5 papers
Reinforcement Learning for Charging Optimization of Inhomogeneous Dicke Quantum Batteries
Xiaobin Song, Siyuan Bai, Da-Wei Wang +4
Charging optimization is a key challenge to the implementation of quantum batteries, particularly under inhomogeneity and partial observability. This paper employs reinforcement le…
On the Design of Expressive and Trainable Pulse-based Quantum Machine Learning Models
Han-Xiao Tao, Xin Wang, Re-Bing Wu
Pulse-based Quantum Machine Learning (QML) has emerged as a novel paradigm in quantum artificial intelligence due to its exceptional hardware efficiency. For practical applications…
Predictive Performance of Deep Quantum Data Re-uploading Models
Xin Wang, Han-Xiao Tao, Re-Bing Wu
Quantum machine learning models incorporating data re-uploading circuits have garnered significant attention due to their exceptional expressivity and trainability. However, their…
Unleashing the Expressive Power of Pulse-Based Quantum Neural Networks
Han-Xiao Tao, Jiaqi Hu, Re-Bing Wu
Quantum machine learning (QML) based on Noisy Intermediate-Scale Quantum (NISQ) devices hinges on the optimal utilization of limited quantum resources. While gate-based QML models…
On the Role of Controllability in Pulse-based Quantum Machine Learning Models
Han-Xiao Tao, Re-Bing Wu
Pulse-based quantum machine learning (QML) models possess full expressivity when they are ensemble controllable. However, it has also been shown that barren plateaus emerge in such…