We benchmark eight classical optimizers for exact-statevector VQE calculations on a controlled hierarchy of frustrated spin models, ranging from a diagonal Ising glass to transverse-field Ising and anisotropic Heisenberg models. The benchmark includes local, stochastic-gradient, evolutionary, covariance-adaptation, and swarm-based optimization methods under matched function-evaluation budgets. To understand their performance beyond final energies, we characterize the underlying Hamiltonian–ansatz landscapes in terms of local minima, gradients, curvature, and ground-state reachability. We use simple variational circuits, from an R_y product-state ansatz for the diagonal model to shallow R_y–CNOT hardware-efficient circuits for the noncommuting models, and study how increasing circuit depth changes their expressivity, reachability, and optimization geometry. We find that optimizer performance changes substantially across the model hierarchy and is closely connected to landscape structure, while the variational gap represents a separate source of error. These results show how classical optimization, variational expressivity, and landscape geometry jointly determine VQE performance for frustrated spin models.

Variational quantum algorithms are a leading approach to NISQ-era computation, but finite-shot measurements make their optimization stochastic. We benchmark more than fifty metaheuristic algorithms on a multi-phase benchmark covering Ising and 192-parameter Hubbard models using a hardware-efficient TwoLocal ansatz in a shot-noise-only setting, with finite-shot sampling but without hardware-specific coherent or decoherence errors. Standard differential evolution and local optimizers (COBYLA and SPSA) perform well in noiseless simulations but degrade or stagnate under finite-shot sampling. Among the tested methods, CMA-ES and adaptive DE variants, particularly iL-SHADE, were the most reliable on the noisy objectives. These results limit the generality of standard DE findings obtained from statevector simulations and indicate that adaptive parameter control may improve robustness to finite-shot noise.

This paper provides a structural and methodological audit of several quantum-inspired Differential Evolution variants, focusing primarily on the recently proposed PSEQADE algorithm. Our analysis shows that these frameworks share a foundational core characterized by mathematical contradictions and procedural ambi- guities. Specifically, the published text introduces unindexed circular dependencies, dimensional conflations between scalars and vectors, a logically inverted sampling operator, initialization biases, and a monotonically increasing scale factor that causes immediate floating-point overflow under standard execution. Method- ologically, the empirical validation deviates from standard IEEE CEC protocols by evaluating the algorithm on simplified, unshifted base functions while subjecting established baseline optimizers to a limited function evaluation budget. Compounded by the absence of replication code, these discrepancies make independent reproduction unresolvable. Additionally, the reported results, achieving absolute zero variance and perfec- tion on complex, high-dimensional landscapes within a fraction of standard evaluation budgets, are unusual and not discussed in much detail. This analysis indicates that the current formulations do not align with the rigorous validation standards established by the global swarm and evolutionary optimization commu- nities. Furthermore, the manuscript relies entirely on classical, two-dimensional trigonometric operations while asserting unsupported claims of physical quantum advantage. This mischaracterization transforms a standard quantum-inspired conceptual metaphor into a misleading claim of literal quantum utility, creating a fundamental contradiction with the algorithm’s actual classical mechanics.

Swarm and evolutionary algorithms are usually analyzed as complete procedural systems in which nonlinear selection, replacement, and adaptation obscure simpler structure within candidate generation. This paper introduces an operator–selection factorization that separates objective-independent variation from boundary repair and fitness-dependent selection, and uses it to study the proposal geometry of the Self-Organizing Migrating Algorithm (SOMA) and Differential Evolution (DE). The canonical SOMA proposal is shown to be affine in the search space and exactly linear in an augmented migrant–leader state. In leader-relative coordinates, the resulting operator provides a direct interpretation of interpolation, projection, overshooting, and coordinate masking. Under Bernoulli perturbation masks, we derive closed-form expressions for the proposal mean, covariance, expected squared step length, expected squared distance from the leader, active dimensionality, and coordinate coverage. For canonical DE/rand/1/bin, we derive the finite-population moments of differential mutation and characterize the additional covariance and coordinate dependence induced by forced-coordinate binomial crossover. Exact enumeration and Monte Carlo experiments verify the analytical identities and quantify the effects of mask conditioning, boundary repair, and fitness-based selection. The analysis further motivates geometry-controlled and rotation-aware SOMA variants, together with an adaptive population-reducing extension of iSOMA. Experiments on the complete noiseless BBOB benchmark show that these operator-guided variants substantially improve upon canonical SOMA and are competitive with established DE methods in several dimension–budget regimes. The results demonstrate how proposal-level operator analysis can support both the interpretation and design of population-based optimizers.

This paper provides a structural and methodological audit of several quantum-inspired Differential Evolution variants, focusing primarily on the recently proposed PSEQADE algorithm. Our analysis shows that these frameworks share a foundational core characterized by mathematical contradictions and procedural ambiguities. Specifically, the published text introduces unindexed circular dependencies, dimensional conflations between scalars and vectors, a logically inverted sampling operator, initialization biases, and a monotonically increasing scale factor that causes immediate floating-point overflow under standard execution. Methodologically, the empirical validation deviates from standard IEEE CEC protocols by evaluating the algorithm on simplified, unshifted base functions while subjecting established baseline optimizers to a limited function evaluation budget. Compounded by the absence of replication code, these discrepancies make independent reproduction unresolvable. Additionally, the reported results, achieving absolute zero variance and perfection on complex, high-dimensional landscapes within a fraction of standard evaluation budgets, are unusual and not discussed in much detail. This analysis indicates that the current formulations do not align with the rigorous validation standards established by the global swarm and evolutionary optimization communities. Furthermore, the manuscript relies entirely on classical, two-dimensional trigonometric operations while asserting unsupported claims of physical quantum advantage. This mischaracterization transforms a standard quantum-inspired conceptual metaphor into a misleading claim of literal quantum utility, creating a fundamental contradiction with the algorithm's actual classical mechanics.

This study evaluates colorectal risk factors and compares classical models against Quantum Neural Networks (QNNs) for anastomotic leak prediction. Analyzing clinical data with 14% leak prevalence, we tested ZZFeatureMap encodings with RealAmplitudes and EfficientSU2 ansatze under simulated noise. F_β-optimized quantum configurations yielded significantly higher sensitivity (83.3%) than classical baselines (66.7%). This demonstrates that quantum feature spaces better prioritize minority class identification, which is critical for low-prevalence clinical risk prediction. Our work explores various optimizers under noisy conditions, highlighting key trade-offs and future directions for hardware deployment.

Anastomotic leak is a life-threatening complication following colorectal surgery. This study benchmarks Quantum Neural Networks (QNNs) against hyperparameter-tuned classical models (logistic regression, multi-layer perceptrons, boosting algorithms) for anastomotic leak prediction. Using a 200-patient clinical dataset strictly bounded by a priori medical constraints, we simulated QNNs with ZZFeatureMap encoding and EfficientSU2/RealAmplitudes ansatze under realistic hardware noise. To ensure statistical reliability, performance metrics were averaged across 10 independent optimization runs. The EfficientSU2-BFGS configuration achieved the highest mean AUC of [Formula: see text], while RealAmplitudes with CMA-ES maximized Average Precision ([Formula: see text]). Crucially, at a fixed, clinically necessary sensitivity of [Formula: see text], specific QNN configurations achieved significantly higher specificity (up to [Formula: see text]) and Negative Predictive Value (up to [Formula: see text]) compared to classical models (maximum [Formula: see text] and [Formula: see text], respectively), effectively minimizing false positives. However, classical models maintained superior probability calibration for continuous risk stratification. We conclude that QNNs offer robust discriminative performance for clinical screening, warranting further validation on larger, independent cohorts.

This paper provides a historical analysis of the IEEE CEC Single Objective Optimization competition results (2010–2024). We analyze how benchmark functions shaped winning algorithms, identifying the 2014 introduction of dense rotation matrices as a key performance filter. This design choice introduced parameter non-separability, reduced effectiveness of coordinate-dependent methods (PSO, GA), and established the dominance of Differential Evolution variants capable of preserving the rotational invariance of their difference vectors, specifically L-SHADE. Post-2020 analysis reveals a shift toward high complexity hybrid optimizers that combine different mechanisms (e.g., Eigenvector Crossover, Societal Sharing, Reinforcement Learning) to maximize ranking stability. We conclude by identifying structural similarities between these modern benchmarks and Variational Quantum Algorithm landscapes, suggesting that evolved CEC solvers possess the specific adaptive capabilities required for quantum control.

The enormous size of data produced by astrophysical and cosmological simulations presents significant challenges for high-quality analysis and visualization. The datasets are often too large to handle efficiently, and VFX (visual effects) software does not natively support scientific formats or extremely large simulations. To address these limitations, we propose a method that reduces the data size while reconstructing the representation into a format suitable for VFX workflows to enable high-quality, cinematic visualizations. Our approach constructs a multi-resolution grid guided by fractal dimension analysis, enabling efficient data reduction while preserving the visually and structurally important features. The proposed method can be efficiently adapted for parallel computation across multiple processes, making it well-suited for large datasets. In this work, we present a sequential version of the method. The resulting multi-resolution representation is directly integrated into our modified path-tracing rendering pipeline, allowing the compressed structure to be used during volumetric rendering. The results demonstrate that the method is capable of reducing memory demands significantly while preserving the essential structures. The method achieves compression ratios as low as 5 % of the original data size while maintaining high visual and numerical fidelity, with PSNR (peak signal-to-noise ratio) above 80 dB and SSIM (structural similarity index) exceeding 0.99. Rendering experiments show that the compact multi-resolution representation substantially reduces memory consumption compared to uniform grids, while maintaining comparable rendering performance with also slight improvements in frame rate.

Ensuring efficient and safe trajectory planning for UAVs in complex and dynamic environments is a critical challenge, especially for UAVs that are increasingly deployed in applications like environmental monitoring, disaster management, and surveillance. The primary complications in the safe control of UAVs include real-time obstacle avoidance, adaptation to unpredictable environmental changes, and coordination among multiple UAVs to prevent collisions. This paper addresses these challenges by proposing a novel approach for UAV trajectory planning that integrates obstacle avoidance and target acquisition. We introduce a new cost function designed to minimize the distance to the target while maximizing the distance from obstacles, effectively balancing these competing objectives to ensure safety and efficiency. To optimize this cost function, we employ the self-organizing migrating algorithm, a swarm intelligence algorithm inspired by the cooperative and competitive behaviors observed in natural organisms. Our method enables UAVs to autonomously generate safe and efficient paths in real-time, adapt to dynamic changes, and scale to large swarms without relying on centralized control. Simulation results across three scenarios-including a complex environment with ten UAVs and multiple obstacles-demonstrate the effectiveness of our approach. The UAVs successfully reach their targets while avoiding collisions, confirming the reliability and robustness of the proposed method. This work contributes to advancing autonomous UAV operations by providing a scalable and adaptable solution for trajectory planning in challenging environments.

This work addresses the challenge of enabling practitioners without quantum expertise to transition from classical to hybrid quantum-classical machine learning workflows. We propose a three-stage framework: starting with a classical self-training model, then introducing a minimal hybrid quantum variant, and finally applying diagnostic feedback via QMetric to refine the hybrid architecture. In experiments on the Iris dataset, the refined hybrid model improved accuracy from 0.31 in the classical approach to 0.87 in the quantum approach. These results suggest that even modest quantum components, when guided by proper diagnostics, can enhance class separation and representation capacity in hybrid learning, offering a practical pathway for classical machine learning practitioners to leverage quantum-enhanced methods.

The bio-inspired metaheuristics have evolved as effective alternatives to handle portfolio optimization problems, which remain a key instrument in investment analysis. This study proposes the adoption of the Slime Mould Algorithm (SMA) to address the portfolio optimization problem with the objective of maximizing the Sharpe ratio under Lintner’s framework of short selling. The SMA-based portfolio optimization is performed on two distinct datasets from the NASDAQ and the NSE, with optimal parameter settings fine-tuned using Sobol’s sensitivity analysis and random and grid search methods. Rigorous benchmarking experimentation against three widely used metaheuristics, the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Differential Evolution (DE), over multiple independent runs, demonstrates the performance superiority of SMA over others. Statistical tests like the KruskalWallis H test and the post-hoc Dunn’s test also confirm a significant performance difference among the four algorithms. The study concludes the reliability and effectiveness of SMA in Sharpe-based portfolio optimization.

In recent years, there has been increased interest in using advanced technologies such as artificial intelligence, particularly in public safety and rescue operations. This paper focuses on an innovative approach to monitoring and analysing the movement of firefighters during rescue operations using artificial intelligence. In our research, we implemented a system that uses data obtained from sensors placed on the protective suits of firefighters. This data is analysed using deep-learning neural networks after advanced data preprocessing. The goal is to provide a more accurate real-time interpretation of firefighter movement, improving rescue teams’ coordination and increasing firefighters’ safety in their work. This paper presents the results of initial experiments that demonstrate the effectiveness of the proposed system in different rescue operation scenarios. At the end of the paper, we also discuss possible challenges and directions for further research in this area. Our work represents an important step towards integrating artificial intelligence into critical public safety operations. It offers new opportunities for improving rescue operations and protecting lives.

This paper presents the application of the improved Self-Organizing Migrating Algorithm (iSOMA) to the synthesis and optimization of quantum circuits. We develop a comprehensive method to evolve candidate quantum circuits with minimal cost by integrating iSOMA with circuit evaluators in Qiskit. Experimental evaluation across 100 independent runs demonstrates a 90% success rate in synthesizing Toffoli gates, with an 80% circuit uniqueness rate indicating diverse solution exploration. The algorithm achieves a median cost of 0.000 and determinism = 8/8 for successful runs, confirming its effectiveness for quantum circuit synthesis. This research is important for future 6G networks and beyond, as quantum computing has the potential to be more efficient, especially in the physical layer of the Radio Access Network (RAN), where quantum-supported optimization mechanisms are able to process a large number of tasks faster. The proposed synthesis of Toffoli gates controlled by iSOMA technology contributes to quantum computing by supporting the efficient design of quantum circuits, which is a prerequisite for the deployment of quantum-native functions, such as the Quantum Fourier Transform, in future communications and wireless infrastructures.

Cellular automata are discrete, rule-based systems that exhibit complex behavior emerging from simple local interactions. This paper explores the foundational principles of cellular automata and their applications in unconventional computing paradigms, including the modeling of natural phenomena, emergent computation, and robotics. We highlight how cellular automata systems provide a versatile framework for solving computational problems beyond traditional architectures, leveraging their inherent parallelism, adaptability, and scalability. Furthermore, we examine their potential in robotics for decentralized control and swarm behavior, emphasizing their role in advancing unconventional computation.

This study investigates the application of quantum neural networks (QNNs) for propensity score estimation to address selection bias in comparing survival outcomes between laparoscopic and open surgical techniques in a cohort of 1177 colorectal carcinoma patients treated at University Hospital Ostrava (2001-2009). Using a dataset with 77 variables, including patient demographics and tumor characteristics, we developed QNN-based propensity score models focusing on four key covariates (Age, Sex, Stage, BMI). The QNN architecture employed a linear ZFeatureMap for data encoding, a SummedPaulis operator for predictions, and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for robust, gradient-free optimization in noisy quantum environments. Variance regularization was integrated to mitigate quantum measurement noise, with simulations conducted under exact, sampling (1024 shots), and noisy hardware (FakeManhattanV2) conditions. QNNs, particularly with simulated hardware noise, outperformed classical logistic regression and gradient boosted machines in small samples (AUC up to 0.750 for n=100), with noise modeling enhancing predictive stability. Propensity score matching and weighting, optimized via genetic matching and matching weights, achieved covariate balance with standardized mean differences of 0.0849 and 0.0869, respectively. Survival analyses using Kaplan-Meier estimation, Cox proportional hazards, and Aalen additive regression revealed no significant survival differences post-adjustment (p-values 0.287-0.851), indicating confounding bias in unadjusted outcomes. These results highlight QNNs' potential, enhanced by CMA-ES and noise-aware strategies, to improve causal inference in biomedical research, particularly for small-sample, high-dimensional datasets.

Hyperspectral imaging offers a powerful means of ground cover analysis. Absence of ground truth data makes correct number of clusters detection a challenging task in real-time environment. The rich spectral content also makes processing difficult and autonomous cluster detection methods an essential research paradigm. In this work, a qutrit based Grey Wolf Optimizer (GWO) is introduced to handle highdimensional hyperspectral scenes. Classical GWO is a robust swarm intelligence tool, yet it often struggles with stagnant diversity and premature convergence. To overcome these limitations, qutrit Hadamard gate is used to enhance population diversity. The premature convergence problem is addressed using qutrit NOT gates. Rigorous comparative analysis is done with classical GWO, its modified variant, and qubit variant of GWO. F-score is used for cluster quality evaluation and the Adjusted Rand Index is used as the objective function to determine optimal number of clusters. Results demonstrate that the qutrit integrated framework consistently outperforms existing methods across various benchmarks.

Irregular dynamics (especially chaotic) is often undesirable in economics because it presents challenges for predicting and controlling the behavior of economic agents. In this paper, we used an overlapping generations (OLG) model with a control function in the form of government spending as an example, to demonstrate an effective approach to forecasting and regulating chaotic dynamics based on a combination of classical control methods and artificial intelligence algorithms. We showed that in the absence of control variables, both regular and irregular (including chaotic) behavior could be observed in the model. In the case of irregular dynamics, a small control action introduced in the model allows modifying the behavior of economic agents and switching their dynamics from irregular to regular mode. We used control synthesis by the Pyragas method to solve the problem of regularizing the irregular behavior and stabilizing unstable periodic orbits (UPOs) embedded in the chaotic attractor of the model. To maximize the basin of attraction of stabilized UPOs, we used several types of evolutionary algorithms (EAs). We compared the results obtained by applying these EAs in numerical experiments and verified the outcomes by numerical simulation. The proposed approach allows us to improve the forecasting of dynamics in the OLG model and make agents' expectations more predictable.