Nowadays, we are at the dawn of a new age, the quantum era. Quantum computing is no longer a dream; it is a reality that needs to be adopted. But this new technology is taking its first steps, so we still do not have models, standards, or methods to help us in the creation of new systems and the migration of current ones. Given the current state of quantum computing, we need to go back to the path software engineering took in the last century to achieve the new golden age for quantum software engineering.

Quantum computing is the latest revolution in computing and will probably come to be seen as an advance as important as the steam engine or the information society. In the last few decades, our understanding of quantum computers has expanded and multiple efforts have been made to create languages, libraries, tools, and environments to facilitate their programming. Nonetheless, quantum computers are complex systems at the bottom of a stack of layers that programmers need to understand. Hence, efforts towards creating quantum programming languages and computing environments that can abstract low-level technology details have become crucial steps to achieve a useful quantum computing technology. However, most of these environments still lack many of the features that would be desirable, such as those outlined in The Talavera Manifesto for Quantum Software Engineering and Programming. For advancing quantum computing, we will need to develop quantum software engineering techniques and tools to ensure the feasibility of this new type of quantum software. To contribute to this goal, this paper provides a review of the main quantum software components and platforms. We also propose a set of quality requirements for the development of quantum software platforms and the conduct of their quality assessment.

Data is currently one of the most important assets for companies in every field. The continuous growth in the importance and volume of data has created a new problem: it cannot be handled by traditional analysis techniques. This problem was, therefore, solved through the creation of a new paradigm: Big Data. However, Big Data originated new issues related not only to the volume or the variety of the data, but also to data security and privacy. In order to obtain a full perspective of the problem, we decided to carry out an investigation with the objective of highlighting the main issues regarding Big Data security, and also the solutions proposed by the scientific community to solve them. In this paper, we explain the results obtained after applying a systematic mapping study to security in the Big Data ecosystem. It is almost impossible to carry out detailed research into the entire topic of security, and the outcome of this research is, therefore, a big picture of the main problems related to security in a Big Data system, along with the principal solutions to them proposed by the research community.