6 papers
Training-Free Quantum Generative Paradigm via Local Parent Hamiltonians
Shu Tian, Jiaqi Hu, Rebing Wu +1
We propose a training-free quantum generative paradigm, which is fundamentally different from current generative models, which demand substantial computational power, face practica…
Generation via Classical Noise Reuploading
Xin Wang, Rebing Wu
We propose a novel quantum generative model paradigm that fundamentally avoids the issue of extremely small post-selection probabilities present in previous models. Unlike existing…
Towards Ultimate Accuracy in Quantum Multi-Class Classification: A Trace-Distance Binary Tree AdaBoost Classifier
Xin Wang, Yabo Wang, Rebing Wu
We propose a Trace-distance binary Tree AdaBoost (TTA) multi-class quantum classifier, a practical pipeline for quantum multi-class classification that combines quantum-aware reduc…
Tight Generalization Bound for Supervised Quantum Machine Learning
Xin Wang, Rebing Wu
We derive a tight generalization bound for quantum machine learning that is applicable to a wide range of supervised tasks, data, and models. Our bound is both efficiently computab…
Machine Learning for Estimation and Control of Quantum Systems
Hailan Ma, Bo Qi, Ian R. Petersen +3
The development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation, and sensing.…
Limitations of Amplitude Encoding on Quantum Classification
Xin Wang, Yabo Wang, Bo Qi +1
It remains unclear whether quantum machine learning (QML) has real advantages when dealing with practical and meaningful tasks. Encoding classical data into quantum states is one o…