Search Results (499)

Artificial intelligence (AI) systems increasingly depend on complex, multi-stage supply chains that incorporate pre-trained models, third-party datasets, open-source libraries, and automated training pipelines. This dependency creates a rapidly expanding attack surface in which model poisoning, dependency compromise, and provenance manipulation can undermine system integrity long before deployment. Existing AI governance frameworks—including the NIST AI Risk Management Framework and NIST’s Secure Software Development Framework—acknowledge supply chain risks but do not define a verifiable model provenance structure or cryptographically durable integrity guarantees. Simultaneously, the transition to post-quantum cryptography (PQC) introduces new requirements for long-lived AI artifacts: classical digital signatures used to verify model lineage, dataset integrity, and pipeline attestation will become vulnerable to quantum-enabled forgery within the expected operational lifetime of many AI systems. This paper synthesizes evidence from policy, standards, and benchmark sources to characterize the emerging AI supply chain threat landscape and identify cryptographic dependencies that the PQC transition disrupts. We propose a formal Model Bill of Materials with PQC-safe extensions (MBOM-PQC), a unified signing and attestation pipeline integrating ML-DSA and hybrid signature modes, and a five-level Supply Chain Assurance Maturity Model (SCAMM) supporting repeatable organizational evaluation. Together, these contributions aim to provide a structured foundation for AI supply chain integrity, supporting verifiable model lineage, authenticity, and trustworthiness through the PQC transition and beyond. The framework is presented as a design-science contribution comprising three integrated artifacts and is extended with operational guidance for continuous-learning pipelines (§6.5), a formal scoring methodology for organizational assessment (§7.3.5), and a hardware-root-of-trust migration cost matrix (§8.3.6). Full article

The quantum motion of a photon in an arbitrary medium was considered within the framework of the gauge symmetry group $SU\left(2\right)\otimes U\left(1\right)$ using the Yang–Mills (Y-M) equations for Abelian fields. A system of second-order partial differential equations (PDEs) for the vector wave function of a photon is derived using the first-order Y-M equations as identities. The full wave function of a photon was defined as the arithmetic mean of the components of the wave function. In a particular case, an equation is obtained for its full wave function, taking into account the structure of space-time in a plane perpendicular to the direction of propagation of the photon. The quantum state of a photon in a nanowaveguide was investigated, and it is shown that under certain conditions, it is reduced to the problem of two coupled 1D quantum harmonic oscillators (QHO) with variable frequencies. An explicit expression is obtained for the wave function of a photon, which is characterized by two vibrational quantum numbers. A quantum theory of a photon for a dissipative medium has been developed taking into account the processes of absorption and emission of photons. The mathematical expectation (ME) of the photon wave function is constructed as the product of two 2D integral representations in which the integrand is the solution of a system of two coupled second-order PDEs. The ME of the probability amplitude of the transition of a single-photon state into one of the two-photon entangled Bell states is constructed. Finally, it was proven that, in addition to frequency, spin, momentum and polarization, the photon also has a spatial structure responsible for the cross sections of processes in which this massless fundamental particle participates. Full article

Electric vehicles are an integral part of transportation electrification and are increasingly embedded within smart-grid-integrated energy systems that support accessibility, efficiency, and reduced environmental impact. As electric vehicle adoption grows, new challenges emerge in intelligent vehicle control, energy management, load management, and EV integration into the smart grid. In response, this paper presents a comprehensive survey of electric vehicle systems covering market evolution, enabling technologies, operational performance, and the energy systems that underpin scalable electric mobility. The survey illustrates the need for real-time monitoring, control, and optimization while exploring advanced computational approaches in quantum computing and machine learning that can address these challenges. Finally, this work identifies open research challenges and future directions related to energy optimization, smart-grid integration, and intelligent load management to provide a unified perspective on electric vehicles as a key component of both intelligent vehicle systems and sustainable smart transportation. Full article

Smooth window functions that restrict field actions to finite spacetime domains appear throughout quantum field theory, quantum optics, and open quantum systems, wherever interactions are switched on and off, detectors couple for finite durations, or systems decohere within bounded regions. When such a window function $◊\left(x\right)$ is introduced into the matter action of a covariant field theory, two structural consequences are unavoidable: the windowed Ward identities acquire boundary layer corrections confined to the window transition region, and the contracted Bianchi identity requires a compensating stress-energy contribution at the window boundary. Both consequences follow from the product rule of covariant differentiation and are independent of any specific physical motivation for the window. The present paper develops these consequences systematically for each sector of the Standard Model in curved spacetime. The windowed action prescription is applied to Dirac fermions, complex scalar fields, Maxwell theory, and the complete $SU{\left(3\right)}_c\times SU{\left(2\right)}_L\times U{\left(1\right)}_Y$ gauge Lagrangian. Each sector is shown to recover standard curved spacetime quantum field theory exactly within the localization window, with all deviations confined to a boundary layer whose thickness is set by the applicable operational localization scale—including decoherence, detector resolution, generalized uncertainty, or clock-precision bounds as appropriate. A Noether analysis yields windowed Ward identities of the form ${\nabla }_{\mu }(◊J^{\mu })=0$: gauge invariance and Lorentz symmetry are preserved exactly within the window, and apparent non-conservation is a kinematic boundary effect structurally identical to the open-system flux terms that arise when tracing over environmental degrees of freedom. The non-local boundary term $T_{\mu \nu }^{\mathrm{nl}}$ required by the Bianchi identity decomposes as $T_{\mu \nu }^{\mathrm{nl}}=T_{\mu \nu }^{\mathrm{comp}}+T_{\mu \nu }^{\mathrm{Rem}}$, where $T_{\mu \nu }^{\mathrm{comp}}$ is the boundary layer compensator and $T_{\mu \nu }^{\mathrm{Rem}}$ is its macroscopic coarse-grained remnant in the high-localization-density regime. A formal lemma establishes that, under stated regularity, phase-incoherence, finite-correlation-length, and variance-control assumptions, $T_{\mu \nu }^{\mathrm{comp}}$ vanishes upon coarse-graining for ordinary quantum fields, so standard field evolution leaves no macroscopic stress-energy remnant. The sharp-window limit recovers the Israel junction conditions exactly, and the smooth-window generalization is structurally identical to the Ashtekar–Krishnan dynamical horizon flux balance laws. The generalized uncertainty principle (GUP), extended uncertainty principle (EUP), relativistic GUP (RGUP), and Salecker–Wigner clock bounds constrain only the admissible operational thickness of the window boundary layer, $ϵ$, and do not alter the product rule origin of the windowed Ward identities or the Bianchi-required compensator. Full article

Embedded cryptography stands or falls on entropy quality, yet small devices have few trustworthy sources and little tolerance for heavyweight protocols. We build a Quantum Entropy as a Service (QEaaS) system that moves QRNG-derived entropy from a Quantis device to ESP32-class clients over post-quantum-secured channels. On the server side, the design exposes two paths: direct quantum entropy through a custom OpenSSL provider and mixed entropy through the Linux system pool. On the client side, we extend libcoap’s Zephyr support, integrate wolfSSL-based DTLS 1.3 into the CoAP stack, and add a BLAKE2s entropy pool that preserves the standard Zephyr extraction interface while introducing an injection API for server-provided entropy. Benchmarks on ESP32 hardware, targeting 100 iterations per configuration, show that ML-KEM-512 completes a DTLS 1.3 handshake in 313 ms on average without certificate verification, 35% faster than ECDHE P-256. Pairing ML-KEM-512 with ML-DSA-44 lowers the mean to 225 ms. Certificate verification adds roughly 194 ms for ECDSA but only 17 ms for ML-DSA-44, so the fully post-quantum configuration remains 63% faster than classical ECDHE P-256 with ECDSA even under full verification. Local BLAKE2s pool operations stay below 0.1 ms combined. On this platform, post-quantum key exchange and authentication are not only feasible; they are faster than the classical baseline. Full article

Artificial intelligence is typically formulated as an information-processing system composed of artificial neurons, where computation is understood as recursive operations connecting inputs and outputs. However, real neural systems are materially embodied and continuously reconfigured by metabolic and physical processes, suggesting that computation cannot be reduced to fixed causal structures. In this paper, we propose a theoretical framework that captures the interplay between informational and material processes as the interaction between two computational schemes: a vertical scheme, representing fixed cause–effect relations, and a horizontal scheme, representing transformations between such relations. We show that the vertical scheme corresponds to Bayesian inference, which updates probability distributions over a fixed hypothesis space, and is consistent with the free-energy minimization principle. In contrast, the horizontal scheme is formalized as inverse Bayesian inference, which modifies the hypothesis space itself by updating likelihood structures based on experienced data. We further demonstrate that the interplay between these schemes can be expressed algebraically as a process of continuously gluing Boolean algebras. This construction yields a non-distributive orthomodular lattice, i.e., quantum logic, without invoking Hilbert space formalism. In this view, quantum logic emerges not as a static logical system but as a structural consequence of dynamically reconfiguring causal contexts. This framework provides a unified perspective in which inference is understood not only as optimization within a fixed model but also as a process that generates and transforms the model itself. It offers a formal basis for describing open-ended computation and suggests a connection to approaches such as unconventional computing and Natural Born Intelligence, where computational structures evolve through interaction with material processes. Unlike existing approaches, this framework derives quantum-logic-like structure from the continual reconfiguration of causal contexts rather than from Hilbert-space assumptions or optimization within a fixed hypothesis space. Full article

GPU-accelerated quantum chemistry programs can dramatically reduce the time required for electronic structure calculations, yet most existing implementations either retrofit GPU kernels onto legacy CPU codebases or optimize individual kernels without addressing workflow-level integration overhead. We present GANSU (GPU Accelerated Numerical Simulation Utility), an open-source quantum chemistry framework written entirely in CUDA/C++ that integrates GPU-accelerated kernels for electron repulsion integrals, Fock matrix construction, and post-Hartree–Fock (post-HF) methods into a unified, GPU-resident execution pipeline. The key design principle is to eliminate host–device data transfers between computational stages by keeping all intermediate data, including density matrices, integral buffers, and Fock matrix replicas, on the GPU throughout the self-consistent field (SCF) iteration, combined with runtime-selectable integral strategies (stored ERI, resolution-of-the-identity, and Direct-SCF) that adapt to system size and available memory. On an NVIDIA H200 GPU, GANSU achieves end-to-end speedups of up to $52\times$ over PySCF for SCF, $45\times$ for MP2 on molecules with up to 470 basis functions, and $44\times$ for FCI, while outperforming GPU4PySCF by up to $34\times$ for FCI, across a range of molecular systems with up to 650 basis functions. The framework further provides analytical energy gradients and geometry optimization with nine algorithms, all operating within the same GPU-resident data flow. These results demonstrate that workflow-aware kernel integration, not just kernel-level optimization, is essential for realizing the full potential of GPU acceleration in scientific computing. GANSU is publicly available under the BSD-3-Clause license. Full article

Quantum computing introduces new computational capabilities that can support advanced cybersecurity solutions when combined with machine learning. In recent years, quantum machine learning (QML) has emerged as a promising approach for enhancing network intrusion detection systems (IDS), particularly for analyzing complex and high-dimensional network traffic. This paper presents a systematic survey of QML techniques applied to network intrusion detection. The survey reviews peer-reviewed studies published up to January 2026 that employ quantum, hybrid quantum–classical, and quantum-inspired learning models for IDS. The selected studies are analyzed with respect to the algorithms used, intrusion detection datasets, and evaluation metrics reported. The analysis shows that most current approaches rely on simulated quantum environments and legacy datasets, while evaluation practices remain inconsistent across studies. These findings highlight the early developmental stage of QML-based IDS and the need for standardized evaluation protocols and more realistic experimental settings. Finally, open challenges and future research directions are identified to support the development of reliable, scalable, and practically deployable QML-based intrusion detection systems. Full article

We propose a proposal to achieve strong mechanical squeezing in an optomechanical system through the joint effect of a weak squeezed vacuum field and Duffing nonlinearity. The squeezing of the cavity field induced by the squeezed vacuum field is transferred to the mechanical oscillator, which has already been squeezed via Duffing nonlinearity. This joint effect significantly enhances the degree of mechanical squeezing, enabling it to exceed the 3 dB strong mechanical squeezing limit. Moreover, the resulting mechanical squeezing exhibits remarkable robustness against thermal noise. The joint effect proposed in this scheme can be directly observed through homodyne detection of the cavity output field. This novel approach opens up a new avenue for generating a strong mechanical squeezed state and provides a promising pathway for the applications of macroscopic quantum control in quantum sensing and quantum information processing. Full article

Surface Acoustic Wave (SAW) technology, long central to analog signal processing and RF filtering, is undergoing a major renewal. Driven by advances that decouple SAWs from traditional piezoelectric materials and fixed-function devices, the field is gaining unprecedented control over acoustic, optical, and electronic interactions at the micro and nanoscale. This review synthesizes these developments across four fronts: new physical mechanisms for SAW manipulation, emerging material platforms, ranging from thin films to 2D systems, along with reconfigurable device architectures and circuits, and the expanding landscape of applications they enable. Optical methods are reshaping how SAWs are generated and controlled, bypassing the limits of conventional electromechanical coupling. Coherent optical excitation of high-Q SAW cavities via Brillouin-like optomechanical interactions now grants access to modes in non-piezoelectric substrates such as diamond and silicon, while on-chip SAW excitation in photonic waveguides through backward stimulated Brillouin scattering opens new integrated sensing routes. In parallel, magneto-acoustic experiments have revealed nonreciprocal SAW diffraction from resonant scattering in magnetoelastic gratings. On the device side, ZnO thin-film transistors integrated on LiNbO₃ exploit acoustoelectric coupling to realize voltage-tunable phase shifters; UHF Z-shaped delay lines achieve high sensitivity in a compact footprint; and parametric synthesis of wideband, multi-stage lattice filters targets 5G-class performance. Atomistic simulations show that SAW propagation in 2D MXene films can be engineered via surface terminations, while aerosol jet printing and SAW-assisted particle patterning provide agile, cleanroom-light fabrication of microfluidic and magnetic components. These advances enable applications ranging from hybrid quantum systems and quantum links to lab-on-a-chip particle control, SBS-based and UHF sensing, reconfigurable RF front-ends, and soft robotic actuators based on patterned magnetic composites. At the same time, optical techniques offer non-contact probes of dissipation, and MXenes and other emerging materials open new regimes of acoustic control. Conclusively, they are transforming SAW technology into a versatile, programmable platform for mediating complex interactions in next-generation electronic, photonic, and quantum systems. Full article

Carbon quantum dots (CQDs) and stimuli-responsive hydrogels are advanced functional materials whose hybridization yields CQD-enhanced stimuli-sensitive hydrogels, opening new interdisciplinary avenues for smart material applications. This review systematically summarizes the latest advances in these composites, focusing on synthetic strategies, structure–property modulation mechanisms, and practical applications. Distinct from existing reviews that either investigate CQDs or hydrogels independently or discuss their composites in a single research field, this work features core novelties in integration strategy, application scope and critical analysis: it systematically compares the advantages, limitations and applicable scenarios of three typical CQD–hydrogel integration approaches (physical entrapment, in situ synthesis, covalent conjugation), comprehensively covers the multi-field application progress of the composites and conducts in-depth cross-field analysis of their common scientific issues and technical bottlenecks. By incorporating CQDs, the composites achieve remarkable performance optimizations: 40% improved mechanical toughness, sub-ppm-level heavy metal-sensing sensitivity, and over 80% organic dye photocatalytic degradation efficiency, addressing pure hydrogels’ inherent limitations of insufficient strength and single functionality. These enhancements enable sophisticated applications in biomedical field (real-time biosensing, controlled drug delivery), environmental remediation (pollutant detection/degradation), energy storage, and flexible electronics. The synergistic interplay between CQDs and hydrogels facilitates precise single/multi-stimulus responsiveness (pH, temperature, light), a pivotal advance for precision medicine and intelligent environmental monitoring. Despite promising progress, the large-scale practical application of CQD–hydrogel composites still faces prominent challenges: the difficulty in scalable fabrication with the uniform dispersion of CQDs in hydrogel matrices, poor long-term stability of most composites under physiological cyclic stress (service life < 6 months in practical tests), and low accuracy in discriminating multi-stimuli in complex real-world matrices. Future research should prioritize biomass-based eco-friendly CQD synthesis, machine learning-aided multimodal responsive systems, and 3D bioprinting for scalable manufacturing. Full article

FAEST is a National Institute of Standards and Technology post-quantum signature candidate based on the Vector Oblivious Linear Evaluation-in-the-Head paradigm, whose signing performance is dominated by repeated Advanced Encryption Standard Counter-based Pseudorandom Generator calls. The reference implementation provides no FAEST-specialized acceleration for Advanced RISC Machine platforms. This paper proposes a three-layer Advanced Reduced Instruction Set Computer Machine NEON Single Instruction Multiple Data optimization: a register-resident 256-byte S-box with Table Lookup/Table Lookup with Extension-based SubBytes and four-way/eight-way parallel Advanced Encryption Standard processing; a fixed-length Pseudorandom Generator specialized for the FAEST tree structure; and Portable Operating System Interface for Unix thread-based parallelization of independent Vector Oblivious Linear Evaluation instances. Evaluated on all 12 parameter sets of FAEST v2 on Raspberry Pi 4 (without Advanced Reduced Instruction Set Computer Machine version 8 crypto-extensions) and Apple M2 (with hardware Advanced Encryption Standard support), the proposed method achieves signing speedups of up to 136.9x on Raspberry Pi 4 and 330.1x on Apple M2 over the pure-C reference. On Raspberry Pi 4, the NEON implementation outperforms OpenSSL; on Apple M2, the NEON-plus-Portable Operating System Interface for Unix thread configuration outperforms hardware-accelerated OpenSSL across all parameters, confirming that NEON SIMD combined with task-level parallelization can exceed hardware-accelerated single-thread throughput on Advanced Reduced Instruction Set Computer Machine-based platforms. Full article

The Mpemba effect refers to the counterintuitive situation in which a system initially farther from equilibrium can relax faster than one that starts closer to it. In quantum systems, the effect is enriched by the presence of coherent dynamics, dissipation, and metastable manifolds associated with long-lived Liouvillian modes. Here we demonstrate a giant Mpemba effect in open quantum systems, where relaxation can be either hyper-accelerated or dramatically slowed depending on the initial state. We focus on weakly-coupled particle-conserving bosonic networks, each of which independently relaxes rapidly to a unique stationary state. When a weak coherent interaction is introduced, the composite system typically develops slow metastable modes and a hierarchy of relaxation timescales. We show that by tailoring the interaction Hamiltonian, these slow modes can be effectively suppressed for a broad class of initial states satisfying a minimal global requirement, enabling ultrafast relaxation even when the system starts far from equilibrium. Conversely, other initial states—sometimes arbitrarily close to the stationary state—may remain trapped in the metastable manifold and decay anomalously slowly. This mechanism provides a general route to engineer giant Mpemba effects, offering new possibilities for controlling dissipative dynamics, accelerating state preparation, and manipulating relaxation processes in complex quantum devices. Full article

The literature indicates that the qubit requirements for factoring RSA-2048 remain on the order of 1 million, under commonly assumed architectures and error-correction models, leaving a substantial gap between current resource estimates and near-term practical feasibility. Reducing this requirement to the low-thousand-qubit regime therefore remains an important open research objective. This work proposes a hybrid classical–quantum algorithm that uses a classical modular exponentiation subroutine with a Quantum Number Theoretic Transform (QNTT) circuit to increase the speed and reduce the required quantum resources relative to Shor’s algorithm for integer factorization, which underpins cryptographic systems like RSA and ECC. We evaluate multiple coprime numbers, the result of multiplication of two primes, in both simulation and real quantum hardware, using IBM’s reference Shor implementation as the baseline. Because Shor and proposed Jesse–Victor–Gharabaghi (JVG) use different register sizes for the same coprime N, the reported gate/depth reductions should be interpreted as end-to-end quantum-resource budgets for factoring the same N, rather than a per-qubit or transform-only efficiency claim. In simulation, the JVG algorithm achieved substantial practical reductions in computational resources, decreasing runtime from 174.1 s to 5.4 s, memory usage from 12.5 GB to 0.27 GB, and quantum gate counts by approximately 99%. On quantum hardware, JVG reduced the required runtime from 67.8 s to 2 s, and the quantum gate counts by over 98%. We showed that the proposed algorithm can address the relevant RSA-1024 case scenario, establishing that this method can provide validation for large-scale situations. Furthermore, extrapolation to RSA-2048 indicates that the JVG algorithm significantly outperforms Shor’s approach, requiring a projected quantum runtime of 29 h for ten thousand runs for factorization under identical scaling assumptions. Overall, these results support JVG as a more hardware-compatible and robust noise-tolerant substitute for Shor’s framework, offering a viable research direction toward practical quantum integer factorization on near-term Noisy Intermediate-Scale Quantum (NISQ) devices. Full article

The emergence of classicality through quantum decoherence is commonly described from complementary perspectives emphasizing stability (environment-induced superselection), objectivity (Quantum Darwinism), or physical feasibility (information thermodynamics). In realistic open quantum systems, however, these aspects coexist and compete under finite physical resources. In this work we argue that classical structure selection is most naturally understood as a resource-constrained, multi-objective process. We introduce the Informational Economy Functional (IEF), an effective accounting framework that places loss of distinguishability, energetic dissipation, and the generation of redundantly accessible records on equal footing. The associated Principle of Informational Economy characterizes emergent classical structures as those achieving an optimal compromise among stability, objectivity, and energetic feasibility. Classicality is thus neither maximally stable, nor maximally redundant, nor maximally energy-efficient, but instead reflects a Pareto-optimal balance shaped by environmental constraints. The IEF yields falsifiable predictions concerning pointer-structure variability, redundancy deformation, and resource-sensitive trade-offs, and suggests concrete experimental tests in continuously monitored quantum platforms. Classical reality is thereby reinterpreted as the most economical configuration in which information can stably form, propagate, and persist. Full article