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
Software testing plays a fundamental role in software engineering, involving the systematic evaluation of software to identify defects, errors, and vulnerabilities from the early stages of the development process. Education in software testing is essential for students and professionals, as it promotes quality and favours the construction of reliable software solutions. However, motivating students to learn software testing may be a challenge. To overcome this, educators may incorporate some strategies into the teaching and learning process, such as real-world examples, interactive learning, and gamification. Gamification aims to make learning software testing more engaging for students by creating a more enjoyable experience. One approach that has proven effective is to use serious games. This paper presents a novel serious game to teach white-box testing test case design techniques, named GAMFLEW (GAMe For LEarning White-box testing). It describes the design, game mechanics, and its implementation. It also presents a preliminary evaluation experiment with students to assess the usability, learnability, and perceived problems, among other aspects. The results obtained are encouraging.

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We focus on the integration of Formal Methods as mandatory theme in any Computer Science University curriculum. In particular, when considering the ACM Curriculum for Computer Science, the inclusion of Formal Methods as a mandatory Knowledge Area needs arguing for why and how does every computer science graduate benefit from such knowledge. We do not agree with the sentence While there is a belief that formal methods are important and they are growing in importance, we cannot state that every computer science graduate will need to use formal methods in their career. We argue that formal methods are and have to be an integral part of every computer science curriculum. Just as not all graduates will need to know how to work with databases either, it is still important for students to have a basic understanding of how data is stored and managed efficiently. The same way, students have to understand why and how formal methods work, what their formal background is, and how they are justified. No engineer should be ignorant of the foundations of their subject and the formal methods based on these. In this article, we aim at highlighting why every computer scientist needs to be familiar with formal methods. We argue that education in formal methods plays a key role by shaping students' programming mindset, fostering an appreciation for underlying principles, and encouraging the practice of thoughtful program

Abstract
In healthcare, diagnoses usually rely on physician expertise. However, complex cases may benefit from consulting similar past clinical reports cases. In this paper, we present MedLink (http://medlink.inesctec.pt), a tool that given a free-text medical report, retrieves and ranks relevant clinical case reports published in health conferences and journals, aiming to support clinical decision-making, particularly in challenging or complex diagnoses. To this regard, we trained two BERT models on the sentence similarity task: a bi-encoder for retrieval and a cross-encoder for reranking. To evaluate our approach, we used 10 medical reports and asked a physician to rank the top 10 most relevant published case reports for each one. Our results show that MedLink’s ranking model achieved NDCG@10 of 0.747. Our demo also includes the visualization of clinical entities (using a NER model) and the production of a textual explanation (using a LLM) to ease comparison and contrasting between reports. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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