Quantum Computation in Industry 4.0 Cyber-Physical Systems

Resumo

The strategic design of organizations in an environment where complexity is constantly increasing, as in the cyber-physical systems typical of Industry 4.0, is a process full of uncertainties. Leaders are forced to make decisions that affect other organizational units without being certain that their decisions are the right ones. To date, genetic algorithms and Bayesian networks were able to calculate the alignment status of industrial processes measured through certain key performance indicators (KPIs) to ensure that Industry 4.0 leaders make decisions aligned with the organization’s strategic objectives. However, the computational cost of these algorithms increases exponentially with the number of KPIs. The main objective of this thesis is to develop quantum algorithms that enable real-time strategic designs of complex industrial organizations and their practical implementation. To this end, we have demonstrated that quantum circuits can improve the results of genetic algorithms and Bayesian networks when optimizing certain complex industrial processes. We have developed algorithms that allow solving simple practical cases of chains of command and dependence in industrial processes. As a final objective we have implemented the theoretical development based on quantum principles in a device that allows to discern in real time the need to modify an industrial process due to the presence of production errors and to achieve an intuitive interface for the people who use it. The methodology we have used throughout the research is based on the development of quantum mirror circuits of the industrial processes to be designed in an optimal way. The data that will allow validation of the quantum algorithms have been obtained from a series of sensors installed in various industrial equipment with which it has been possible to design different case studies. For the integration of quantum simulations in industrial equipment we have used Radio Frequency Identification sensors based on low cost computing on a Raspberry Pi and to achieve interfaces that can be easily interpreted.