4 papers
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…
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…
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…