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This report is the fourth deliverable of Task 6.2 – Quantum Rule-Based Systems (QRBS) for breast cancer detection of the NEASQC project. The document presents the work carried out so far, and is complementary to the other deliverables of this task. D6.11 (M32) will include a preliminary version of the RBS and QRBS software along with the specification of the IDC application that will be developed. The report begins with an introduction of the preliminary version of the QRBS software, presenting how the project is structured, providing some use examples of common operations users will make with the library, and a brief com-mentary on the library’s documentation which is presented in an appendix. Following that, we present the specification for the Invasive Ductal Carcinoma (IDC) application, based on previous work on quantum computing techniques and applying them to the clinical problem specifically. We continue by extending the work on previous deliverables regarding traceability and testing of the QRBS software, which is a critical part on making sure that the developed library provides valid and verified functionalities. To close the report, the conclusions obtained during the development of the work carried out are presented and some ideas for future work to be included in upcoming deliverables are shown., {"references":["Amin, M. B., Greene, F. L., Edge, S. B., Compton, C. C., Gershenwald, J. E., Brookland, R. K., Meyer, L., Gress, D. M., Byrd, D. R., & Winchester, D. P. (2017). The eighth edition ajcc cancer staging manual: Continuing to build a bridge from a population-based to a more \"personalized\" approach to cancer staging. CA: A Cancer Journal for Clinicians, 67(2), 93–99. https://doi.org/10.3322/caac.21388","Giuliano, A. E., Edge, S. B., & Hortobagyi, G. N. (2018). Eighth edition of the ajcc cancer staging manual: Breast cancer. Annals of Surgical Oncology, 25(7), 1783–1785. https://doi.org/10.1245/s10434-018-6486-6","Kruchten, P. (2004). The rational unified process: An introduction. Addison-Wesley.","Ledley, R. S., & Lusted, L. B. (1959). Reasoning foundations of medical diagnosis. Science, 130(3366), 9–21. https: //doi.org/10.1126/science.130.3366.9","Moret-Bonillo, V., Gomez Tato, A., Magaz Romero, S., Mosqueira-Rey, E., & Alvarez-Estevez, D. (2022). D6.9: Qrbs software specifications. https://doi.org/10.5281/zenodo.7299193","Moret-Bonillo, V., Magaz-Romero, S., & Mosqueira-Rey, E. (2022). Quantum computing for dealing with inaccurate knowledge related to the certainty factors model. Mathematics, 10(2). https://doi.org/10.3390/math10020189","Moret-Bonillo, V., Mosqueira-Rey, E., & Magaz-Romero, S. (2021). D6.5 Quantum Rule-Based System (QRBS) Requirement Analysis. https://doi.org/10.5281/zenodo.5949157","Moret-Bonillo, V., Mosqueira-Rey, E., Magaz-Romero, S., & Alvarez-Estevez, D. (2023). Hybrid classic-quantum computing for staging of invasive ductal carcinoma of breast. arXiv preprint arXiv:2303.10142. https://arxiv. org/abs/2303.10142","Moret-Bonillo, V., Mosqueira-Rey, E., Magaz-Romero, S., & G´omez-Tato, A. (2021). Quantum rule-based systems (qrbs) models, architecture and formal specification (D6. 2). https://www.neasqc.eu/wp- content/uploads/ 2021/05/NEASQC D6.2 QRBS-Models-Architecture-and-Formal-Specification-V1.5-Final.pdf","Waterman, D. A. (1985). A guide to expert systems. Addison-Wesley Longman Publishing Co., Inc."]}

Despite the great current relevance of Artificial Intelligence, and the extraordinary innovations that this discipline has brought to many fields -among which, without a doubt, medicine is found-, experts in medical applications of Artificial Intelligence are looking for new alternatives to solve problems for which current Artificial Intelligence programs do not provide with optimal solutions. For this, one promising option could be the use of the concepts and ideas of Quantum Mechanics, for the construction of quantum-based Artificial Intelligence systems. From a hybrid classical-quantum perspective, this article deals with the application of quantum computing techniques for the staging of Invasive Ductal Carcinoma of the breast. It includes: (1) a general explanation of a classical, and well-established, approach for medical reasoning, (2) a description of the clinical problem, (3) a conceptual model for staging invasive ductal carcinoma, (4) some basic notions about Quantum Rule-Based Systems, (5) a step-by-step explanation of the proposed approach for quantum staging of the invasive ductal carcinoma, and (6) the results obtained after running the quantum system on a significant number of use cases. A detailed discussion is also provided at the end of this paper., Submitted to Information (ISSN 2078-2489)

This report is the third deliverable of Task 6.2 - Quantum Rule-Based Systems (QRBS) for breast cancer detection of the NEASQC project. The document presents the work carried out so far, and is complementary to the other deliverables of this task. The report begins with an introduction into the software specification process, presenting the necessary definitions for the reader to comprehend the work carried out. Following those concepts come the several specifications obtained, including both static and dynamic design, as well as the implementations specifications for this use case. Following that, focus is on the testing phase, in order to prove that the specification obtained is coherent regards both itself and the previous work. To close the report, the conclusions obtained during the development of the work carried out are presented to enhance the following work., {"references":["Ambler, S. (2002).Agile modeling: Effective practices for extreme programming and the unified process. John Wiley & Sons.","Bourque, P., & Fairley, R. E. (2014).Guide to the software engineering body of knowledge (SWEBOK©). IEEE Computer Society Press.","Kruchten, P. (2004).The rational unified process: An introduction. Addison-Wesley.","Pressman, R. S. (2005).Software engineering: A practitioner's approach. Palgrave macmillan"]}

This report is the second deliverable of Task 6.2-Quantum Rule-Based Systems (QRBS) for breast cancer detection of the NEASQC project.The document presents the work carried out so far, and is complementary to the other deliverables of this task. The report begins with an introduction into the requirement analysis process, explaining the necessary concepts to understand the development of the work. Along with said concepts,the applied procedure for the analysis of requirements used in this work is also presented, to put the reader in context and to justify the following sections. Once the necessary concepts have been presented, the report illustrates the different phases of the work carried out, starting with the needs and features (vision).This phase serves to detect several factors that are crucial to the requirement analysis, such as the actors related to the system or the needs that the system must cover. The document continues with next step of the process, related to the use cases, which are decompositions of the previous needs according to their functionality and logical structuring. Each use case is studied, explained and detailed in depth. This decomposition allows for a more robust and traceable development across the next phases of the work. Finally, following the previous decomposition, test cases are defined. This test cases conform a test plan that will help on later stages of the development,when it arises the need to check whether the system meets the requirements specified., {"references":["Adolph, S., Bramble, P., Cockburn, A., & Pols, A. (2003). Patterns for Effective Use Cases.Pearson Education, Inc.","Ambler, S. (2002). Agile Modeling: Effective Practices for Extreme Programming and the Unified Process.John Wiley & Sons.","Cockburn, A. (2001). Writing Effective Use Cases.Addison-Wesley.","Hasan, K., Ramsay, B., & Moyes, I. (1994).Object oriented expert systems for real-time power system alarm processing: Part I. Selection of a toolkit. Electric Power Systems Research, 30(1), 69-75.","ISO/IEC/IEEE. (2010). ISO/IEC/IEEE International Standard -Systems and software engineering --Vocabulary. ISO/IEC/IEEE 24765:2010(E), 1-41","ISO/IEC/IEEE. (2017). ISO/IEC/IEEE International Standard -Systems and software engineering -Vocabulary.ISO/IEC/IEEE. doi:10.1109/IEEESTD.2017.8016712","Kotonya, G., & Sommerville, I. (1998). Requirements Engineering: Processes and Techniques.John Wiley & Sons.","Krause, P., & Clark, D. (2012). Representing uncertain knowledge: an artificial intelligence approach.Springer Science & Business Media.","Kruchten, P. (2004). The Rational Unified Process: An Introduction(3rd ed.). Addison-Wesley Professional.","Larman, C. (2005). Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development(3rd ed.). Pearson Education, Inc.","Martin, R. C. (2000). Design principles and design patterns. Object Mentor, 1(34), 597.","Perot, F. (1988). A knowledge acquisition assistant for the expert system shell Nexpert-Object.","Prusak, L. (2001). Where did knowledge management come from? IBM Systems Journal, 40(4), 1002-1007.","Rumbaugh, J.,Jacobson, I., & Booch, G. (2004). Unified Modeling Language Reference Manual, The (2nd Edition).Pearson Higher Education.","Spence, I., & Probasco, L. (2001). Traceability Strategies for Managing Requirements with Use Cases. Rational Software White Paper. Recuperado el 13 de 12 de 2021, de https://www.semanticscholar.org/paper/Traceability-Strategies-for-Managing-Requirements-Spence-Probasco/4ed79d38b02b070b165ab91267a7390b89903fc6","Rehman, T., Khan, M. N., & Riaz, N. (2013). Analysis of requirement engineering processes, tools/techniques and methodologies. International Journal of Information Technology and Computer Science (IJITCS), 5(3), 40."]}

This report is the first deliverable of UDC-CESGA, related to task 6.2 of Work Package 6 of the NEASQC project, UC6. The document incorporates information on the approach to the work carried out so far, from the project start date to the deadline established for this first deliverable. The report includes a brief description of invasive ductal carcinoma of the breast (IDC), the methodology followed for the modeling of a rule-based system for the diagnosis and treatment of IDC, a preliminary analysis to evaluate the suitability of quantum computing in this domain, a proposal about the quantum approximation that we want to use, and that we will later have to evaluate, and the analysis about the formal requirements of the application that we intend to carry out. We also include a quantum proposal on the uncertainty associated with reasoning in medicine. A brief summary of the IDC is necessary to place the use case in the context of the project. The description will range from the initial symptoms that allow the clinician to consider the possibility of IDC, the diagnostic process, the degree of severity of the IDC, and the possible associated treatments. The methodological description of the knowledge engineering used is necessary to understand the architecture of a classical rule-based system, and to be able to formalize the problem in terms of declarative knowledge, procedural knowledge and inferential circuits. Next, a qualitative analysis of the problem in terms of quantum logical operators is presented to illustrate the possibility of converting a conventional rule-based system into a quantum rule-based system. Finally, the formal requirements of the quantum rule-based system will be mentioned. Also, we will pay special attention to the imprecision of the information and the uncertainty associated with clinical practice., {"references":["American Cancer Society. (2019). Breast Cancer Facts & Figures 2019-2020. Atlanta: American Cancer Society, Inc.","Atos. (2016-2020). qat.pylinalg: Python Linear-algebra simulator. Obtenido de https://myqlm.github.io/myqlm_specific/qat-pylinalg.html","Kendal, S. L., & Creen, M. (2007). An Introduction to Knowledge Engineering. Springer.","Ledley, R. S., & Lusted, L. B. (1959). Reasoning Foundations of Medial Diagnosis. Science, 9-21.","Lindley, D. V. (2014). Understanding Uncertainty, Revised Edition. John Wiley & Sons Ltd.","Moret-Bonillo, V. (2000). Fundamentos de la Inteligencia Artificial. Universidade da Coruña.","Moret-Bonillo, V. (2018). Emerging technologies in artificial intelligence: quantum rule-based systems. Progress in Artificial Intelligence 7, 155-166.","Nalepa, G. J. (2008). Methodologies and Technologies for Rule-Based Systems Design and Implementation. Towards Hybrid Knowledge Engineering. Springer.","Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. Artificial Intelligence, Volume 29, Issue 3, 241-288.","Shaffer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press.","Shortliffe, E. H., & Buchanan, B. G. (1975). A model of inexact reasoning in medicine. Mathematical biosciences, 351-379.","Yanofsky, N. S., & Mannucci, M. A. (2008). Quantum Computing for Computer Scientists. Cambridge University Press.","Zadeh, L. A. (1965). Fuzzy sets. Information and Control, Volume 8, Issue 3, 338-353."]}

Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine and the costs associated with its manual diagnosis. The increment and heterogeneity of the different techniques, however, make it somewhat difficult to adequately follow the recent developments. A literature review within the area of computer-assisted diagnosis of SAHS has been performed comprising the last 15 years of research in the field. Screening approaches, methods for the detection and classification of respiratory events, comprehensive diagnostic systems, and an outline of current commercial approaches are reviewed. An overview of the different methods is presented together with validation analysis and critical discussion of the current state of the art.

Fault trees are a type of model which captures how small failures in probabilistic systems can propagate and ultimately lead to a critical system failure. An important component of fault tree analysis is finding small subsets of events which can cause a critical failure (often called ���cut sets���). Finding these small cut sets is important because they often correspond to the most likely way a system will fail. In this deliverable we provide an open source implementation of a procedure which computes minimal cut sets from fault trees by translating the problem to a satisfiability (SAT) problem. This SAT formula can then either be solved with a classical SAT solver, or with a quantum algorithm: specifically Grover���s algorithm for amplitude amplification. The solutions found by both methods are the same, but the quantum algorithm allows for a quadradic speedup in time complexity. Additionally, for quantum computers which have too few qubits to handle the entire problem instance, a divide and conquer approach can theoretically be used to split the problem up and obtain smaller quantum speedups (Rennela, Brand, Laarman, & Dunjko, 2021). The main component of this deliverable is the open source library itself, which can be found online here: https://github.com/NEASQC/ft-2-quantum-sat. In section 2 of this document we give an overview of problem of finding cut sets, and how the library solves this problem., {"references":["Audemard, G., & Simon, L. (2018). On the glucose SAT solver. International Journal on Artificial Intelligence Tools, 1840001","Bailleux, O., & Boufkhad, Y. (2003). Efficient CNF encoding of boolean cardinality constraints. International conference on principles and practice of constraint programming (pp. 108-122). Springer","Barrère, M., & Hankin, C. (2020). Fault tree analysis: Identifying maximum probability minimal cut sets with MaxSAT. 2020 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S) (pp. 53-54). IEEE","Biere, A., Heule, M., & van Maaren, H. (2009). Handbook of satisfiability. IOS press","Rennela, M., Brand, S., Laarman, A., & Dunjko, V. (2021). Hybrid divide-and-conquer approach for tree search algorithms. arXiv preprint arXiv:2007.07040","Ruijters, E., & Stoelinga, M. (2015). Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Computer science review, 29-62","Steven, E., & Antoine, R. (2007). Open-PSA Model Exchange Format. Rapport technique. The Open-PSA Initiative"]}