Search Results (319)

Quantum machine learning (QML) has emerged as a promising approach for enhancing image classification by exploiting quantum computational principles such as superposition and entanglement. However, practical applications on complex datasets like CIFAR-100 remain limited due to the low expressivity of shallow circuits and challenges in circuit optimization. In this study, we propose HQCNN–REGA—a novel hybrid quantum–classical convolutional neural network architecture that integrates data re-uploading and genetic algorithm optimization for improved performance. The data re-uploading mechanism allows classical inputs to be encoded multiple times into quantum states, enhancing the model’s capacity to learn complex visual features. In parallel, a genetic algorithm is employed to evolve the quantum circuit architecture by optimizing gate sequences, entanglement patterns, and layer configurations. This combination enables automatic discovery of efficient parameterized quantum circuits without manual tuning. Experiments on the MNIST and CIFAR-100 datasets demonstrate state-of-the-art performance for quantum models, with HQCNN–REGA outperforming existing quantum neural networks and approaching the accuracy of advanced classical architectures. In particular, we compare our model with classical convolutional baselines such as ResNet-18 to validate its effectiveness in real-world image classification tasks. Our results demonstrate the feasibility of scalable, high-performing quantum–classical systems and offer a viable path toward practical deployment of QML in computer vision applications, especially on noisy intermediate-scale quantum (NISQ) hardware. Full article

Radio Frequency Identification (RFID) technology is widely applied in various scenarios, including logistics tracking, supply chain management, and target monitoring. In these contexts, the malicious cloning of legitimate tag information can lead to sensitive data leakage and disrupt the normal acquisition of tag information by readers, thereby threatening personal privacy and corporate security and incurring significant economic losses. Although some efforts have been made to detect cloning attacks, the presence of missing tags in RFID systems can obscure cloned ones, resulting in a significant reduction in identification efficiency and accuracy. To address these problems, we propose the block-based cloned tag identification (BCTI) protocol for identifying cloning attacks in the presence of missing tags. First, we introduce a block indicator to sort all tags systematically and design a block mechanism that enables tags to respond repeatedly within a block with minimal time overhead. Then, we design a superposition strategy to further reduce the number of verification times, thereby decreasing the execution overhead. Through an in-depth analysis of potential tag response patterns, we develop a precise method to identify cloning attacks and mitigate interference from missing tags in probabilistic cloning attack scenarios. Moreover, we perform parameter optimization of the BCTI protocol and validate its performance across diverse operational scenarios. Extensive simulation results demonstrate that the BCTI protocol meets the required identification reliability threshold and achieves an average improvement of $24.01\%$ in identification efficiency compared to state-of-the-art solutions. Full article

We investigate the dynamics of vector solitons in a two-component Bose–Einstein condensates governed by the system of Gross–Pitaevskii equations. Using a gauge-transformation approach, we construct a four-soliton solution and analyze their interactions, including superposition states, fission, and shape-preserving collisions. We explore the ability of time-dependent parameters, such as the intra- and intercomponent interaction coefficients and trapping potential, to control the soliton properties. In particular, we demonstrate controlled four-soliton fission, highlighting its potential applications to quantum data processing and coherent matter-wave transport. The results suggest experimental realization in BEC systems and provide insights into nonlinear wave interactions in multicomponent quantum fluids. Full article

Reliable transmission of high-quality video over wireless channels is challenged by fading and noise, which degrade visual quality and disrupt temporal continuity. To address these issues, this paper proposes a quantum communication framework that integrates quantum superposition with multi-input multi-output (MIMO) spatial diversity techniques to enhance robustness and efficiency in dynamic video transmission. The proposed method converts compressed videos into classical bitstreams, which are then channel-encoded and quantum-encoded into qubit superposition states. These states are transmitted over a $2\times 2$ MIMO system employing varied diversity schemes to mitigate the effects of multipath fading and noise. At the receiver, a quantum decoder reconstructs the classical information, followed by channel decoding to retrieve the video data, and the source decoder reconstructs the final video. Simulation results demonstrate that the quantum MIMO system significantly outperforms equivalent-bandwidth classical MIMO frameworks across diverse signal-to-noise ratio (SNR) conditions, achieving a peak signal-to-noise ratio (PSNR) up to 39.12 dB, structural similarity index (SSIM) up to 0.9471, and video multi-method assessment fusion (VMAF) up to 92.47, with improved error resilience across various group of picture (GOP) formats, highlighting the potential of quantum MIMO communication for enhancing the reliability and quality of video delivery in next-generation wireless networks. Full article

Aspect-Based Sentiment Analysis (ABSA) has gained significant popularity in recent years, which emphasizes the aspect-level sentiment representation of sentences. Current methods for ABSA often use pre-trained models and graph convolution to represent word dependencies. However, they struggle with long-range dependency issues in lengthy texts, resulting in averaging and loss of contextual semantic information. In this paper, we explore how richer semantic relationships can be encoded more efficiently. Inspired by quantum theory, we construct superposition states from text sequences and utilize them with quantum measurements to explicitly capture complex semantic relationships within word sequences. Specifically, we propose an attention-based semantic dependency fusion method for ABSA, which employs a quantum embedding module to create a superposition state of real-valued word sequence features in a complex-valued Hilbert space. This approach yields a word sequence density matrix representation that enhances the handling of long-range dependencies. Furthermore, we introduce a quantum cross-attention mechanism to integrate sequence features with dependency relationships between specific word pairs, aiming to capture the associations between particular aspects and comments more comprehensively. Our experiments on the SemEval-2014 and Twitter datasets demonstrate the effectiveness of the quantum-inspired attention-based semantic dependency fusion model for the ABSA task. Full article

In this work, we report the formation of multiple mode-locking states in an Erbium/Ytterbium co-doped fiber laser, such as domain-wall (DW) dark pulses, high-order dark harmonic pulses, dissipative soliton resonance (DSR) pulses, and dual-wavelength h-shaped pulses. By increasing the pump power and adjusting the quarter-wave retarder (QWR) plates, we experimentally achieve 310th-order harmonic dark pulses. DSR pulses emerge at a pump power of 1.01 W and remain stable up to 9.07 W, reaching a maximum pulse width of 676 ns and a pulse energy of 1.608 µJ, while Dual-wavelength h-shaped pulses have a threshold of 1.42 W and maintain stability up to 9.07 W. Using a monochromator, we confirm that these h-shaped pulses result from the superposition of a soliton-like pulse and a DSR-like pulse, emitting at different wavelengths but locked in time. The fundamental repetition rate for dark pulsing, DSR, and h-shaped pulses is 321.34 kHz. This study provides new insights into complex pulse dynamics in fiber lasers and demonstrates the versatile emission regimes achievable through precise pump and polarization control. Full article

This paper introduces a novel theoretical framework that reformulates optimal control as a quantum search problem using Grover’s algorithm, leveraging its quadratic speedup. Specifically, the method encodes all possible control inputs into a quantum superposition state and uses a reference value interpreted as a candidate minimum to evaluate which inputs yield a lower control cost. To guide the search, we integrate a quantum comparator circuit to identify the inputs below this reference, and quantum counting to estimate their number. The reference is iteratively updated using a sigmoid-based rule until only one input satisfies the condition, thereby ensuring convergence to the global minimum within the discretized control space. Although full quantum implementation is currently infeasible due to oracle complexity and hardware limitations, we simulate the process using a classical controller as a pseudo-oracle to illustrate the algorithmic structure. This work does not aim to demonstrate performance gains but rather to establish a foundational method for embedding control synthesis within Grover-based quantum circuits. The framework paves the way for scalable quantum control systems once hardware resources permit full realization. Full article

This study addresses the coupled influence of the threshold pressure gradient and stress sensitivity during the seepage process in low-permeability reservoirs. By integrating Laplace transform, perturbation transform, the image principle, and the superposition principle, a non-steady-state seepage model for segmented-fractured horizontal wells considering both effects is established for the first time. The analytical solution of the point source function including the threshold pressure gradient ($\lambda$) and stress sensitivity effect (permeability modulus $\alpha$) is innovatively derived and extended to closed-boundary reservoirs. The model accuracy is verified by CMG numerical simulation (with an error of only 1.02%). Based on this, the seepage process is divided into four stages: I linear flow (pressure derivative slope of 0.5), II fracture radial flow (slope of 0), III dual radial flow (slope of 0.36), and IV pseudo-radial flow (slope of 0). Sensitivity analysis indicates the following: (1) The threshold pressure gradient significantly increases the seepage resistance in the late stage (the pressure curve shows a significant upward curvature when $\lambda$ = 0.1 MPa/m); (2) Stress sensitivity dominates the energy dissipation in the middle and late stages (a closed-boundary-like feature is presented when $\alpha$ > 0.1 MPa⁻¹); (3) The half-length of fractures dominates the early flow (a 100 m fracture reduces the pressure drop by 40% compared to a 20 m fracture). This model resolves the accuracy deficiency of traditional single-effect models and provides theoretical support for the development effect evaluation and well test interpretation of fractured horizontal wells in low-permeability reservoirs. Full article

We review quantum architectures for engineering the N00N state, a bipartite maximally entangled state essential in quantum metrology. These schemes transform the initial state $|N⟩\otimes |0⟩$ into the N00N state, ${\displaystyle \frac{1}{\sqrt{2}}}\left(\right|N⟩\otimes |0⟩+|0⟩\otimes |N⟩),$ where $|N⟩$ and $|0⟩$ are Fock states with N and 0 excitations, respectively. We demonstrate that this state can be generated through superpositions of quantum light modes, hybrid light–matter interactions, or spin ensembles. Our approach also enables the creation of mesoscopic and macroscopic entangled states, including entangled coherent and squeezed states. Furthermore, we show that a broad class of maximally entangled states can be realized within this framework. Extensions to multi-mode state engineering are also explored. Full article

Particle-reinforced Al-based matrix composites have great potential for application in aerospace, automotive manufacturing, and defense due to their high strength, hardness, and excellent wear and corrosion resistance. However, the presence of particles increases the processing difficulty, making it a typical difficult-to-machine material. In recent years, ultrasonic vibration-assisted machining has been quite popular in manufacturing this kind of material. This paper reviews the research advancements in ultrasonic vibration-assisted machining of particle-reinforced Al-based matrix composites, providing a comprehensive analysis of the effects of introducing an ultrasonic energy field on tool wear, chip morphology, cutting force, cutting temperature, and surface integrity. Ultrasonic vibration periodically alters the contact state between the tool and the workpiece, effectively reducing the tool wear rate and extending the tool life. Meanwhile, ultrasonic vibration facilitates the fracture and ejection of chips, enhancing chip morphology and reducing energy consumption during the cutting process. Additionally, ultrasonic vibration significantly decreases cutting force and cutting temperature, contributing to the stability of the cutting process and improving processing efficiency. Regarding surface integrity, ultrasonic vibration-assisted machining refines the machined surface’s microstructure, reducing surface defects and residual stress, thereby significantly enhancing the machining quality. In the future, we will conduct in-depth research on the effects of ultrasonic energy on material properties in terms of softening effect, thermal effect, and stress superposition, further revealing the mechanism of ultrasonic vibration-assisted processing of particle-reinforced aluminum-based composite materials. Full article

To address the high-frequency resonance issues in renewable energy systems integrated via MMC-HVDC transmission under weak grid conditions, this paper establishes a wide-frequency sequence impedance model for renewable energy grid-side converters and MMC-HVDC sending-end converters. The impedance characteristics of the MMC-HVDC transmission system under two distinct control modes are compared, and the constant power control mode is selected for detailed analysis to better evaluate the effectiveness of suppression strategies. Based on this framework, the superposition theorem is employed to analyze the interaction mechanism between the impedance characteristics of the MMC-HVDC sending-end converter and the renewable energy grid-connected system. Since the aim of this study is to propose suppression strategies for the MMC-HVDC transmission system, a sensitivity analysis of its control parameters is conducted. The results identify the current loop and voltage feedforward control as the dominant factors influencing high-frequency oscillations. Accordingly, a coordinated control strategy combining current loop regulation and voltage feedforward compensation is proposed. An electromagnetic transient simulation model is developed in MATLAB/Simulink. The simulations demonstrate that the proposed strategy effectively suppresses oscillations in the MMC-HVDC system across high-frequency ranges. Furthermore, it avoids negative damping characteristics within a broad frequency band, significantly enhancing the steady-state performance of renewable energy systems integrated via MMC-HVDC transmission. Full article

In this paper, the evolution of the beam from the double Hermite–Gaussian beam superposition state to the double Laguerre–Gaussian beam superposition state is realized based on the astigmatism conversion. Firstly, the tunable output of the double Hermite–Gaussian mode superposition state is realized by adjusting the off-axis pumping distance of the crystal. On this basis, an astigmatic mode converter is added to the back end of the resonant cavity output mirror. By utilizing it, the evolution from the double Hermite–Gaussian mode superposition state to the specific double Laguerre–Gaussian mode superposition state is realized. The evolution process of the double mode superposition state based on the astigmatic mode is analyzed theoretically. The light field change of the evolution process is demonstrated experimentally. Full article

Ultrasonic vibration (UV) has been employed in various plastic forming processes due to its special effect known as acoustoplasticity. Mostly, UV is applied along the longitudinal direction in experimental investigations. However, very few studies have focused on normal UV-assisted uniaxial tension, which is more similar to the loading state of sheet metal in actual forming processes. Herein, normal UV-assisted tension tests on a TA1 thin sheet are performed to study its mechanical properties and microstructure evolution. The macro-mechanical behavior is demonstrated by stress–strain curves under different ultrasonic amplitudes and strain rates. Fracture morphology and microstructure evolution are characterized by scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) to reveal the UV softening mechanism at the micro level. The results show that the stress reduction induced by UV reaches 20% when the ultrasonic amplitude is 13.9 μm. Fracture mode changes from ductile fracture to brittle fracture with increasing amplitude. Microstructure examinations show that low-angle grain boundary (LAGB) fraction, kernel average misorientation (KAM), and geometrically necessary dislocation (GND) density in the samples experiencing normal UV-assisted tension are all decreased, leading to a reduction in deformation resistance. The inverse pole figures (IPFs) further reveal that the plastic deformation mechanism of the TA1 thin sheet is diversified with the superposition of normal UV. Full article

The cornerstones of the Cellular Automaton Interpretation of Quantum Mechanics are its underlying ontological states that evolve by permutations. They do not create would-be quantum mechanical superposition states. We review this with a classical automaton consisting of an Ising spin chain which is then related to the Weyl equation in the continuum limit. Based on this and generalizing, we construct a new “Necklace of Necklaces” automaton with a torus-like topology that lends itself to represent the Dirac equation in 1 + 1 dimensions. Special attention has to be paid to its mass term, which necessitates this enlarged structure and a particular scattering operator contributing to the step-wise updates of the automaton. As discussed earlier, such deterministic models of discrete spins or bits unavoidably become quantum mechanical, when only slightly deformed. Full article

Latent variables play a crucial role in psychometric research, yet traditional models often struggle to address context-dependent effects, ambivalent states, and non-commutative measurement processes. This study proposes a quantum-inspired framework for latent variable modeling that employs Hilbert space representations, allowing questionnaire items to be treated as pure or mixed quantum states. By integrating concepts such as superposition, interference, and non-commutative probabilities, the framework captures cognitive and behavioral phenomena that extend beyond the capabilities of classical methods. To illustrate its potential, we introduce quantum-specific metrics—fidelity, overlap, and von Neumann entropy—as complements to correlation-based measures. We also outline a machine-learning pipeline using complex and real-valued neural networks to handle amplitude and phase information. Results highlight the capacity of quantum-inspired models to reveal order effects, ambivalent responses, and multimodal distributions that remain elusive in standard psychometric approaches. This framework broadens the multivariate analysis theoretical and methodological toolkit, offering a dynamic and context-sensitive perspective on latent constructs while inviting further empirical validation in diverse research settings. Full article