Quantum Generators: a Self-Driving Experimentation for New Reproducibility of Complex Micro-Scale Protein Synthesis.

EasyChair Preprint 7270

Abstract

Quantum Generators is a means of achieving mass food production with short production cycles and when and where required by means of machines rather than land based farming which has serious limitations. 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 (generating multiple copies of kernel in repetition). In this paper, we present a method to replicate curiosity-driven learning that can accurately identify and analyse an unknown and complex set of conditions of an experimental or computational procedure in such a way that the method autonomously chooses the experiments that maximize the number of new and reproducible observations/targets for effective protein synthesis.  This model is part of robotic synthesis (equipped with CA) that can effectively explore a complex phenomenon exhibited by protein folding in cell synthesizer and is designed in an open-ended way with no explicit discovery or optimization. By applying CA-based robotic model to multicomponent cell synthesis, we may discover specific response to environmental parameters like temperature, etc. in a CellSynputer where abstraction representing cell synthesis is embodied.  In this way, it is possible to script and run desired synthesis with reconfigurable system for automated experimentation of diverse protein folding outcomes depending on the crop tissues. Since curiosity learning(CA) is nothing but Reinforcement learning, we show an implementation of it with small model in obscene of real-world model of CellSynputer.  Although the platform model given us a method of automating and optimizing cellular assemblies however, this need to be tested using natural crop cells for quantum generation.

Keyphrases: CellSynputer, On-demand Crop Seeds, Quantum Generators, Reinforcement Learning, Replicative Behaviour, Robotic Curiosity Learning

@booklet{EasyChair:7270,
  author    = {Poondru Prithvinath Reddy},
  title     = {Quantum Generators: a Self-Driving Experimentation for New Reproducibility of Complex Micro-Scale Protein Synthesis.},
  howpublished = {EasyChair Preprint 7270},
  year      = {EasyChair, 2021}}