Eliminate Entanglement in Quantum Information Processing Utilizing Hybrid Quantum-Classical Neural Networks

A particle generated inside a group cannot be described independently of the other group particles during the interaction, sharing, or the development of a particle's group in close geographic in the quantum mechanics. Particles' arrangement represents their quantum states at zero temperature and highlights the particle's energy and symmetry gap during the Symmetry Protected Topology SPT as the “trivial” topological instructions' existence of these particles and provides a more consistent method for differentiating the quantum data source's features such as a quantum sensor or an intricate simulation of a device through quantum entanglement. In this research utilizing the TensorFlow framework with a cluster state as a data source which mayor may not excited, the researcher demonstrates and evaluates a quantum variant of the conventional classical neural network (CNN) called the hybrid quantum-classical convolutional neural network (HQCNN) which is trained on data containing a quantum mechanical state protected by the SPT topology. The accuracy was determined in purely model's results did not exceed 89.58% accuracy within ten epochs, whereas the hybrid model's results were accurate to 100% within three epochs with a single (Twist) filter and seven epochs with a multi-filter (multi-Twist) model with the goal of eliminating quantum entanglement.