Complexity and Quantum Sciences
Analysis of complexity is one of the most effective tools to investigate nonlinear
phenomena. Complexity, as per as information theory is concerned, is a measure which
quantifies the amount of instability in a domain. It has been studied that
such unpredictable behavior can be quantified by information entropy, introduced by
Shannon. In its applications, complexity plays an important role in several diverse areas
of Business and Economics (organizational structure, economical data prediction, stock
market etc), Social Sciences, Medical (interpreting ECG, EEG signals) and Engineering
applications (wide, starting from optics to sensors).
It is also well-known that quantum theory is the fundamental theory that describes microscopic physics which departs from known classical physics. Quantum mechanics has been applied very successfully to physics, chemistry and even biology. More recently, quantum sciences when applied directly to technological purposes has shown to be able to provide quantum security enhancement in communications including quantum secure internet. On the other hand, quantum science has also provided a new computing paradigm that may change the future computational science landscape.
As both complexity and quantum sciences progress to help solve more difficult
problems in both domains, it is not inconceivable that methods developed by both will
be mutually beneficial to each other. Already, we have seen approaches like machine
learning in quantum sciences, quantum approach to complex networks and others have
been considered by researchers.
The present international symposium is based on collections of lectures, to discuss the developments and novel applications of quantum sciences and complexity theory.
©2019. Laboratory of Cryptography, Analysis and Structure. Institute for Mathematical Research. UPM.