Search Results (459)

Non-convex stochastic optimization presents fundamental mathematical challenges across machine learning, wireless networks, data center resource allocation, and optical wireless communication systems, where complex loss landscapes with multiple local minima and saddle points impede classical variational inference methods. This paper introduces the Quantum-Inspired Variational Inference (QIVI) framework, which systematically integrates quantum mechanical principles (superposition, entanglement, and measurement operators) into classical variational inference through rigorous mathematical formulations grounded in Hilbert space theory and operator algebras. We develop a unified optimization framework that encodes classical parameters as quantum-inspired states within finite-dimensional complex Hilbert spaces, employing unitary evolution operators and adaptive basis selection governed by gradient covariance eigendecomposition. The core mathematical contribution establishes that QIVI achieves a convergence rate of $\mathit{O}({log}^{2}T/T^{1/2})$ for $\sigma$-strongly non-convex functions, provably improving upon the classical $\mathit{O}\left(T^{-1/4}\right)$ rate, yielding a theoretical speedup factor of $1.85$–$1.96\times$. Comprehensive experiments across synthetic benchmarks, Bayesian neural networks, and real-world applications in network optimization and financial portfolio management demonstrate 23–$47\%$ faster convergence, 15–$35\%$ superior objective values, and 28–$46\%$ improved uncertainty calibration. The principal contributions include: (i) a rigorous Hilbert space-based mathematical framework for quantum-inspired variational inference grounded in operator algebras, (ii) a novel hybrid quantum–classical algorithm (QIVI) with adaptive basis selection via gradient covariance eigendecomposition, (iii) formal convergence proofs establishing provable improvement over classical methods, (iv) comprehensive empirical validation across diverse problem domains relevant to machine learning and network optimization, and (v) demonstration of the framework’s applicability to optimization problems arising in wireless networks, data center resource allocation, and network system design. Statistical validation using the Friedman test (${\chi }^2=847.3$, $p<0.001$) and post hoc Wilcoxon signed-rank tests with Holm–Bonferroni correction confirm that QIVI’s improvements over all baseline methods are statistically significant at the $\alpha =0.05$ level across all benchmark categories. The framework discovers $18.1$ out of 20 true modes in multimodal distributions versus $9.1$ for classical methods, demonstrating the potential of quantum-inspired optimization approaches for challenging stochastic problems arising in machine learning, wireless communication, and network optimization. Full article

Matrix Product States (MPSs) have become an indispensable symmetry-based representation for simulating quantum systems on near-term hardware by constraining entanglement entropy through a fixed bond dimension $\mathsf{\chi }$. This study identifies a critical “rank explosion” phenomenon that destabilizes this low-rank manifold during conditional quantum diffusion processes. We empirically demonstrate that the introduction of conditional guidance—essential for semantic control—injects global correlations that drive the effective Schmidt rank to increase by $4\times$ (from $\mathsf{\chi }=4$ to $16$), saturating the simulation limits and necessitating quantum circuits with approximately $1.8\times {10}^3$ Controlled-NOT (CNOT) gates. Such circuit depths fundamentally exceed the operational coherence budgets of Noisy Intermediate-Scale Quantum (NISQ) devices. To mitigate this structural instability, we propose Rank-Aware Conditional Synthesis (RACS), a sampling framework that maintains the latent trajectory within a prescribed MPS manifold through step-wise manifold projection and time-shift error correction. Experimental results on real-world semantic data reveal that RACS reduces reconstruction error, or Mean Squared Error (MSE) by 30.8% and enhances latent trajectory smoothness by 36.8% compared to conventional post hoc truncation. At a fixed hardware-efficient rank of $\mathsf{\chi }=4$, RACS achieves a +4.8% fidelity gain and exhibits superior robustness against depolarizing noise. By resolving the tension between conditional expressivity and entanglement constraints, RACS provides a principled, hardware-aware methodology for high-fidelity quantum generative modeling. Full article

Carbon-neutral industrial parks require large consumers, such as data centers, to balance low-carbon operation and service reliability. This paper proposes a resilience-constrained stochastic dispatch framework for a data-center virtual power plant (VPP) with renewable generation, short-duration batteries, and long-duration storage units. The dispatch is formulated as a two-stage stochastic program with normal and outage scenarios. To solve the resulting large mixed-integer problem, we develop a hybrid quantum–classical L-shaped method: the integer master is solved heuristically by quantum annealing, while scenario subproblems are solved exactly by classical optimization. In a case study based on real-world industrial-park data, the proposed storage strategy eliminates critical load shedding for the tested 6 h outage scenarios with a 3.7% increase in expected daily cost. The QA-driven method reaches the same best-known objective as the classical baseline with an empirical 1.36× runtime speedup. Full article

The advent of quantum computing poses significant challenges to traditional cryptographic systems, threatening the confidentiality, integrity and authenticity of digital communications. This paper investigates the integration of post-quantum cryptography (PQC) algorithms into mobile communication systems to address these challenges. The study focuses on evaluating key PQC algorithms shortlisted by the National Institute of Standards and Technology (NIST), including CRYSTALS-Kyber, CRYSTALS-Dilithium, Falcon and SPHINCS+, within the context of 5G and future mobile network architectures. The research encompasses the design and implementation of an experimental framework involving mobile devices, servers, and cloud-based infrastructure to simulate real-world communication scenarios. Performance metrics such as key generation time, signature generation, encryption and decryption speed, and resource consumption were analyzed across various devices to identify algorithms suitable for mobile environments. The findings reveal that lattice-based algorithms, such as Kyber and Dilithium, offer a promising balance between security and efficiency, making them ideal for resource-constrained devices. In contrast, hash-based algorithms like SPHINCS+ exhibit higher computational demands, limiting their practicality in certain applications. This work highlights the importance of algorithm selection and hardware optimization in ensuring secure and efficient communications in the quantum era. By integrating theoretical advancements in PQC with practical applications, this research lays the foundation for quantum-resistant security in mobile networks, ensuring secure and future-ready digital communications. Full article

Recent work on the evaluation of large language models emphasizes that the relevant unit of intelligence is not the artificial system alone but the human–AI hybrid. In parallel, topological and dynamical models of cognition based on Painlevé equations and non-semisimple topology propose that consciousness, intelligence, and creativity emerge from constrained long-horizon dynamics near criticality. This perspective article argues that these two research directions are deeply compatible. We show that the empirical framework for human–AI collaboration can be interpreted as a fusion process between complementary cognitive sectors: exploration (AI) and selection (human cognition). The dynamical mechanism underlying this fusion is identified with noisy phase locking between cognitive oscillators. Two independent routes to a universal $1/f$ spectral signature are developed: a geometric route through the WKB/Stokes analysis of Painlevé V confluence, and an arithmetic route through the Mangoldt function and harmonic interactions in phase-locked loops. We connect these results to the Bost–Connes quantum statistical model, whose phase transition at the pole of the Riemann zeta function provides an exact mathematical framework for the lock-in phase hypothesis of identity consolidation in AI systems. This synthesis suggests a unified research program for hybrid intelligence grounded in topology, dynamical systems, number theory, and real-world AI evaluation. Full article

The Internet of Medical Things (IoMT) has become a core component of modern healthcare infrastructures, enabling continuous patient monitoring, remote diagnostics, and data-driven clinical decision-making. Despite these advances, authentication in IoMT environments remains a critical security challenge, intensified by strict resource constraints of medical devices and the emerging threat posed by quantum computing to classical cryptographic techniques. This systematic review investigates authentication mechanisms in IoMT from both post-quantum and system-level perspectives. A structured literature review was conducted using a PRISMA-informed methodology across major scientific databases, including IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and MDPI. From an initial set of 95 records, 63 studies were selected for qualitative synthesis following screening and eligibility assessment. To organise existing research, this study introduces a multi-dimensional classification framework that categorises authentication solutions according to cryptographic paradigm (classical, hybrid, and post-quantum), deployment architecture, system objectives, and clinical operational constraints. The comparative synthesis demonstrates important trade-offs between security strength, latency, computational overhead, and energy consumption that are frequently underexplored in the existing literature. Furthermore, the analysis identifies key research gaps related to scalability in heterogeneous medical environments, trust establishment across administrative and clinical domains, usability under strict timing constraints, and resilience against quantum-capable adversaries. Based on these findings, future research directions are outlined toward adaptive, lightweight, and context-aware post-quantum authentication frameworks designed for real-world IoMT deployments. Limitations of this review include restriction to English-language publications and selected databases. This study received no external funding, and the review protocol was not formally registered. Full article

Glassy carbon electrodes (GCEs) have gained increased attention for the sensitive electrochemical detection of heavy metals due to their excellent chemical stability, wide potential window, and good electrical conductivity. These characteristics make GCEs an effective platform for sensor development. In particular, nanomaterial-modified GCEs have emerged as a promising strategy, offering enhanced sensitivity, selectivity, and faster response compared to conventional analytical techniques. This review summarizes recent advances over the past five years in the use of GCEs modified with chemically synthesized nanoparticles for the simultaneous detection of multiple heavy metal ions, including cadmium, lead, mercury, and chromium. It also includes how quantum chemical methods have aided our understanding of these phenomena. Heavy metals pose significant environmental and public health risks, with well-documented neurological, cardiovascular, reproductive, and carcinogenic effects, highlighting the need for accurate and rapid monitoring methods. Regulatory limits established by organizations such as the World Health Organization and the Environmental Protection Agency further emphasize the demand for highly sensitive detection technologies. This review examines the fundamental properties of GCEs, common nanomaterial modification techniques, and their application in multi-ion detection systems. Key advantages such as cost-effectiveness, portability, and adaptability to diverse sample matrices are highlighted. Current challenges, including electrode fouling, selectivity, and matrix interference, are also addressed, along with future perspectives for improving GCE-based sensors for real-world environmental monitoring. Full article

The proliferation of Internet of Things (IoT) devices and embedded processors has recently spurred rapid advances in hardware-level security. This paper systematically reviews developments in securing microcontroller units (MCUs) and constrained embedded platforms from 2020 to 2026, a period marked by the finalization of NIST’s post-quantum cryptography standards and accelerated commercial deployment of hardware security primitives. Through analysis of the peer-reviewed literature, industry implementations, and standardization efforts, we survey five critical areas: post-quantum cryptography (PQC) implementations on resource-constrained hardware, physically unclonable functions (PUFs) for device authentication, hardware Roots of Trust and secure boot mechanisms, side-channel attack mitigations, and Trusted Execution Environments (TEEs) for microcontroller-class devices. For each domain, we analyze technical mechanisms, deployment constraints (power, memory, cost), security guarantees, and commercial maturity. Our review distinguishes itself through its integration perspective, examining how these primitives must be composed to secure real-world embedded systems, and its emphasis on post-standardization PQC developments. We highlight critical gaps including PQC memory overhead challenges, ML-resistant PUF designs, and TEE developer friction, while documenting commercial progress such as PSA Level 3 certified components and 500+ million PUF-enabled devices deployed. This synthesis provides practitioners with practical guidance for securing the next generation of IoT and embedded systems. Full article

The turning point that sparked the initiation of quantum theory was the Planck–Einstein postulate that the energy of a monochromatic radiation field is quantized in terms of photons, and this was followed by the development of the principles of quantum mechanics. Although some conceptual issues remain to be resolved, quantum mechanics is regarded as a well-established discipline which may lead to the unraveling of the nature of matter in general. Today, the influence of quantum mechanics is evident in its applications, with remarkable technological advances involving diverse aspects of the physical world. What appears to need particular attention, however, (after a hundred years have elapsed since the birth of quantum mechanics) is the impact that the concept of the ‘quantum’ has had beyond traditional quantum mechanics. The paper describes how the ‘quantum’ concept has influenced and continues to influence developments in physical systems, which are essentially classical, in that they are basically governed, entirely, or in part, by non-quantum laws, but in which, the physics is distinguished by its own special quantum—the photon analogue. The paper illustrates this by considering, as prototype examples, bulk plasmons and phonons. The study outlines the systematic quantization of plasmons and phonons, both of the polariton (transverse) forms and their longitudinal forms, and discusseshow these interact with quantum systems such as electrons, atoms, and condensed matter. It is demonstarted using one case, namely, involving longitudinal plasmons, how utilizing quantum concepts and techniques facilitate their interaction with matter, as in electron energy loss spectroscopy. Full article

Vehicular ad hoc networks (VANETs) require authentication systems that balance privacy, scalability, and post-quantum security. While lattice-based V-LDAA offers quantum resistance, it faces challenges in signature size, traceability, and integration. We propose post-quantum traceable direct anonymous attestation (PQ-TDAA), combining National Institute of Standards and Technology (NIST)-standard Dilithium2 and Falcon-512 signatures with adapted Beullens-style blind signatures and Fiat–Shamir simplified Schnorr proofs, reducing proof size by 69.2% (8 kB vs. V-LDAA’s 26 kB) and supporting European Telecommunications Standards Institute Technical Specification (ETSI TS) 102 941-compliant traceability through Road Side Unit (RSU)-assisted verification. Evaluated using SageMath, Python 3.11, and NS-3, PQ-TDAA-Falcon-512 achieves 8.1 ms and 49.7 ms end-to-end delays at 10 and 20 vehicles, respectively, with 64.7 Mbps goodput on congested 802.11p channels, showing promise for densities of ≤50 vehicles and advantages over Dilithium2. Real-world validation on ARM Cortex-A76 (Raspberry Pi 5, emulating automotive OBUs) yields sub-0.5 ms V2V cycles within 100 ms beacon intervals, supporting practical embedded deployment. Future work will extend PQ-TDAA to emerging 5G and NR-V2X settings, integrate more realistic mobility and channel models through coupled NS-3 and SUMO co-simulation, and investigate side-channel resistance for enhanced scalability and robustness in real deployments. Full article

The convergence of cyber-physical systems (CPSs), operational technologies (OTs), industrial control systems (ICSs), and quantum computing poses unprecedented challenges for the security and resilience of critical infrastructures (CIs). As quantum capabilities progress, classical cryptographic mechanisms such as RSA and ECC face increasing risks from quantum algorithms (Shor and Grover), while CPS and OT remain constrained by long life cycles, heterogeneity, and limited upgrade capabilities. This study conducts a systematic literature review (SLR) following a GQM-PICO-PRISMA methodological framework to examine 66 primary studies, selected from 1.522 records identified in seven scientific databases and published between 2005 and 2025. The review identifies dominant research domains, ranging from IoT/IIoT security to machine learning-based intrusion detection in CPS/OT environments, and synthesizes key challenges. Findings reveal significant fragmentation in CPS taxonomies, limited integration of post-quantum cryptography (PQC) into OT/ICS protocols, a scarcity of real-world datasets, and insufficient quantum threat modeling (QTM). This work consolidates and structures prior evidence into a literature-derived classification of quantum-era CPS/OT cybersecurity topics and distills a prioritized research agenda for advancing quantum-resilient architectures. Full article

In this work, we synthesized blue-fluorescent nitrogen-doped carbon quantum dots (N-CQDs) via a facile, economical, and environmentally friendly one-pot synthesis, using citric acid as the carbon source and polyethyleneimine (PEI) as the nitrogen dopant. The as-prepared N-CQDs exhibited uniform size distribution, with an average diameter of approximately 3 nm and a quantum yield of up to 23.6%. Based on the mechanism of HClO-triggered static fluorescence quenching and oxidation of surface amine groups on the N-CQDs, we established a quantitative detection platform for hypochlorous acid (HClO). The proposed method demonstrated a linear response over the concentration range of 0–40 μmol/L, with a detection limit as low as 0.17 μmol/L. It also featured a rapid response time (within 2 min), high selectivity, and strong anti-interference capability against various common species, including Cl⁻, H₂O₂, NO₂⁻, NO₃⁻, TBHP, TBO•, Br⁻, I⁻, S²⁻, F⁻, O²⁻ and HO•. Furthermore, the probe was successfully applied to detect HClO in real-world samples such as river water and beer. Owing to its outstanding photostability and low toxicity, it proved highly effective for monitoring intracellular HClO in living cells. Full article

The selective detection of microplastics (MPs) in aquatic environments is hindered by particle size diversity and matrix-induced interferences. This study reports an excitation–emission matrix (EEM) fluorescence sensing platform using polyamide-derived carbon quantum dots (PACQDs; 0.5–2.6 nm) for the size- and concentration-resolved detection of polyethylene terephthalate MPs (PETMPs). PACQDs exhibited a pronounced fluorescence “turn-off” response upon PETMP interaction, governed by particle size (10–149 μm) and loading (4–8 g L⁻¹). Small PETMPs (10 μm) followed linear Stern–Volmer behavior, achieving a detection limit of 1.67 mg L⁻¹ in deionized water. Conversely, larger particles induced non-linear optical effects, including scattering-driven enhancement and inner-filter effects. Multivariate analysis using PCA and PARAFAC resolved three distinct components associated with surface-state quenching, scattering-mediated redistribution, and surface area-driven binding. Component-specific scores confirmed that PACQDs are most sensitive to small PETMPs, while larger particles primarily introduce optical interference. Selectivity tests showed distinct discrimination of PETMPs over polyamide and polypropylene. In tap water, significant matrix effects were corrected via matrix-matched calibration, achieving recoveries within 80–120%. This study establishes EEM-based multivariate fluorescence as a mechanism-informed strategy for PETMP sensing, highlighting the robust applicability of PACQDs for monitoring small PETMPs in real-world water matrices. Full article

Cybersecurity threats are evolving constantly, and the arrival of quantum computing raises serious doubts about whether today’s cryptographic methods will hold up over time. This concern has motivated interest in algorithms designed to resist future attacks, with CRYSTALS-Kyber emerging as a practical candidate and forming the basis of an NIST post-quantum standard. This study focuses on protecting data exchanged between a vehicle sensor suite and cloud services over the Message Queuing Telemetry Transport protocol. Performance must remain acceptable; therefore, attention centers on lightweight and efficient execution while leveraging the board’s hardware capabilities to keep latency and resource usage low. Adding this layer of post-quantum encryption helps limit the exposure of critical telemetry and control data to sophisticated adversaries. It also aims to preserve integrity and confidentiality in vehicular communications as the Internet of Things becomes increasingly connected. This approach maintains a practical balance between forward-looking security and real-world deployability. Full article

Quantum computing is a new discipline that uses the ideas of quantum physics to do calculations that are not possible with conventional computers. Quantum bits, called qubits, could exist in superposition states, making them suitable for parallel processing in contrast to traditional bits. When it comes to addressing complex challenges like proof simulation, optimization, and cryptography, quantum entanglement and quantum interference provide exponential improvements. This survey focuses on recent advances in entanglement routing, quantum key distribution (QKD), and qubit management for short- and long-distance quantum communication. It studies optimization approaches such as integer programming, reinforcement learning, and collaborative methods, evaluating their efficacy in terms of throughput, scalability, and fairness. Despite improvements, challenges remain in dynamic network adaptation, resource limits, and error correction. Addressing these difficulties necessitates the creation of hybrid quantum–classical algorithms for efficient resource allocation, hardware-aware designs to improve real-world deployment, and fault-tolerant architecture. Therefore, this survey suggests that future research focus on integrating quantum networks with existing classical infrastructure to improve security, dependability, and mainstream acceptance. This connection has significance for applications that require secure communication, financial transactions, and critical infrastructure protection. Full article