17-19 May, 2018
About the Conference
Our mission is to to bring together world top experts in the quantum theory approach to cognitive, socio-humanitarian and computer sciences for brainstorming on the most important problems of probabilistic modeling of systems of artificial intelligence, cognitive, social and political sciences, economics, and finances.
- Alexander P. Alodjants, deputy chair (ITMO University, Saint Petersburg, Russia)
- Andrei Yu. Khrennikov, deputy chair (Linnaeus University, Sweden/ ITMO University, Russia)
- Sergei V. Khmelevsky, deputy chair (ITMO University, Saint Petersburg, Russia)
- Quantum cognition is across-disciplinary field of research which seeks to describe cognitive and psychological phenomena in the human system with formalisms and methods of quantum theory. The aim is to found out fundamental similarities in behavior of complicated social and quantum systems. Exploration of these analogies allows to obtain new information about the behavior of complex biological and social systems, on the other hand, to use well-developed methods of quantum mechanics, laser physics, and nonlinear dynamics for modeling human and social behavior.
- Cognitive computations are newest technologies that partially repeat the features of the human brain and are able to work much more efficiently. It is promising to use of such systems in a huge variety of fields and directions, including banking, materials science, business optimization, urban infrastructure management, environmental assessment, research in various fields of science and medicine. The main task of cognitive technologies is to enable a person to work with unstructured data in a convenient way.
- Quantum machine learning and quantum algorithms are high performance programing technologies for solutions on quantum (or quantum-like) computers (simulators) complex problems like data search in big data bases, classification etc. The software for quantum machine learning uses quantum algorithms to processinformation, that does not available to the "ordinary" classical computer. This opens entirely new possibilities and prospects in terms of accelerating the solution of computational hard problems that can surpass the most famous classical algorithms used in machine learning.