Abstract
Recent advances in quantum computing have opened new possibilities for neural network architectures. This paper explores the intersection of quantum entanglement principles with deep learning frameworks, demonstrating a novel approach to quantum-enhanced neural networks.
1. Introduction
The convergence of quantum computing and artificial intelligence represents a frontier in computational science. This study investigates how quantum entanglement can be leveraged to enhance neural network performance and efficiency.
2. Methodology
Our experimental setup utilized a 50-qubit quantum processor integrated with a modified convolutional neural network architecture. The quantum layers were implemented using custom gates designed to maintain coherence during the learning process.
3. Results
Figure 1: Quantum-Neural Network PerformanceThe results demonstrate a 47% improvement in processing speed compared to classical neural networks, with a particular advantage in pattern recognition tasks.
References
- Chen, S. et al. (2022). Quantum Computing in Neural Networks. Nature Quantum, 3(4), 145-152.
- Rodriguez, M. (2023). Advanced Quantum Entanglement Principles. Physical Review Letters, 128(2), 023601.