Search Results (226)

Optimization problems in machine-learning (ML) applications can be computationally challenging for classical methods, particularly when they involve large, unstructured search spaces. Quantum computing offers a promising approach to combinatorial optimization by utilizing quantum superposition and amplitude amplification. This paper presents a new methodology using logic-based quantum machine learning (QML) as a complete framework for employing a multi-solution ternary Grover’s algorithm to minimize incomplete binary functions represented by Pseudo-Kronecker Reed–Muller (PKRO) expansions, consistent with Occam’s razor principle. Unlike previous quantum approaches based on Kronecker Reed–Muller (KRO) or fixed-polarity Reed–Muller (FPRM) representations, our work introduces the first Grover-based optimization framework for PKRO forms. This framework formulates logic minimization as a quantum search problem to identify minimum-cost AND-XOR representations with the fewest nonzero coefficients. A hybrid binary–ternary quantum architecture is developed to explore the enlarged PKRO search space, enabling optimization of both completely and incompletely specified binary functions. Owing to the greater flexibility of PKRO expansions, our framework produces more compact AND–XOR representations than KRO- and FPRM-based approaches. Across all 256 3-variable binary functions, PKRO provides less nonzero coefficients for 4.688% of functions compared with KRO and for 37.5% compared with FPRM, with average reductions of 0.047 and 0.484 nonzero coefficients, respectively. Our multi-solution ternary Grover search reduces the number of Grover iterations by more than 91% compared with the single-solution approach. Full article

The effective resolution of non-deterministic polynomial time hard (NP-hard) combinatorial optimization problems requires a delicate balance between global exploration and local exploitation. While Quantum-Inspired Algorithms (QIAs) leverage principles of superposition to explore vast search spaces, they often lack the fine-grained exploitation capabilities of classical heuristics. To address this limitation, we propose the Quantum-Memetic Hybrid Algorithm (QMHA), a component-based framework that synergistically integrates qubit-based global search with adaptive classical refinement. The QMHA architecture explicitly coordinates five distinct algorithmic components: (1) quantum rotation gates for exploration, (2) a problem-aware memetic operator for immediate solution refinement, (3) an adaptive learning rate schedule, (4) periodic local search, and (5) a stagnation-based population reset for diversity management. We rigorously evaluate the framework against nine established metaheuristics, including Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO), MAX-MIN Ant System (MMAS), Memetic Algorithms (MA), Quantum Evolutionary Algorithm (QEA), and Harmony Search (HS), across a comprehensive benchmark suite comprising six NP-hard problem families: constrained combinatorial (Knapsack), graph-based (Max-Cut), permutation-based (TSP), constraint satisfaction (Graph Coloring), bin optimization (Bin Packing), and scheduling (Flow Shop Scheduling), as well as real-world machine learning (Feature Selection) problems and the continuous Congress on Evolutionary Computation (CEC) 2022 benchmark. Statistical analysis using Friedman tests and Nemenyi post hoc comparisons confirms that QMHA achieves a statistically significant performance advantage ($p<0.004$) and superior average rank (1.5) compared to component baselines and state-of-the-art competitors. Comprehensive analyses include computational complexity profiling, parameter sensitivity mapping, scalability testing up to $D=2000$, noise robustness evaluation, variable correlation degradation analysis, a six-component ablation study, exploration–exploitation dynamics tracking, integration mechanism comparison across five architectures, and a multi-objective extension feasibility study. The proposed framework offers a robust, verified approach to hybrid optimization without relying on biological metaphors. Full article

The standardization of post-quantum cryptography has shifted the central question of migration from which algorithms to adopt toward whether organizations can change cryptography at all. Prevailing treatments describe crypto-agility as a technical property of protocols and software, an abstraction layer that lets algorithms be swapped; that framing explains part of the problem and understates the rest. Drawing on organizational-capability theory, information-technology governance scholarship, and sociotechnical systems thinking, this paper reframes crypto-agility as a sociotechnical organizational capability: the coordinated capacity of people, process, architecture, and governance to detect cryptographic change, decide on a response, and reconfigure cryptographic mechanisms across the estate while preserving security and operations. The paper specifies the capability construct and its four dimensions and proposes a five-level maturity model that folds the standardization, agility, and hybridization pillars into its indicators. A preliminary application to three organizations that document their migration publicly shows the model can be applied and discriminates among them, while revealing that public evidence under-observes the people and governance dimensions. The model is presented as a theoretically grounded artifact for future validation, not as observed data; implications for governance, procurement, and assurance are developed. Full article

The coming era of quantum supremacy has created an existential threat to critical information infrastructures (CIIs). This threat is realized through delayed attack vectors of the “Harvest Now, Decrypt Later” (HNDL) class. Existing security paradigms dictate forced migration to post-quantum cryptography (PQC) algorithms. However, in the context of legacy architectures, and in particular in the ICS and SCADA segments, such a strategy generates quantum technological friction. This friction can trigger cascading failures of legitimate CII services. This article proposes addressing the PQC transformation problem not as a linear IT modernization task, but as a general-sum differential Stackelberg game. The concept of “active deception” (Cyber Deception) is described as a Leader (Defender) strategy, i.e., the variable redistribution of budgets between real cryptographic migration and the generation of resource-intensive decoy infrastructures. Using the Forward-Backward Sweep Method algorithm, optimal software controls are obtained for three typical architectural profiles, calibrated using statistical data from the critical information infrastructure of the Republic of Kazakhstan. Numerical simulations have demonstrated that in high-inertia and strategic networks, maximizing active deception creates the necessary “temporal buffer”, minimizing the damage from an HNDL compromise while maintaining operational continuity. The results support the need to revise rigid cryptographic transition regulations in favor of flexible, mathematically sound hybrid strategies. Full article

Clustering may uncover latent behavioral structure in Autism Spectrum Disorder (ASD) screening data without using outcome labels, but the resulting partitions depend strongly on data geometry and the adopted similarity measure. Quantum-hybrid clustering offers alternative distance and similarity estimators, yet whether these subroutines improve on classical methods under controlled conditions remains unclear. We conduct a benchmark of k-means, DBSCAN, agglomerative clustering, and spectral clustering against their quantum-hybrid counterparts. All methods are evaluated in a common 13-dimensional representation, with hyperparameters selected exclusively through internal validation indices. The evaluation covers four synthetic geometries and a 13-dimensional PCA representation of an ASD screening dataset, each containing 300 samples and evaluated over 10 seed-defined stochastic runs. Clustering quality is measured using the Silhouette Index (SI), Davies–Bouldin Index (DBI), Calinski–Harabasz Index (CHI), Adjusted Mutual Information (AMI), and Adjusted Rand Index (ARI). Under this validation protocol, Q-means exactly recovers the Gaussian clusters and improves label agreement on anisotropic data, but it does not outperform classical k-means on the ASD screening data. Q-spectral significantly reduces DBI on Two Moons, Concentric Rings, and ASD screening data, although these reductions do not consistently translate into higher AMI or ARI. Q-DBSCAN and Q-agglomerative exhibit greater sensitivity to distance distortions and finite-shot noise. Overall, the results reveal geometry-dependent trade-offs rather than uniform quantum-hybrid superiority. We relate these findings to theoretical complexity, measurement noise, state-preparation costs, and eigensolver bottlenecks in near-term implementations. Full article

Patent document–keyword count data are typically high-dimensional, sparse, and dominated by zero entries, which makes it difficult to simultaneously reconstruct keyword frequencies and identify meaningful technological structures. This study proposes a machine learning approach to latent structure learning for zero-inflated patent keyword count data. The proposed zero-gated latent factor model (ZG-LFM) combines nonnegative matrix factorization (NMF) with keyword-specific logistic occurrence models. NMF is used to extract interpretable document–factor and factor–keyword representations, while the occurrence gate estimates the probability that each keyword appears in a given patent document. The method was evaluated in an initial domain-specific case study using a document–keyword matrix constructed from 9434 quantum computing patent documents and 175 keywords, of which 87.60% of the entries were zero. Predictive performance was assessed using root mean squared error, mean absolute error, and the area under the receiver operating characteristic curve across different numbers of latent factors. The experimental results showed that NMF provided more accurate keyword count reconstruction, whereas the proposed model consistently achieved better discrimination between zero and nonzero keyword entries. These findings indicate that latent count reconstruction and keyword occurrence modeling provide complementary information for analyzing sparse patent data. The learned latent factors further revealed coherent quantum computing subdomains, including hybrid quantum–classical execution, quantum machine learning, quantum state measurement and error analysis, quantum cryptography, superconducting chips, quantum circuits, optical control, qubit devices, and optimization algorithms. The proposed framework therefore provides interpretable latent technology structures while improving the identification of keyword occurrence patterns in zero-inflated patent data. These findings demonstrate the feasibility of the framework within the analyzed quantum computing corpus; its generalizability across other technological domains remains to be evaluated. Full article

Single-path transmission optimization is a core task for resource scheduling and operation of complex networks, which requires coordinated optimization of transmission cost and flow. Classical algorithms bear heavy computational loads in high-dimensional decision spaces as networks grow. This paper constructs a hybrid quantum model integrating quantum approximate optimization algorithm (QAOA) and cubic spline interpolation. Paths, discrete flows, and trade-off coefficients are unified within a quadratic unconstrained binary optimization (QUBO) model. Least-squares fitting converts native parameters into QUBO coefficients, whose fitting errors are measured to verify robustness and penalty sensitivity, and auxiliary variables eliminate high-order terms to exponentially cut qubit consumption. QAOA narrows the feasible range via global coarse search, and cubic spline interpolation further yields precise continuous flow values. Powered by quantum superposition for parallel full-space exploration, the framework avoids repeated modeling for separate bias coefficients. Mixed integer programming (MIP) and genetic algorithm (GA) are adopted as comparative benchmarks. For the small-scale network instance, the relative error between the proposed method and the global optimum solved by MIP is less than 1%. For the large-scale case, the overall error of our approach remains within an acceptable range even when discrepancies exist between results yielded by classical algorithms. Full article

Hybrid quantum–classical machine learning offers a promising direction for advancing automated quality control in industrial settings. In this study, we investigate two hybrid quantum–classical approaches for classifying defects in aluminum TIG welding images and benchmarking their performance against a conventional deep learning model. A convolutional neural network is used to extract compact and informative feature vectors from weld images, effectively reducing the higher-dimensional pixel space to a lower-dimensional feature space. Our first quantum approach encodes these features into quantum states using a parameterized quantum feature map composed of rotation and entangling gates. We compute a quantum kernel matrix from the inner products of these states, defining a linear system in a higher-dimensional Hilbert space corresponding to the support vector machine (SVM) optimization problem and solving it using a Variational Quantum Linear Solver (VQLS). We also examine the effect of the quantum kernel condition number on classification performance. In our second method, we apply angle encoding to the extracted features in a variational quantum circuit and use a classical optimizer for model training. Both quantum models are tested on binary and multiclass classification tasks, and the performance is compared with the classical CNN model. Our results show that while the CNN model demonstrates robust performance, hybrid quantum–classical models perform competitively. This highlights the potential of hybrid quantum–classical approaches for near-term real-world applications in industrial defect detection and quality assurance. Full article

This work explores the practical application of the Quantum Fourier Transform (QFT) for image alignment, focusing on the estimation of rotation angles in grayscale images containing structured line patterns and text. Motivated by the need to demonstrate quantum computing’s potential in addressing real-world image processing tasks, we develop a hybrid approach that combines classical pre-processing with quantum computation. We demonstrate that QFT can be applied to a concrete image processing task, illustrating the practical utility of quantum computing beyond theoretical examples. A classical baseline using the Fast Fourier Transform (FFT) is implemented via the ToolIP framework, achieving fast and accurate angle estimation. In parallel, a quantum version replaces the FFT with a simulated QFT on IBM’s Qiskit platform, using the Quantum Image Encoding Probability scheme to reduce qubit requirements. The classical method delivers results in milliseconds, while the quantum implementation, constrained by simulation and encoding overhead, demands significantly greater computational effort. Nonetheless, our findings show that QFT-based angle estimation is feasible and can yield results comparable to classical techniques. This study demonstrates the applicability of quantum algorithms to real-world image processing and underscores both the theoretical promise of QFT and the practical limitations faced in the current Noisy Intermediate-Scale Quantum (NISQ) era. Full article

The Internet Key Exchange Protocol Version 2 (IKEv2) underpins Internet Protocol Security (IPsec) by establishing secure associations and negotiating cryptographic keys. Its reliance on classical public-key primitives such as Elliptic Curve Diffie–Hellman (ECDH) renders it vulnerable to quantum attacks, as Shor’s algorithm can break these schemes once large-scale quantum computers become available. To address this challenge, we integrate post-quantum cryptography into IKEv2 using a hybrid key exchange combining ECDH over P-384 with the ML-KEM-768 parameter set of the Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM), following Request for Comments (RFC) 9370 and RFC 9242. We present a Fragmentation Boundary Model that identifies when post-quantum payloads approach or exceed the non-fragmenting IKE_SA_INIT payload budget across ML-KEM parameter sets, Internet Protocol (IP) versions, and effective path maximum transmission units (PMTUs). We implemented the hybrid design on a Texas Instruments TM4C1294 microcontroller running FreeRTOS and CycloneTCP, and measured its execution time, memory footprint, and network overhead. Across 150 successful handshakes per configuration, mean Security Association establishment time increased from 3390.4 to 3488.6 ms, an overhead of 98.2 ms (2.9%). Flash use increased by 8%, random-access memory (RAM) use by 5%, and total IKEv2 traffic size by 42%, while no IP fragmentation was observed under the evaluated Internet Protocol version 4 (IPv4) conditions. These findings establish the feasibility of the evaluated hybrid configuration on the TM4C1294 platform and provide a platform-specific baseline for further evaluation of quantum-resilient IPsec on embedded automotive architectures. Full article

Hybrid post-quantum TLS deployments pair classical and post-quantum signature algorithms to resist future quantum adversaries. However, to the best of our knowledge, no active IETF TLS, PQUIP, or LAMPS working-group draft treats CPU-cost adversarial algorithm substitution as a first-class availability threat. We define the Cryptographic Amplification Factor ($\mathrm{CAF}$), a metric for the per-resource cost asymmetry that an adversary induces by forcing a TLS 1.3 server onto a high-cost signature algorithm (SLH-DSA) instead of a low-cost one (ECDSA or ML-DSA). We characterise four adversary classes—insider misconfiguration, supply-chain compromise, remote TLS peer, and protocol man-in-the-middle—demonstrate working proof-of-concept exploits on representative testbeds, and evaluate three defensive primitives under realistic deployment conditions. On the liboqs 0.14.0 reference implementation, ${\mathrm{CAF}}_{r_{\mathrm{cpu}}}$ reaches $\mathrm{12,255}\times$ on Apple M-series ARM64 and $\mathrm{14,272}\times$ on AWS Graviton3 (3904–$\mathrm{14,272}\times$ across five microarchitectures). Under a representative two-worker hardware security module (HSM) calibration ($\lambda =13.5$ TPS), substitution drives the modelled queue utilisation $\rho$ past the stability boundary ($1.44\times {10}^{-4}\to 1.787$), a model-independent saturation; on the testbed, we observe server-CPU saturation, accept-queue exhaustion, and dropped connections. Two enforcement defences and a Parallel Signing Architecture provably bound $\rho \le \alpha <1$ by admission control, regardless of attack rate. The exposed surface is concentrated today in high-assurance and financial-sector deployments and widens as FIPS 206 finalises. Full article

The Domain Name System Security Extensions (DNSSEC) provide the cryptographic foundation for DNS data integrity and origin authentication. Cryptographically relevant quantum computers (CRQCs) threaten this foundation, since RSA and ECDSA, the signature schemes underlying DNSSEC, are susceptible to polynomial time attacks via Shor’s algorithm. NIST-standardized post-quantum cryptography (PQC) signatures structurally exceed the 1232-byte UDP payload limit of DNS, and no existing proposal simultaneously resolves all transport, security, and operational constraints. Following PRISMA 2020 guidelines, this paper systematically reviews 27 peer-reviewed works published between 2020 and 2025, providing the first unified analytical framework for post-quantum DNSSEC research across five dimensions: transport constraints, cryptographic agility, denial-of-service resilience, operational deployment, and standardization readiness. Three findings emerge. First, every NIST-standardized PQC scheme is structurally incompatible with UDP transport in non-minimal DNSSEC responses. Second, analytical modeling predicts that the DNSKEY-RRSIG validation product scales quadratically in the number of coexisting signature algorithms, and that the interaction of KeyTrap with PQC validation overhead compounds denial-of-service severity super-linearly. This prediction is formalized as a testable hypothesis pending experimental characterization, while the algorithm agility mechanism enabling PQC deployment simultaneously enables downgrade attacks. Third, the architecture of signature-based DNSSEC lacks retroactive protection against key compromise, which is the signature-domain equivalent of forward secrecy. No security proof exists for hybrid dual-signature constructions owing to IND-CMP composition problems. We identify five previously unaddressed research gaps and present a prioritized three-phase roadmap through 2029. Post-quantum DNSSEC constitutes a protocol-redesign problem, not an algorithm-substitution problem. Full article

Quantum algorithms for flow simulation are advancing rapidly, but reproducible wall-bounded benchmarks with classical reference data are still needed to evaluate future quantum and hybrid quantum-classical solvers. This work presents a forced D3Q19 multiple-relaxation-time (MRT) lattice-Boltzmann method (LBM) benchmark for body-force-driven Poiseuille flow in a three-dimensional channel. The solver combines periodic streamwise and spanwise boundaries, halfway bounce-back walls, moment-space relaxation, and body-force forcing with the half-force velocity correction. The solution is verified against the analytical parabolic profile using relative $L_2$ and maximum profile errors, mass conservation, extrapolated wall slip, and wall-normal leakage. A verification study over grid resolution, relaxation time, forcing strength, and initialization demonstrates second-order grid convergence and robust conservation behavior. The verified timestep is then decomposed into quantum-relevant primitives, including streaming, wall reflection, moment transformation, MRT relaxation, equilibrium evaluation, forcing, macroscopic recovery, and measurement. The resulting benchmark connects flow-solver accuracy metrics with operator-level requirements for quantum implementation, providing a compact reference problem for future quantum processing unit (QPU)-assisted, hybrid quantum-classical, and quantum-linear-solver-based computational fluid dynamics (CFD) studies. Performance gains over classical LBM execution are not assessed here. Full article

With the development of quantum computing, classical cryptosystems (RSA, ECDSA) that ensure the security of distributed ledgers face an existential threat. This paper examines protocols for protecting personal data (PD) in blockchain, taking into account the “Harvest Now, Decrypt Later” strategy. We propose and formalize a family of protocols designed for storing and exchanging personal data in blockchain systems. The article describes in detail approaches to software implementations of smart contracts for the Ethereum (using ECIES (Elliptic Curve Integrated Encryption Scheme) and Keccak-256) and Hyperledger Fabric 2.5 (integrating NIST post-quantum standards: ML-KEM (Module-Lattice-Based Key Encapsulation Mechanism) and ML-DSA (Module-Lattice-Based Digital Signature Algorithm)) platforms based on the developed protocols. For all developed protocols, a Threat Agent Model (TAM) is presented, threat scenarios are examined, and resilience to typical attack scenarios is demonstrated. A comparative analysis of computational efficiency and overhead is conducted. The results show that using lattice cryptography provides high performance, but the 50-fold increase in signature size makes direct implementation of PQC (Post-Quantum Cryptography) in Layer 1 public networks economically unfeasible. A hybrid model and the use of Layer 2 to ensure quantum resistance are proposed. Full article

Martian dust storms cut off communication and break standard robot navigation. We built a hybrid system that keeps robot swarms alive during these blackouts and recovers their data quickly. Our rovers use Spiking Neural Networks (SNNs) on their own edge processors to navigate without a signal. Once the storm passes, we use the Quantum Approximate Optimization Algorithm (QAOA) on a cloud platform to merge the fragmented maps the rovers collected while they were offline. We tested this system in a Robot Operating System 2 (ROS 2) and Gazebo environment using a simulated 10-rover Martian deployment. During the simulated blackout, our SNN edge navigation achieved a 92.0% survival rate, outperforming traditional planners like Dynamic Window Approach (DWA) (29.0%) and Timed Elastic Band (TEB) (24.3%). The neuromorphic approach also reduced overall system power consumption by 80.0% compared to a traditional unoptimized Graphics Processing Unit (GPU)-based Simultaneous Localization and Mapping (SLAM) baseline. For the map recovery phase, our simulated QAOA proof-of-concept evaluated the map constraints in just 1.2 ms, compared to 50.0 ms for a classical Generalized Iterative Closest Point (G-ICP) and g2o pose-graph approach. Despite the noisy sensor data collected during the blackout, the final quantum-stitched map achieved an 8.54 cm Root Mean Square Error (RMSE). These results show that combining edge-based neuromorphic processing with quantum cloud computing secures swarm survival and accelerates post-disaster data recovery for deep-space missions. Full article