7 papers
Linear Complexity Fermionic Simulation on Quantum Devices with Hardware Connectivity Constraints
Xiangyu Gao, Winston Li, Jiakang Li +4
Simulating fermionic systems on quantum hardware requires compiling fermionic Hamiltonians into executable quantum circuits. Existing approaches treat each compilation stage indepe…
Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability
Dengzhe Hou, Zihao Wu, Lingyu Jiang +3
Electroencephalography (EEG) is a cornerstone of brain-computer interfaces and clinical neuroscience, yet deep learning models are typically trained and evaluated under a single, u…
Genesis: A Compiler Framework for Hamiltonian Simulation on Hybrid CV-DV Quantum Computers
Zihan Chen, Jiakang Li, Minghao Guo +7
This paper introduces Genesis, the first compiler designed to support Hamiltonian Simulation on hybrid continuous-variable (CV) and discrete-variable (DV) quantum computing systems…
QuEst: Graph Transformer for Quantum Circuit Reliability Estimation
Hanrui Wang, Pengyu Liu, Jinglei Cheng +10
Among different quantum algorithms, PQC for QML show promises on near-term devices. To facilitate the QML and PQC research, a recent python library called TorchQuantum has been rel…
QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning
Hanrui Wang, Zirui Li, Jiaqi Gu +3
Parameterized Quantum Circuits (PQC) are drawing increasing research interest thanks to its potential to achieve quantum advantages on near-term Noisy Intermediate Scale Quantum (N…
QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization
Hanrui Wang, Jiaqi Gu, Yongshan Ding +4
Parameterized Quantum Circuits (PQC) are promising towards quantum advantage on near-term quantum hardware. However, due to the large quantum noises (errors), the performance of PQ…