Abstract
To address the challenge of detecting stealthy port scans in high-speed networks, this paper introduces p4SD, a lightweight anomaly detection system that identifies reconnaissance activities directly within programmable data planes. Leveraging the P4 language, p4SD uses a cyclic fingerprint buffer and frequency analysis to monitor for anomalous traffic without relying on attack signatures. The system is designed to detect both fast and slow port scans, as its method of measuring relative changes in distinct fingerprints between cycles effectively identifies both the rapid spikes from fast scans and the gradual increases from slow scans. The proof-of-concept demonstrates resource efficiency, achieving throughput close to the hardware's theoretical limits, detecting scan activity in near real-time, and enabling timely responses to potential threats. With over 99% detection accuracy for slow scans, these findings establish p4SD as a practical and scalable solution for real-time, in-network threat detection in modern SDN environments. © 2025 University of Split, FESB.

Abstract
We present BAE, a problem-tailored and noise-aware Bayesian algorithm for quantum amplitude estimation. In a fault tolerant scenario, BAE is capable of saturating the Heisenberg limit; if device noise is present, BAE can dynamically characterize it and self-adapt. We further propose aBAE, an annealed variant of BAE drawing on methods from statistical inference, to enhance robustness. Our proposals are parallelizable in both quantum and classical components, offer tools for fast noise model assessment, and can leverage preexisting information. Additionally, they accommodate experimental limitations and preferred cost trade-offs. We propose a robust benchmark for amplitude estimation algorithms and use it to test BAE against other approaches, demonstrating its competitive performance in both noisy and noiseless scenarios. In both cases, it achieves lower error than any other algorithm as a function of the cost. In the presence of decoherence, it is capable of learning when other algorithms fail. © 2025 Elsevier B.V., All rights reserved.

Abstract
Adaptive variational quantum algorithms arguably offer the best prospects for quantum advantage in the Noisy Intermediate-Scale Quantum era. Since the inception of the first such algorithm, the Adaptive Derivative-Assembled Problem-Tailored Variational Quantum Eigensolver (ADAPT-VQE), many improvements have appeared in the literature. We combine the key improvements along with a novel operator pool-which we term Coupled Exchange Operator (CEO) pool-to assess the cost of running state-of-the-art ADAPT-VQE on hardware in terms of measurement counts and circuit depth. We show a dramatic reduction of these quantum computational resources compared to the early versions of the algorithm: CNOT count, CNOT depth and measurement costs are reduced by up to 88%, 96% and 99.6%, respectively, for molecules represented by 12 to 14 qubits (LiH, H6 and BeH2). We also find that our state-of-the-art CEO-ADAPT-VQE outperforms the Unitary Coupled Cluster Singles and Doubles ansatz, the most widely used static VQE ansatz, in all relevant metrics, and offers a five order of magnitude decrease in measurement costs as compared to other static ans & auml;tze with competitive CNOT counts.

Abstract
Rendering on conventional computers is capable of generating realistic imagery, but the computational complexity of these light transport algorithms is a limiting factor of image synthesis. Quantum computers have the potential to significantly improve rendering performance through reducing the underlying complexity of the algorithms behind light transport. This article investigates hybrid quantum-classical algorithms for ray tracing, a core component of most rendering techniques. Through a practical implementation of quantum ray tracing in a 3D environment, we show quantum approaches provide a quadratic improvement in query complexity compared to the equivalent classical approach. Based on domain specific knowledge, we then propose algorithms to significantly reduce the computation required for quantum ray tracing through exploiting image space coherence and a principled termination criteria for quantum searching. We show results obtained using a simulator for both Whitted style ray tracing, and for accelerating ray tracing operations when performing classical Monte Carlo integration for area lights and indirect illumination.

Abstract
Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with Adaptive Derivative-Assembled Problem-Tailored variational quantum eigensolver (ADAPT-VQE), an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to optimal parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the approximate Hessian in the state of the optimizer to be consistently closer to the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical implementation of a quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.

Abstract
Context: Software testing is a critical aspect of the software development lifecycle, yet it remains underrepresented in academic curricula. Despite advances in pedagogical practices and increased attention from the academic community, challenges persist in effectively teaching software testing. Understanding these challenges from the teachers’ perspective is crucial to aligning education with industry needs. Objective: To analyze the characteristics, practices, tools, and challenges of software testing courses in higher education, from the perspective of educators, and to assess the integration of recent pedagogical approaches in software testing education. Method: A structured survey consisting of 52 questions was distributed to 143 software testing educators across Western European universities, resulting in 49 valid responses. The survey explored topics taught, course organization, teaching practices, tools and materials used, gamification approaches, and teacher satisfaction. Results: The survey revealed significant variability in course content, structure, and teaching methods. Most dedicated software testing courses are offered at the master’s level and are elective, whereas testing is introduced earlier in less specialized (NST) courses. There is low adoption of formal guidelines (e.g., ACM, SWEBOK), limited integration of non-functional testing types, and a high diversity in textbooks and tools used. While modern practices like Test-Driven Development and automated assessment are increasingly adopted, gamification and active learning approaches remain underutilized. Teachers expressed a need for improved and more consistent teaching materials. Conclusion: The study highlights a mismatch between academic practices and industry expectations in software testing education. Greater integration of standardized curricula, broader adoption of modern teaching tools, and increased support for teachers through high-quality, adaptable teaching materials are needed to enhance the effectiveness of software testing education.