Keywords: image processing, quantum computing, reverse emergence, evolutionary algorithms, learning as searching, cellular automata
Image processing using quantum computing and reverse emergence
In this paper, the authors present a new approach for image processing based on reverse emergence and quantum computing. The key idea is to use cellular automata as a complex system and quantum inspired algorithms as a search strategy. Cellular automata system is a collection of many simple units that operate in parallel and interact locally with each other using simple rules so as to produce emergent properties and structures. A system exhibits emergence when there are emergent at the macro level that dynamically arise from the local interactions between the parts at the micro level. The complexity of these novel properties or behaviours observed at the macro level is greater than the sum of the parts. Reverse emergence refers to the problem of finding simple rules which give rise to the desired complex behaviour. To cope with this hard problem, the authors propose the use of quantum evolutionary algorithms for training cellular automata to perform image processing tasks. The resulting software is simpler and flexible compared to conventional software development techniques and the obtained results are very promising.