A Variational Approach to Quantum Error Mitigation
Gokul Subramanian Ravi, Kaitlin Smith, et al.
APS March Meeting 2022
Neural networks are computing models that have been leading progress in Machine Learning (ML) and Artificial Intelligence (AI) applications. In parallel, the first small-scale quantum computing devices have become available in recent years, paving the way for the development of a new paradigm in information processing. Here we give an overview of the most recent proposals aimed at bringing together these ongoing revolutions, and particularly at implementing the key functionalities of artificial neural networks on quantum architectures. We highlight the exciting perspectives in this context, and discuss the potential role of near-term quantum hardware in the quest for quantum machine learning advantage.
Gokul Subramanian Ravi, Kaitlin Smith, et al.
APS March Meeting 2022
Jun Qi, Chao-Han Huck Yang, et al.
NeurIPS 2021
Weiwen Jiang, Jinjun Xiong, et al.
Nature Communications
Alexey Galda, Elica Kyoseva, et al.
QCE 2024