The NEASQC project is organising a series of webinars that are not a simple overview of the NISQ Quantum Computing use cases investigated by our project, but a platform to learn from and exchange with the best experts in the fields covered by NEASQC. Our Work Package 6 prepared a programme of four weekly webinars in February 2022, dedicated to symbolic AI and graph algorithmics.
Dr Venkatesh Kannan, Leader of the Work Package 6 (Symbolic AI and graph algorithmics) in the NEASQC project, and Centre Technical Manager, Platforms & Technologies Programme Manager at the Irish Centre for High-End Computing (ICHEC)
Dr Andreas Wichert, Professor at the University of Lisbon
Quantum Tree Search
Abstract: In an uninformed search, no additional information about the states is given. A heuristic search is based on a heuristic function h(ν) that estimates the cheapest cost from the node ν to the goal. However, inventing heuristic functions is difficult. An alternative approach is that of the quantum tree search algorithm. Using Grover’s algorithm, we search through all possible paths and verify, for each path, whether it leads to the goal state.
Bio: Andreas (Andrzej) Wichert was born in Poland, Wroclaw. His grandfather Wladyslaw Wichert was a Polish officer.
He studied computer science at the University of Saarland, where he graduated in 1993. Afterwards, he became a PhD student at the Department of Neural Information Processing, University of Ulm. During this period he worked at the DaimlerChrysler Research and Technology Speech Recognition Group. He received a PhD in computer science in 2000 with his work on associative computation. He has since worked in the field of fMRI as a researcher with an interdisciplinary group, Department of Psychiatry III Ulm, changing to F&K Delvotec bonding machines where he led the development of a diagnostic expert system. From 2004 to 2005 he was the scientific director of MITI research group Klinikum rechts der Isar of the Technical University Munich. Since 2006, he is a researcher of INESC-ID. Since 2009 he is a member of the Group of AI for People and Society (gaips).
His research focuses on Artificial Intelligence, Machine Learning, Neural Networks, Quantum Cognition, Quantum Artificial Intelligence.
Dr Vicente Moret Bonillo, Professor at the University of A Coruña
Inaccurate Knowledge and Quantum Computing
Bio: (Valencia, 1962). Degree in Chemistry (B.Sc. Fundamental Chemistry, Major in Physical Chemistry, University of Santiago de Compostela, Spain, 1984). Doctor in Physics (Ph.D. Applied Physics, University of Santiago de Compostela, Spain, 1988). Post-Doctoral Research Fellow Biomedical Engineering Department (Medical College of Georgia, USA, 1988-1990). Full Professor Computer Science. Senior Member IEEE (Biomedical Engineering, since 2006). Award of Merit for Significant Contribution in the Field of Clinical Engineering (USA, 1990). Principal investigator of more than 30 projects funded on a competitive basis. Director 11 doctoral theses related to various aspects of Artificial Intelligence. Author of more than 145 scientific publications in international journals and congresses (JCR, Scopus, PubMed,…). Recognized specialist in Medical Applications of Artificial Intelligence, Intelligent Monitoring Medicine, Imprecise Reasoning Models in Medicine and Intelligent Systems Validation. Areas of current interest: Intelligent Monitoring of Sleep Apnea Syndrome and Quantum Computing.
Abstract: The classical model of certainty factos for dealing with innacurate knowledge can be efficiently implemented in a quantum environment. For this, we assume that certainty factors are strongly correlated with the quantum probability. We first explore the certainty factors approach for inexact reasoning from a classical point of view. Next, we introduce some basic aspects of quantum computing, and we pay special attention to quantum rule-based systems. We then build a use case: an inferential network to be implemented in both, the classical approach and the corresponding quantum circuit. Both implementations have been used to compare the behavior of the classical and the quantum approaches when confronted with the same hypothetical case. We analyze three different situations:(1) Only Imprecision (which refers to inaccuracy in declarative knowledge or facts) is present in the use case, (2) Only Uncertainty (which refers to inaccuracy in procedural knowledge or rules) is present in the use case, and (3) Both Imprecision and Uncertainty are present in the use case. Finally, we analyze the results to reach a conclusion about the eventually intrinsic probabilistic nature of the certainty factors model and to pave the way for future quantum implementations of this method for handling inaccurate knowledge.