Design Space Exploration of Hybrid Quantum–Classical Neural Networks

Electronics

Saif Al-Kuwari¹  Muhammad Kashif¹ 

[1] Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha P.O. Box 34110, Qatar;

关键词: quantum machine learning;    quantum neural networks;    hybrid neural networks;    amplitude encoding;    angle encoding;    variational quantum circuits;   

DOI  :  10.3390/electronics10232980

来源: DOAJ

【 摘 要 】

The unprecedented success of classical neural networks and the recent advances in quantum computing have motivated the research community to explore the interplay between these two technologies, leading to the so-called quantum neural networks. In fact, universal quantum computers are anticipated to both speed up and improve the accuracy of neural networks. However, whether such quantum neural networks will result in a clear advantage on noisy intermediate-scale quantum (NISQ) devices is still not clear. In this paper, we propose a systematic methodology for designing quantum layer(s) in hybrid quantum–classical neural network (HQCNN) architectures. Following our proposed methodology, we develop different variants of hybrid neural networks and compare them with pure classical architectures of equivalent size. Finally, we empirically evaluate our proposed hybrid variants and show that the addition of quantum layers does provide a noticeable computational advantage.

【 授权许可】

Unknown