Search Results (711)

In this work, we introduce a novel method to extract correlations in diagonal quantum states in multi-particle quantum systems, addressing a significant limitation of traditional approaches that require prior knowledge of the density matrices of quantum states. Instead of relying on classical information processing, our method is based on weak couplings and ancillary systems, eliminating the need for classical communication, optimization, and complex calculations. The concept of mutually unbiased bases is intrinsically linked to symmetry, as it entails the uniform distribution of quantum states across distinct bases. Within the framework of our theoretical model, mutually unbiased bases are employed to facilitate weak measurements and to function as the post-selected states. To quantify the correlations in the initial state, we employ the trace distance between the initial state and the product of its marginal states, and illustrate the feasibility and effectiveness of our approach. We generalize the approach to accommodate high-dimensional multi-particle systems for potential applications in quantum information processing and quantum networks. Full article

In this paper, we investigate the memory effects introduced by the time-fractional Schrödinger equation proposed by Naber on quantum entanglement and quantum dense coding under amplitude damping noise. Two formulations are analyzed: one with fractional operations applied to the imaginary unit and one without. Numerical results show that the formulation without fractional operations on the imaginary unit may be more suitable for describing non-Markovian (power-law) behavior in dissipative environments. This finding provides a more physically meaningful interpretation of the memory effects in time-fractional quantum dynamics and indirectly addresses fundamental concerns regarding the violation of unitarity and probability conservation in such frameworks. Our work offers a new perspective for the application of fractional quantum mechanics to realistic open quantum systems and shows promise in supporting the theoretical modeling of decoherence and information degradation. Full article

In this position paper, we argue that the conventional understanding of ‘information’ (as generally conceived in science, in a digital fashion) is overly simplistic and not consistently applicable to living systems, which are open systems that cannot be reduced to any kind of ‘portion’ (building block) ascribed to the category of quantity. Instead, it is a matter of relationships and qualities in an indivisible analogical (and ontological) relationship between any presumed ‘software’ and ‘hardware’ (information/matter, psyche/soma). Furthermore, in biological systems, contrary to Shannon’s definition, which is well-suited to telecommunications and informatics, any kind of ‘information’ is the opposite of internal entropy, as it depends directly on order: it is associated with distinction and differentiation, rather than flattening and homogenisation. Moreover, the high degree of structural compartmentalisation of living matter prevents its energetics from being thermodynamically described by using a macroscopic, bulk state function. This requires the Second Principle of Thermodynamics to be redefined in order to make it applicable to living systems. For these reasons, any static, bit-related concept of ‘information’ is inadequate, as it fails to consider the system’s evolution, it being, in essence, the organized coupling to its own environment. From the perspective of quantum field theory (QFT), where many vacuum levels, symmetry breaking, dissipation, coherence and phase transitions can be described, a consistent picture emerges that portrays any living system as a relational process that exists as a flux of context-dependent meanings. This epistemological shift is also associated with a transition away from the ‘particle view’ (first quantisation) characteristic of quantum mechanics (QM) towards the ‘field view’ possible only in QFT (second quantisation). This crucial transition must take place in life sciences, particularly regarding the methodological approaches. Foremost because biological systems cannot be conceived as ‘objects’, but rather as non-confinable processes and relationships. Full article

Rovelli’s relational interpretation of quantum mechanics tells us that the description of a system in the formalism of quantum mechanics is not an absolute but is relative to the observer itself. The interpretation goes further and proposes a set of axioms. In standard non-relational language, one of them states that an observer can only retrieve a finite amount information from a system by means of measurement. Our contribution starts with the observation that quantum mechanics, i.e., quantum field theory (QFT) in dimension 1, radically differs from QFT in higher dimensions. In higher dimensions, boundary data (or initial data) cannot be characterized by finitely many measurements. This calls for a notion of measuring scale, which we provide. At a given measuring scale, the observer has partial information about the system. Our notion of measuring scale generalizes the one implicitly used in Wilsonian QFT. At each measuring scale, there are effective theories, which may be corrected, and if the theory turns out to be renormalizable, the mentioned corrections converge to determine a completely corrected (or renormalized) theory at the given measuring scale. The notion of a measuring scale is the cornerstone of Wilsonian QFT; this notion tells us that we are not describing a system from an absolute perspective. An effective theory at that scale describes the system with respect to the observer, which may retrieve information from the system by means of measurement in a specific way determined by our notion of measuring scale. We claim that a relational interpretation of quantum physics for spacetimes of dimensions greater than 1 is Wilsonian. Full article

The chiral nonlinear Schrödinger equation (CNLSE) serves as a simplified model for characterizing edge states in the fractional quantum Hall effect. In this paper, we leverage the generalization and parameter inversion capabilities of physics-informed neural networks (PINNs) to investigate both forward and inverse problems of 1D and 2D CNLSEs. Specifically, a hybrid optimization strategy incorporating exponential learning rate decay is proposed to reconstruct data-driven solutions, including bright soliton for the 1D case and bright, dark soliton as well as periodic solutions for the 2D case. Moreover, we conduct a comprehensive discussion on varying parameter configurations derived from the equations and their corresponding solutions to evaluate the adaptability of the PINNs framework. The effects of residual points, network architectures, and weight settings are additionally examined. For the inverse problems, the coefficients of 1D and 2D CNLSEs are successfully identified using soliton solution data, and several factors that can impact the robustness of the proposed model, such as noise interference, time range, and observation moment are explored as well. Numerical experiments highlight the remarkable efficacy of PINNs in solution reconstruction and coefficient identification while revealing that observational noise exerts a more pronounced influence on accuracy compared to boundary perturbations. Our research offers new insights into simulating dynamics and discovering parameters of nonlinear chiral systems with deep learning. Full article

In the era of rapid industrial automation advancements, the complexity of intelligent manufacturing equipment has been steadily escalated. Stringent demands for high-efficiency and high-precision diagnosis are increasingly being unmet by conventional fault diagnosis methods. To address these challenges, a novel fault diagnosis approach grounded in quantum Q-learning is presented in this paper. The distinct advantages of quantum computing are innovatively integrated with the decision-making framework of Q-learning through this method. By harnessing the multi-information-carrying capacities of qubits, vast amounts of multi-source heterogeneous data generated during equipment operation can be efficiently processed. Latent fault features are thereby rapidly uncovered, significantly reducing the time required for fault-feature extraction. Furthermore, optimal decisions can be dynamically formulated by Q-learning within evolving production environments, leveraging precise analysis outcomes from quantum computing. Real-time equipment status is continuously monitored to accurately identify fault types, pinpoint locations, and promptly generate targeted maintenance strategies. Fault-diagnosis tests conducted on typical industrial intelligent manufacturing equipment demonstrate that the quantum Q-learning method outperforms traditional approaches in terms of diagnosis accuracy, efficiency, and adaptability to complex fault patterns. This breakthrough opens up new frontiers for fault diagnosis in intelligent manufacturing systems. Full article

Physics-informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs’ development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence. The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid numerical-deep learning integration (5× speedup via domain decomposition) and second, constructing bidirectional theory-application mappings where convergence analysis (operator approximation theory) and generalization guarantees (Bayesian-physical hybrid frameworks) directly inform engineering implementations, as validated by $72\%$ cost reduction compared to FEM in high-dimensional spaces ($p<0.01,n=15$ benchmarks). Third, pioneering cross-domain knowledge transfer through application-specific architectures: TFE-PINN for turbulent flows ($5.12\pm 0.87\%$ error in NASA hypersonic tests), ReconPINN for medical imaging ($▵\mathrm{SSIM}=+0.18\pm 0.04$ on multi-institutional MRI), and SeisPINN for seismic systems ($0.52\pm 0.18$ km localization accuracy). We further present a technological roadmap highlighting three critical directions for PINN 2.0: neuro-symbolic, federated physics learning, and quantum-accelerated optimization. This work provides methodological guidelines and theoretical foundations for next-generation scientific machine learning systems. Full article

The multi-factor authentication (MFA) protocol requires users to provide a combination of a password, a smart card and biometric data as verification factors to gain access to the services they need. In a single-server MFA system, users accessing multiple distinct servers must register separately for each server, manage multiple smart cards, and remember numerous passwords. In contrast, an MFA system designed for multi-server architecture allows users to register once at a registration center (RC) and then access all associated servers with a single smart card and one password. MFA with an offline RC addresses the computational bottleneck and single-point failure issues associated with the RC. In this paper, we propose a post-quantum secure MFA protocol for a multi-server architecture with an offline RC. Our MFA protocol utilizes the post-quantum secure Kyber key encapsulation mechanism and an information-theoretically secure fuzzy extractor as its building blocks. We formally prove the post-quantum semantic security of our MFA protocol under the real or random (ROR) model in the random oracle paradigm. Compared to related protocols, our protocol achieves higher efficiency and maintains reasonable communication overhead. 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

Quantum memory is essential for the prolonged storage and retrieval of quantum information. Nevertheless, no current studies have focused on the creation of effective quantum memory for continuous variables while accounting for the decoherence rate. This work presents an effective continuous-variable quantum key distribution method with parameter optimization utilizing the Elitist Elk Herd Random Immigrants Optimizer (2E-HRIO) technique. At the outset of transmission, the quantum device undergoes initialization and authentication via Compressed Hash-based Message Authentication Code with Encoded Post-Quantum Hash (CHMAC-EPQH). The settings are subsequently optimized from the authenticated device via 2E-HRIO, which mitigates the effects of decoherence by adaptively tuning system parameters. Subsequently, quantum bits are produced from the verified device, and pilot insertion is executed within the quantum bits. The pilot-inserted signal is thereafter subjected to pulse shaping using a Gaussian filter. The pulse-shaped signal undergoes modulation. Authenticated post-modulation, the prediction of link failure is conducted through an authenticated channel using Radial Density-Based Spatial Clustering of Applications with Noise. Subsequently, transmission occurs via a non-failure connection. The receiver performs channel equalization on the received signal with Recursive Regularized Least Mean Squares. Subsequently, a dataset for side-channel attack authentication is gathered and preprocessed, followed by feature extraction and classification using Adaptive Depthwise Separable Convolutional Neural Networks (ADS-CNNs), which enhances security against side-channel attacks. The quantum state is evaluated based on the signal received, and raw data are collected. Thereafter, a connection is established between the transmitter and receiver. Both the transmitter and receiver perform the scanning process. Thereafter, the calculation and correction of the error rate are performed based on the sifting results. Ultimately, privacy amplification and key authentication are performed using the repaired key via B-CHMAC-EPQH. The proposed system demonstrated improved resistance to decoherence and side-channel attacks, while achieving a reconciliation efficiency above 90% and increased key generation rate. 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

This paper starts with an updated review and analyzes recent developments in quantum-inspired algorithms for cybersecurity, with specific attention to possible perspectives of optimization. The enhancement of classical computing capabilities with quantum principles is transforming fields such as machine learning, optimization, and cybersecurity. Evolutionary algorithms are one example where progress has already been made using quantum techniques through increased efficiency, generalization, and problem-solving techniques exploited by quantum principles. Quantum-inspired evolutionary algorithms (QIEAs) and quantum kernel methods are prime examples of such approaches. Quantum techniques are also used in the field of cybersecurity: QML-based identification systems for intrusion detection strengthen threat detection and encoding through quantum techniques with advanced cryptographic security, while quantum-secure hashing (QSHA) offers sophisticated means of protecting sensitive information. More specifically, QGANs are known for their integration into adversarial generative networks that increase efficiency by replacing classical models in adversarial defense through the generation of synthetic attack models. In this work, a set of benchmarks is provided for comparison with classical and other quantum-inspired technologies. The results demonstrate that these methods far outperform others in terms of computational efficiency and satisfactory scalability. Although fully functional models are still awaited, quantum computing benefits greatly from quantum-inspired technologies, as the latter enable the development of frameworks that bring us closer to the quantum era. Consequently, the work takes the form of an updated systematic review enriched with optimized perspectives. Full article

Aeroponics efficiently conserves water and fertilizer but faces energy sustainability challenges in maintaining high productivity and quality. This study aimed to identify critical growth phases of lettuce affected by management modes and assess resource/energy efficiency (cost per unit yield) to inform the development of sustainability strategies for lettuce production in a lettuce-dominant aeroponics system integrated with radish. Three management modes were tested: M1 (constant nutrient solution concentrations), M2 (variable nutrient solution concentrations), and M3 (combined variable nutrient solution concentrations and preharvest short-duration continuous light for 48 h). Plant parameters were dynamically measured in a 30-day cultivation cycle. The results showed that the intercropped lettuce exhibited peak growth at 15–25 days after transplanting, and nutrient solution adjustment enhanced the shoot weight and quality, with synergistic quality improvements under M3. However, preharvest lighting reduced the net photosynthetic rate via stomatal closure and lowered the effective quantum yield of photosystem II, preventing biomass increase. The preharvest short-duration continuous light elevated the soluble protein, ascorbic acid, and soluble sugar contents. For yield-focused systems, M2 alone achieved comparable shoot weight to M3 with higher energy efficiency. However, when simultaneously considering lettuce quality enhancement and the yield boost of radish in the intercropping system, M3 demonstrated potential for greater marginal benefits. 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