HL Huang, D Wu, D Fan, X Zhu - Science China Information Sciences, 2020 - Springer
Over the last two decades, tremendous advances have been made for constructing large- scale quantum computers. In particular, quantum computing platforms based on …
NR Zhou, TF Zhang, XW Xie, JY Wu - Signal Processing: Image …, 2023 - Elsevier
It has been reported that quantum generative adversarial networks have a potential exponential advantage over classical generative adversarial networks. However, quantum …
Hybrid quantum–classical systems make it possible to utilize existing quantum computers to their fullest extent. Within this framework, parameterized quantum circuits can be regarded …
Quantum computers have made extraordinary progress over the past decade, and significant milestones have been achieved along the path of pursuing universal fault-tolerant …
Quantum machine learning is expected to be one of the first practical applications of near- term quantum devices. Pioneer theoretical works suggest that quantum generative …
J Carrasquilla - Advances in Physics: X, 2020 - Taylor & Francis
Quantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …
Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of …
Quantum state tomography (QST) is a challenging task in intermediate-scale quantum devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the …
Deep learning has been shown to be able to recognize data patterns better than humans in specific circumstances or contexts. In parallel, quantum computing has demonstrated to be …