Quantum Generators: Chip Design for Processing Protein Structures Using AI and Geometric Patterns in Cell Synthesizer Unit

EasyChair Preprint 10074

Abstract

Quantum Generators is a means of achieving mass food production  when and where required by means of machines. The process for agricultural practices for plant growth  is simulated in a machine with a capacity to produce multiple seeds from one seed input. We present a modular platform for automating cell synthesis which embodies synthesis abstraction , therefore altogether different processor/’computing power’ is required to address cell synthesis. Firstly, the automated synthesis could make use of combination of starting materials for planning the synthesis routes and accordingly, neural networks are required to be trained on all possible reactions in cell synthesis for a particular crop. The trained AI system (machine learning ) allows for autonomous exploration of synthesis space  and these are automatically interpreted for cell structural patterns also and are then used to update the respective machine learning models. Secondly, an AI agent is designed to learn to optimize the final circuit generation from the cell synthesis requirement/environment. We designed an RL agent to add or to remove the circuits to maintain a correct computation and high-performance ‘computer graphics’, and to build through a series of steps for improving the synthesis performance &  efficiency. For this we used fully convolutional neural network the Q-learning algorithm (an RL algorithm ) for cell synthesis and the algorithm trained the circuit design agent using a matrix representation for synthesis requirement. Since we have two learning models( Composition and Geometric patterns ) along with a learning agent for circuit design, we show an implementation of combining two of them with small model in obscene of real-world model of CellSynputer for autonomous protein synthesis. Although the platform model given us a method of automating cellular assemblies however, this need to be tested using natural crop cells for quantum generation.

Keyphrases: AI Chip Design, Chip Circuit Generation, Graph Neural Networks, Quantum Generators, Reinforcement Learning

@booklet{EasyChair:10074,
  author    = {Poondru Prithvinath Reddy},
  title     = {Quantum Generators: Chip Design for Processing Protein Structures Using AI and Geometric Patterns in Cell Synthesizer Unit},
  howpublished = {EasyChair Preprint 10074},
  year      = {EasyChair, 2023}}