Entangled Quantum Neural Network

Sanjay Gupta, Royce K. P. Zia

Jincheng Zhang

This paper investigates the potential of quantum neural networks (QNNs) by focusing on optimizing multi-body entanglement within the network architecture. The core claim is that strategic manipulation of entanglement between multiple nodes in a neural network can significantly enhance computational capabilities and improve information encoding efficiency. The proposed mechanism involves designing an algorithm to dynamically control the entanglement strength between nodes. This allows for the realization of more efficient parallel computation and enhanced information encoding. We explore theoretical frameworks and mathematical formulations to demonstrate the potential advantages of this approach compared to classical neural networks. The research highlights a novel pathway toward constructing quantum-enhanced neural network models by leveraging the unique properties of quantum entanglement. The primary focus is on the mathematical modeling and optimization strategies required to effectively harness entanglement for QNN performance.

Jincheng Zhang

Quantum Neural Networks (QNNs) represent a novel approach to artificial neural networks, leveraging the unique properties of quantum mechanics to enhance computation capabilities. This paper investigates the design and implementation of a quantum neural network (QNN) that employs adaptive neuron structures, dynamically adjusting their connections based on input data. We explore the potential of quantum entanglement to facilitate this self-adaptive behavior, aiming to achieve improved efficiency and robustness compared to classical neural networks. The core mechanism centers around utilizing quantum entanglement to enable neurons to subtly influence each other's states, promoting a form of self-organization within the network. This research offers a promising avenue for advancing QNNs and opens up new possibilities for complex computational tasks.

Davide Pastorello

Jin S. Y. Xu, Xiaoguang Chen, Rong Zhang +1 more

Entanglement distribution is important in quantum communication. Since there is no information with value in this process, purification is a good choice to solve channel noise. In this paper, we simulate the purification circuit under true environment on Cirq, which is a noisy intermediate-scale quantum (NISQ) platform. Besides, we apply quantum neural network (QNN) to the state after purification. We find that combining purification and quantum neural network has good robustness towards quantum noise. After general purification, quantum neural network can improve fidelity significantly without consuming extra states. It also helps to obtain the advantage of entangled states with higher dimension under amplitude damping noise. Thus, the combination can bring further benefits to purification in entanglement distribution.

Yuxuan Du, Xinbiao Wang, Naixu Guo +6 more