Quantum Machine Learning: Exploring Quantum Algorithms for Enhancing Deep Learning Models
This research clarifies the scope, intricacy, and scalability issues surrounding QNNs and offers insights into the possible advantages and difficulties of quantum-enhanced deep learning.
Abstract
Using quantum algorithms to improve deep learning models' capabilities is becoming increasingly popular as quantum computing develops. In this work, we investigate how quantum algorithms using quantum neural networks (QNNs) might enhance the effectiveness and performance of deep learning models. We examine the effects of quantum-inspired methods on tasks, including regression, sorting, and optimization, by thoroughly analyzing quantum algorithms and how they integrate with deep learning systems. We experiment with Estimator QNN and Sampler QNN implementations using Qiskit machine-learning, analyzing their forward and backward pass outcomes to assess the effectiveness of quantum algorithms in improving deep learning models. Our research clarifies the scope, intricacy, and scalability issues surrounding QNNs and offers insights into the possible advantages and difficulties of quantum-enhanced deep learning. This work adds to the continuing investigation of quantum computing's potential to advance machine learning and artificial intelligence paradigms by clarifying the interaction between quantum algorithms and deep learning systems.