Quantum computing Laboratory on quantum computing

CB6. To possess and understand concepts and ideas that provide a basis or opportunity to be original in the development and/or application of ideas, often in a research context CB9. That students know how to communicate their conclusions and the ultimate knowledge and reasons that support them to specialized and non-specialized audiences in a clear and unambiguous manner CB10. That students possess the learning skills that will enable them to continue studying in a manner that will be largely self-directed or autonomous CG2. Knowledge of scientific and technical subjects that enable them to learn new methods and technologies, as well as to be highly versatile in adapting to new situations CG4. Ability to solve scientific and technological problems that may arise within the framework of the applications of quantum technologies in various fields of physics and engineering CG5. Ability to use the theoretical and practical knowledge acquired in the CG6. Ability to develop new products and services based on the use and exploitation of new quantum technologies CG7. Ability and knowledge to enable the enrolment in specialized studies at the PhD level, either in related fields of physics or in the various branches of engineering CE1. General knowledge of quantum sciences and technologies and their impact on the future society from the points of view of research, business and the labor market CE2. Ability to apply the concepts of quantum mechanics and its postulates to various quantum problems and systems of technological interest CE3. Ability to use the main formalisms and more common mathematical tools from quantum mechanics CE6. Knowlege of the principles of quantum computing and its basic elements: qubits, gates and circuits, as well as knowledge and ability to handle various quantum algorithms CE7. Ability to generate codes that implement simple quantum algorithms, to identify the kind of problems that can be advantageously solved with them and to identify the potential physical implementations of a quantum computer

1. Introduction to Financial and Industrial Problems -Optimization Problems -Simulation Problems -Prediction Problems -Classification Problems 2. Review of Classical and Quantum Optimizers -Classical Optimizers -Optimization on Quantum Annealers -Optimization on Gate-based Computers 3.Techniques for Simulation on Quantum Computers -Hamiltonian Simulation Techniques -Monte Carlo Techniques -Solving Systems of Equations 4. Quantum Machine Learning Techniques -Prediction and Classification Models -Feature Extraction and Variable Selection -Variational Circuits 5.Use Cases -Investment Portfolio Optimization -Time Series Prediction -Estimation of Financial Derivatives Prices -Anomaly Detection

LEARNING ACTIVITIES AF1. Theorical class AF2. Practical classes AF4. Team work AF5. Individual student work AF6. Partial and final exams METHODOLOGY MD1. Presentations in class by the teacher with the support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the learning of the students. MD2. Critical reading of texts recommended by the professor of the subject: articles, reports, manuals and/or academic articles, either for later discussion in class, or to broaden and consolidate knowledge of the subject MD3. Resolution of practical cases, problems, etc... raised by the teacher individually or in a group MD4. Presentation and discussion in class, under the teacher's moderation, of topics related to the content of the subject, as well as practical cases MD5. Preparation of work and reports individually or in groups