Quantum Generators: Application of Machine Learning Solution That Uses Swarm Intelligence and Edge Computing Technology to Enable Peer-to-Peer Collaboration and Collective Behavior

EasyChair Preprint no. 11715

Abstract

Quantum Generators is a means of achieving mass food production with short production cycles and when and where required by means of machines. The process for agricultural practices for plant growth in different stages is simulated in a machine with a capacity to produce multiple seeds from one seed input using computational models of multiplication. In this respect, we present a modular platform for automating cell synthesis which embodies synthesis abstraction with complex pathways of protein synthesis. Firstly, the automated synthesis could make use of combination of starting materials for planning the synthesis routes. We applied machine learning system based on best fit conditions and accordingly all quantum generating units follow the swarm intelligence concept in generating desired crops by matching the best cell generated in any of the units. We presented a robotic synthesis equipped with swarm intelligence based AI-driven learning that can effectively explore protein folding and also designed a differential evolution algorithm with sphere objective function with swarm learning to add or to remove the controls  to maintain a correct synthesis. For this we used swarm intelligence and evolution strategies, the differential evolution algorithm on synthesizer units for capturing, optimizing and for executing diverse cell synthesis. In this way, a group of swarm computing units assisted synthesizer consisting of edge computing that is part of CellSynputer is feasible for automated experimentation and in that respect an implementation of Differential Evolution algorithm for managing swarm of process controls as a part of microcontroller unit based on small model is presented. Although the platform model with swarm intelligence as microcontroller unit given us a method of automating and optimizing cellular assemblies however, this need to be tested using natural crop cells.

Keyphrases: CellSynputer, Differential Evolution, machine learning, Quantum Generators, Swarm Intelligence

@Booklet{EasyChair:11715,
  author = {Poondru Prithvinath Reddy},
  title = {Quantum Generators: Application of Machine Learning Solution That Uses Swarm Intelligence and Edge Computing Technology to Enable Peer-to-Peer Collaboration and Collective Behavior},
  howpublished = {EasyChair Preprint no. 11715},

  year = {EasyChair, 2024}}