2 citations · 5 across the 8 of their papers we have counts for
8 papers
Efficient Circuit Wire Cutting Based on Commuting Groups
Xinpeng Li, Vinooth Kulkarni, Daniel T. Chen +5
Current quantum devices face challenges when dealing with large circuits due to error rates as circuit size and the number of qubits increase. The circuit wire-cutting technique ad…
Toward Consistent High-fidelity Quantum Learning on Unstable Devices via Efficient In-situ Calibration
Zhirui Hu, Robert Wolle, Mingzhen Tian +3
In the near-term noisy intermediate-scale quantum (NISQ) era, high noise will significantly reduce the fidelity of quantum computing. Besides, the noise on quantum devices is not s…
A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices
Jinyang Li, Zhepeng Wang, Zhirui Hu +3
Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement…
Carbon Emissions of Quantum Circuit Simulation: More than You Would Think
Jinyang Li, Qiang Guan, Dingwen Tao +1
The rapid advancement of quantum hardware brings a host of research opportunities and the potential for quantum advantages across numerous fields. In this landscape, quantum circui…
VENUS: A Geometrical Representation for Quantum State Visualization
Shaolun Ruan, Ribo Yuan, Qiang Guan +6
Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widel…
Battle Against Fluctuating Quantum Noise: Compression-Aided Framework to Enable Robust Quantum Neural Network
Zhirui Hu, Youzuo Lin, Qiang Guan +1
Recently, we have been witnessing the scale-up of superconducting quantum computers; however, the noise of quantum bits (qubits) is still an obstacle for real-world applications to…
QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model
Zhepeng Wang, Jinyang Li, Zhirui Hu +3
Security has always been a critical issue in machine learning (ML) applications. Due to the high cost of model training -- such as collecting relevant samples, labeling data, and c…
Curvature positivity of invariant direct images of Hermitian vector bundles
Fusheng Deng, Jinjin Hu, Weiwen Jiang
We prove that the invariant part, with respect to a compact group action satisfying certain condition, of the direct image of a Nakano positive Hermitian holomorphic vector bundle…