Zobrazeno 1 - 10
of 532
pro vyhledávání: '"Du, Yuxuan"'
Autor:
Wang, Z. T., Chen, Qiuhao, Du, Yuxuan, Yang, Z. H., Cai, Xiaoxia, Huang, Kaixuan, Zhang, Jingning, Xu, Kai, Du, Jun, Li, Yinan, Jiao, Yuling, Wu, Xingyao, Liu, Wu, Lu, Xiliang, Xu, Huikai, Jin, Yirong, Wang, Ruixia, Yu, Haifeng, Zhao, S. P.
To effectively implement quantum algorithms on noisy intermediate-scale quantum (NISQ) processors is a central task in modern quantum technology. NISQ processors feature tens to a few hundreds of noisy qubits with limited coherence times and gate ope
Externí odkaz:
http://arxiv.org/abs/2406.12195
The No-Free-Lunch (NFL) theorem, which quantifies problem- and data-independent generalization errors regardless of the optimization process, provides a foundational framework for comprehending diverse learning protocols' potential. Despite its signi
Externí odkaz:
http://arxiv.org/abs/2405.07226
Autor:
Zuo, Wenxuan, Zhu, Zifan, Du, Yuxuan, Yeh, Yi-Chun, Fuhrman, Jed A., Lv, Jinchi, Fan, Yingying, Sun, Fengzhu
High-dimensional longitudinal time series data is prevalent across various real-world applications. Many such applications can be modeled as regression problems with high-dimensional time series covariates. Deep learning has been a popular and powerf
Externí odkaz:
http://arxiv.org/abs/2404.04317
Quantum kernels hold great promise for offering computational advantages over classical learners, with the effectiveness of these kernels closely tied to the design of the quantum feature map. However, the challenge of designing effective quantum fea
Externí odkaz:
http://arxiv.org/abs/2401.11098
Optical quantum sensing promises measurement precision beyond classical sensors termed the Heisenberg limit (HL). However, conventional methodologies often rely on prior knowledge of the target system to achieve HL, presenting challenges in practical
Externí odkaz:
http://arxiv.org/abs/2311.07203
Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms, utilizing minimal measurements. While the ran
Externí odkaz:
http://arxiv.org/abs/2311.03713
Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of quantum machine learning, poised to leverage the nascent capabilities of near-term quantum computers to surmount classical machine learning challenges. None
Externí odkaz:
http://arxiv.org/abs/2309.10441
Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality. Statistical learning offers new possibilities in this regime by neural-network protocols and classical shadows, while both methods have limitations: the f
Externí odkaz:
http://arxiv.org/abs/2308.11290
Entanglement serves as the resource to empower quantum computing. Recent progress has highlighted its positive impact on learning quantum dynamics, wherein the integration of entanglement into quantum operations or measurements of quantum machine lea
Externí odkaz:
http://arxiv.org/abs/2306.03481
TeD-Q is an open-source software framework for quantum machine learning, variational quantum algorithm (VQA), and simulation of quantum computing. It seamlessly integrates classical machine learning libraries with quantum simulators, giving users the
Externí odkaz:
http://arxiv.org/abs/2301.05451