QUANTUM AI FRAMEWORKS: ENHANCING MACHINE LEARNING AND OPTIMIZATION THROUGH QUANTUM NEURAL NETWORKS, VARIATIONAL ALGORITHMS, AND QUANTUM WALKS
Narayana Gaddam
The Technology of quantum computing has appeared as a revolutionary technology that can revolt such disciplines as artificial intelligence, cryptography, and numerical complex optimization.In this research, Quantum AI approaches are integrated with upcoming quantum hardware to speed up the computations required in the learning of machines.This thesis explores various tasks on data classification, clustering, pattern recognition using the Quantum Neural Networks, parameterized quantum circuits run with quantum neural network ansatz, and Hybrid Quantum Classical algorithms.The method consists of using variational quantum algorithms to solve efficiently combinatorial optimization problems by means of quantum walk techniques.The key findings show that quantum enhanced models have great computational efficiency on complex data patterns, compared to classical counterparts.Furthermore, quantum error correction and reduction of noise have also been improved for enhancing the Narayana Gaddam https://iaeme.com/Home/journal/IJARET
INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING & TECHNOLOGY•Jun 28, 2022•0 citations