2 citations · 5 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…