8 papers
Enhancing Quantum Machine Learning with Anyons
Da Zhang, Wen-Qiang Liu, Zhaohui Wei +1
The power of quantum computing and quantum machine learning relies on harnessing uniquely quantum phenomena as computational resources. While superposition, coherence and entanglem…
Diagnosing Quantum Circuits: Noise Robustness, Trainability, and Expressibility
Yuguo Shao, Zhenyu Chen, Zhaohui Wei +1
Achieving practical quantum advantage on near-term noisy hardware is a central goal of quantum computation. However, without efficient pre-execution diagnostics, circuit design and…
Enhancing classical simulation with noisy quantum devices
Ruiqi Zhang, Fuchuan Wei, Zhaohui Wei
As quantum devices continue to improve in scale and precision, a central challenge is how to effectively utilize noisy hardware for meaningful computation. Most existing approaches…
Taming Barren Plateaus in Arbitrary Parameterized Quantum Circuits without Sacrificing Expressibility
Zhenyu Chen, Yuguo Shao, Zhengwei Liu +1
Quantum algorithms based on parameterized quantum circuits (PQCs) have enabled a wide range of applications on near-term quantum devices. However, existing PQC architectures face s…
A Unified Frequency Principle for Quantum and Classical Machine Learning
Rundi Lu, Ruiqi Zhang, Weikang Li +3
Quantum neural networks constitute a key class of near-term quantum learning models, yet their training dynamics remain not fully understood. Here, we present a unified theoretical…
Scalable Quantum Error Mitigation with Neighbor-Informed Learning
Zhenyu Chen, Bin Cheng, Minbo Gao +4
Noise in quantum hardware is the primary obstacle to realizing the transformative potential of quantum computing. Quantum error mitigation (QEM) offers a promising pathway to enhan…