36 citations · 54 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
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…
Supervised Learning Guarantee for Quantum AdaBoost
Yabo Wang, Xin Wang, Bo Qi +1
In the noisy intermediate-scale quantum (NISQ) era, the capabilities of variational quantum algorithms are greatly constrained due to a limited number of qubits and the shallow dep…
Power Characterization of Noisy Quantum Kernels
Yabo Wang, Bo Qi, Xin Wang +2
Quantum kernel methods have been widely recognized as one of promising quantum machine learning algorithms that have potential to achieve quantum advantages. In this paper, we theo…
EHA: Entanglement-variational Hardware-efficient Ansatz for Eigensolvers
Xin Wang, Bo Qi, Yabo Wang +1
Variational quantum eigensolvers (VQEs) are one of the most important and effective applications of quantum computing, especially in the current noisy intermediate-scale quantum (N…
Trainability Enhancement of Parameterized Quantum Circuits via Reduced-Domain Parameter Initialization
Yabo Wang, Bo Qi, Chris Ferrie +1
Parameterized quantum circuits (PQCs) have been widely used as a machine learning model to explore the potential of achieving quantum advantages for various tasks. However, trainin…