7 papers · 1 filter
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…
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…
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…
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…
Clifford Perturbation Approximation for Quantum Error Mitigation
Ruiqi Zhang, Yuguo Shao, Fuchuan Wei +3
Quantum error mitigation (QEM) is critical for harnessing the potential of near-term quantum devices. Particularly, QEM protocols can be designed based on machine learning, where t…