2019/01/26 by Zhaoyun Chen, Zhao-Yun Chen, Chen, Zhao-Yun +8 · 4 citations
Computer Science · Engineering · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Computer science #Deep learning #Engineering #Neural Networks and Reservoir Computing #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum computer #Quantum machine learning #Systems engineering #Task (project management) #quant-ph
paper · pdf · doi:10.48550/arxiv.1901.09133
published in arXiv (Cornell University) (Cornell University) · 11 pages, 11 figures. Comments welcome
arxiv created 2019/01/26 · openalex publication_date 2019/01/26 · arxiv updated 2019/01/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Deep learning is a modern approach to realize artificial intelligence. Many frameworks exist to implement the machine learning task; however, performance is limited by computing resources. Using a quantum computer to accelerate training is a promising approach. The variational quantum circuit (VQC) has gained a great deal of attention because it can be run on near-term quantum computers. In this paper, we establish a new framework that merges traditional machine learning tasks with the VQC. Users can implement a trainable quantum operation into a neural network. This framework enables the training of a quantum-classical hybrid task and may lead to a new area of quantum machine learning.