Enhancing Quantum Neural Networks with Deep Learning for Predictive Modeling
Keywords:
Deep learning, Hybrid architecture, Predictive modeling, Quantum computing, Quantum Neural Networks (QNNs)Abstract
Quantum Neural Networks (QNNs) are quickly gaining attention as a next-generation approach at the crossroads of quantum computing and artificial intelligence. These models exploit quantum phenomena such as superposition and entanglement to offer new possibilities for handling complex, high-dimensional datasets. This computational power positions QNNs as strong candidates for advanced pattern recognition and predictive analytics. However, real-world adoption is still limited by several challenges, including unstable behavior, low circuit fidelity, limited scalability, and difficulties in model training due to gradient issues and quantum noise. To help overcome these obstacles, this paper introduces a hybrid model that combines quantum circuits with classical deep learning components. The quantum layer functions as a powerful feature extractor, while classical neural layers manage the high-level learning and prediction tasks. This design allows the model to benefit from the strengths of both quantum and traditional computation.
Our approach was tested on several benchmark datasets and demonstrated consistent improvements in accuracy, training speed, and generalization capability compared to standalone quantum or classical systems. In addition to experimental results, we offer a detailed look at the model’s training behavior, computational cost, and hardware compatibility. This work presents a practical, scalable direction for advancing quantum machine learning and contributes to the broader goal of making quantum-enhanced AI systems accessible for real-world applications. It also highlights how hybrid architectures can serve as a bridge between current technological limitations and the full potential of quantum computing in intelligent systems.
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