performance decay. The high quantum efficiency and stability are attributed to the combination of the electron-rich Al reflective layer and Al₂O₃ protective layer coating, which synergistically facilitate charge separation. Additionally, the newly generated O₂ from water over the Al₂O₃/InP/Al photocatalyst was removed under reduced pressure, thus inhibiting InP photocorrosion and enabling POWS under visible light irradiation. This study offers valuable insights for the development of efficient solar photocatalysts for water splitting. Full article

In this work, we propose a novel quantum-informed epistemic framework that extends the classical notion of probability by integrating plausibility, credibility, and possibility as distinct yet complementary measures of uncertainty. This enriched quadruple (P, Pl, Cr, Ps) enables a deeper characterization of quantum systems and decision-making processes under partial, noisy, or ambiguous information. Our formalism generalizes the Born rule within a multi-valued logic structure, linking Positive Operator-Valued Measures (POVMs) with data-driven plausibility estimators, agent-based credibility priors, and fuzzy-theoretic possibility functions. We develop a hybrid classical–quantum inference engine that computes a vectorial aggregation of the quadruples, enhancing robustness and semantic expressivity in contexts where classical probability fails to capture non-Kolmogorovian phenomena such as entanglement, contextuality, or decoherence. The approach is validated through three real-world application domains—quantum cybersecurity, quantum AI, and financial computing—where the proposed model outperforms standard probabilistic reasoning in terms of accuracy, resilience to noise, interpretability, and decision stability. Comparative analysis against QBism, Dempster–Shafer, and fuzzy quantum logic further demonstrates the uniqueness of architecture in both operational semantics and practical outcomes. This contribution lays the groundwork for a new theory of epistemic quantum computing capable of modelling and acting under uncertainty beyond traditional paradigms. Full article

Squeezed light, a prominent non-classical state of light, exhibits reduced quantum noise in one quadrature component below the standard quantum limit (SQL). The property enables quantum-enhanced precision measurements, surpassing the SQL in quantum sensing applications. This review comprehensively introduces the fundamental concepts, classifications, and experimental generation techniques of squeezed light. It further explores its pivotal role and recent advances in diverse quantum sensing domains, including interferometry, gravitational wave detection, magnetometry, force sensing, biomedical sensing, and quantum radar. The review covers theoretical foundations of squeezed states (including quadrature operators and classification schemes, experimental generation techniques in atomic ensembles, nonlinear crystals, and fibers), fundamentals of quantum sensing with squeezed light (from quantum noise theory to quantum-enhanced metrology), and quantum-enhanced sensing applications across the aforementioned domains. Finally, future challenges and opportunities in the field are discussed. Full article

This paper proposes a quantum algorithm, named Dicke state quantum search (DSQS), to set qubits in the Dicke state $|D_k^n⟩$ of D states in superposition to locate the target inputs or solutions of specific patterns among $2^n$ unstructured input instances, where n is the number of input qubits and $D=\left({\displaystyle \genfrac{}{}{0pt}{}{n}{k}}\right)=\mathrm{O}\left(n^k\right)$ for $min(k,n-k)\ll n/2$. Compared to Grover’s algorithm, a famous quantum search algorithm that calls an oracle and a diffuser O($\sqrt{2^n}$) times, DSQS requires no diffuser and calls an oracle only once. Furthermore, DSQS does not need to know the number of solutions in advance. We prove the correctness of DSQS with unitary transformations, and show that each solution can be found by DSQS with an error probability less than 1/3 through O($n^k$) repetitions, as long as $min(k,n-k)\ll n/2$. Additionally, this paper proposes a classical algorithm, named DSQS-VCP, to generate quantum circuits based on DSQS for solving the k-vertex cover problem (k-VCP), a well-known NP-complete (NPC) problem. Complexity analysis demonstrates that DSQS-VCP operates in polynomial time and that the quantum circuit generated by DSQS-VCP has a polynomial qubit count, gate count, and circuit depth as long as $min(k,n-k)\ll n/2$. We thus conclude that the k-VCP can be solved by the DSQS-VCP quantum circuit in polynomial time with an error probability less than 1/3 under the condition of $min(k,n-k)\ll n/2$. Since the k-VCP is NP-complete, NP and NPC problems can be polynomially reduced to the k-VCP. If the reduced k-VCP instance satisfies $min(k,n-k)\ll n/2$, then both the instance and the original NP/NPC problem instance to which it corresponds can be solved by the DSQS-VCP quantum circuit in polynomial time with an error probability less than 1/3. All statements of polynomial algorithm execution time in this paper apply only to VCP instances and similar instances of other problems, where $min(k,n-k)\ll n/2$. Thus, they imply neither NP ⊆ BQP nor P = NP. In this restricted regime of $min(k,n-k)\ll n/2$, the Dicke state subspace has a polynomial size of $D=\left({\displaystyle \genfrac{}{}{0pt}{}{n}{k}}\right)=\mathrm{O}\left(n^k\right)$, and our DSQS algorithm samples from it without asymptotic superiority over exhaustive enumeration. Nevertheless, DSQS may be combined with other quantum algorithms to better exploit the strengths of quantum computation in practice. Experimental results using IBM Qiskit packages show that the DSQS-VCP quantum circuit can solve the k-VCP successfully. Full article

Recent publications have introduced the concept of quantum-like (QL) bits, along with their associated QL states and QL gate operations, which emerge from the dynamics of complex, synchronized networks. The present work extends these ideas to multi-level QL resources, referred to as QL dits, as higher-dimensional analogs of QL bits. We employ systems of k-regular graphs to construct QL-dits for arbitrary dimensions, where the emergent eigenspectrum of their adjacency matrices defines the QL-state space. The tensor product structure of multi-QL dit systems is realized through the Cartesian product of graphs. Furthermore, we examine the potential computational advantages of employing d-nary QL systems over two-level QL bit systems, particularly in terms of classical resource efficiency. Overall, this study generalizes the paradigm of using synchronized network dynamics for QL information processing to include higher-dimensional QL resources. Full article

We revisit the monotonicity of relative entropy under the action of quantum channels, a foundational result in quantum information theory. Among the several available proofs, we focus on those by Petz and Uhlmann, which we reformulate within a unified, finite-dimensional operator-theoretic framework. In the first part, we examine Petz’s strategy, identify a subtle flaw in his original use of Jensen’s contractive operator inequality, and point out how it was corrected to restore the validity of his line of reasoning. In the second part, we develop Uhlmann’s approach, which is based on interpolations of positive sesquilinear forms and applies automatically to non-invertible density operators. By comparing these two approaches, we highlight their complementary strengths: Petz’s method is more direct and clear; Uhlmann’s method is more abstract and general. Our treatment aims to clarify the mathematical structure underlying the monotonicity of relative entropy and to make these proofs more accessible to a broader audience interested in both the foundations and applications of quantum information theory. Full article

In recent years, quantum network technology has been rapidly developing, with new theories, solutions, and protocols constantly emerging. The breakthrough experiments and achievements are impressive, such as the construction and operation of ultra-long-distance and multi-user quantum key distribution (QKD) networks, the proposal, verification, and experimental demonstration of new network nonlocality characteristics, etc. The results of recent research on QKD and network nonlocality are summarized and analyzed in this paper, including CV-MDI-QKD (continuous-variable measurement-device-independent QKD), TF-QKD (twin-field QKD), AMDI-QKD (asynchronous MDI-QKD), the generalization, sharing, and certification of network nonlocality, as well as the main achievements and related research tools of full network nonlocality and genuine network nonlocality, aiming to identify the current status and future development paths of the QKD and network nonlocality. Full article

Aerial-assisted vehicular edge computing (AVEC) has emerged as a transformative approach to addressing the limitations of traditional vehicular edge computing (VEC) in dynamic vehicular environments. By integrating platforms such as unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites, AVEC systems offer enhanced scalability, flexibility, and responsiveness, enabling efficient resource allocation and adaptive decision-making. This paper presents a comprehensive survey of resource allocation techniques in AVEC, addressing both traditional and reinforcement learning-based approaches. These techniques aim to optimize the allocation of bandwidth, computation, and energy resources across heterogeneous platforms, ensuring reliable and efficient operations in diverse scenarios. Additionally, the study examines key challenges inherent in AVEC, including achieving seamless interoperability among diverse platforms, addressing scalability in large-scale systems, and adapting to real-time environmental dynamics. To address these challenges, the paper proposes future research directions, such as leveraging advanced technologies like quantum computing for solving complex optimization problems, employing tiny machine learning (TinyML) to enable resource-efficient intelligence on low-power devices, and predictive task offloading to enhance proactive resource management. By presenting a detailed analysis of existing techniques and identifying critical research opportunities, this paper seeks to guide researchers and practitioners in developing more efficient, secure, and adaptive AVEC systems. The insights from this study contribute to advancing the design and deployment of resilient intelligent transportation networks, paving the way for the next generation of vehicular connectivity. Full article

Perovskite nanocrystals (PeNCs) have attracted considerable interest as promising materials for next-generation optoelectronic devices owing to their high photoluminescence quantum yield, narrow emission linewidths, simple composition tunability, and solution processability. However, the practical applicability of these NCs is limited by their compositional, thermal, and environmental instabilities, which compromise their long-term operational performance and reliability. Compositional instability arises from ion migration and phase segregation, leading to spectral shifts and unstable emission. Thermal degradation is driven by volatile organic cations and weak surface bonding, while environmental factors such as moisture, oxygen, and ultraviolet irradiation promote defect formation and material degradation. This review describes the recent advances in improving the photoluminescent stability of PeNCs through compositional engineering (A-/B-site substitution), ligand engineering (X-/L-type modulation), and surface passivation strategies. These approaches effectively suppress degradation pathways while maintaining or improving the optical properties of PeNCs. By performing a comparative analysis of these strategies, this review provides guidelines for the rational design of stable and efficient PeNCs for light-emitting applications. Full article

Our research goal is to provide the mathematical guidance to enable any combination of “intelligent” machines, artificial intelligence (AI) and humans to be able to interact with each other in roles that form the structure of a team interdependently performing a team’s tasks. Our quantum-like model, representing one of the few, if only, mathematical models of interdependence, captures the tradeoffs in energy expenditures a team chooses as it consumes its available energy on its structure versus its performance, measured by the uncertainty (entropy) relationship generated. Here, we outline the support for our quantum-like model of uncertainty relations, our goals in this study, and our future plans: (i) Redundancy reduces interdependence. This first finding confirms the existence of interdependence in systems, both large and small. (ii) Teams with orthogonal roles perform best. This second finding is the root cause of humans, including scientists, being unable to appreciate the role of interdependence in “squeezing” states of teams. (iii) Cognitive reports may not equal behavior. The last finding allows us to tie our research together and to account for the absence of social scientists from leading the mathematical science of teams. In this article, we review the need for a mathematics for the future of team operations, the literature, the mathematics in our model of agents with full agency (viz., intelligent and interdependent), our hypothesis that freely organized teams enjoy significant advantages over command decision-making (CDM) systems, and results from the field. We close with future plans and a generalization about squeezing states to control interdependent systems. Full article

Electrical networks face operational challenges from power quality-affecting disturbances. Since disturbance signatures directly affect classifier performance, optimized feature selection becomes critical for accurate power quality assessment. The pursuit of robust feature extraction inevitably constrains the dimensionality of the discriminative feature set, but the complexity of the recognition model will be increased and the recognition speed will be reduced if the feature vector dimension is too high. Building upon the aforementioned requirements, in this paper, we propose a feature extraction framework that combines improved symplectic geometric mode decomposition, refined generalized multiscale quantum entropy, and refined generalized multiscale reverse dispersion entropy. Firstly, based on the intrinsic properties of power quality disturbance (PQD) signals, the embedding dimension of symplectic geometric mode decomposition and the adaptive mode component screening method are improved, and the PQD signal undergoes tri-band decomposition via improved symplectic geometric mode decomposition (ISGMD), yielding distinct high-frequency, medium-frequency, and low-frequency components. Secondly, utilizing the enhanced symplectic geometric mode decomposition as a foundation, the perturbation features are extracted by the combination of refined generalized multiscale quantum entropy and refined generalized multiscale reverse dispersion entropy to construct high-precision and low-dimensional feature vectors. Finally, a double-layer composite power quality disturbance model is constructed by a deep extreme learning machine algorithm to identify power quality disturbance signals. After analysis and comparison, the proposed method is found to be effective even in a strong noise environment with a single interference, and the average recognition accuracy across different noise environments is 97.3%. Under the complex conditions involving multiple types of mixed perturbations, the average recognition accuracy is maintained above 96%. Compared with the existing CNN + LSTM method, the recognition accuracy of the proposed method is improved by 3.7%. In addition, its recognition accuracy in scenarios with small data samples is significantly better than that of traditional methods, such as single CNN models and LSTM models. The experimental results show that the proposed strategy can accurately classify and identify various power quality interferences and that it is better than traditional methods in terms of classification accuracy and robustness. The experimental results of the simulation and measured data show that the combined feature extraction methodology reliably extracts discriminative feature vectors from PQD. The double-layer combined classification model can further enhance the model’s recognition capabilities. This method has high accuracy and certain noise resistance. In the 30 dB white noise environment, the average classification accuracy of the model is 99.10% for the simulation database containing 63 PQD types. Meanwhile, for the test data based on a hardware platform, the average accuracy is 99.03%, and the approach’s dependability is further evidenced by rigorous validation experiments. Full article

To address the impact of distributed energy resource volatility on distribution network fault restoration, this paper proposes a strategy that incorporates net load forecasting. A Bayesian-optimized long short-term memory neural network is used to accurately predict the net load within fault-affected areas, achieving an R² of 0.9569 and an RMSE of 12.15 kW. Based on the forecasting results, a fast restoration optimization model is established, with objectives to maximize critical load recovery, minimize switching operations, and reduce network losses. The model is solved using a genetic algorithm enhanced with quantum particle swarm optimization (GA-QPSO), a hybrid metaheuristic known for its superior global exploration and local refinement capabilities. GA-QPSO has been successfully applied in various power system optimization problems, including service restoration, network reconfiguration, and distributed generation planning, owing to its effectiveness in navigating large, complex solution spaces. Simulation results on the IEEE 33-bus system show that the proposed method reduces network losses by 33.2%, extends the power supply duration from 60 to 120 min, and improves load recovery from 72.7% to 75.8%, demonstrating enhanced accuracy and efficiency of the restoration process. Full article