Defraud 发表于 2025-3-25 05:01:02
http://reply.papertrans.cn/17/1673/167209/167209_21.png弯腰 发表于 2025-3-25 11:28:30
http://reply.papertrans.cn/17/1673/167209/167209_22.pngCommonplace 发表于 2025-3-25 12:18:57
http://reply.papertrans.cn/17/1673/167209/167209_23.png得罪 发表于 2025-3-25 17:14:13
http://reply.papertrans.cn/17/1673/167209/167209_24.pnghermitage 发表于 2025-3-25 19:58:02
http://reply.papertrans.cn/17/1673/167209/167209_25.pngMorbid 发表于 2025-3-26 01:11:35
,Deep Variational Auto-Encoder for Model-Based Water Quality Patrolling with Intelligent Surface Vehithm, exploiting the submodularity of the problem, demonstrates a 41% and 55% performance improvement over algorithms without UNet-VAE. This method enhances monitoring coverage and intensification of high-interest areas, providing a promising approach for hydrological resource surveillance.GLIDE 发表于 2025-3-26 05:43:54
http://reply.papertrans.cn/17/1673/167209/167209_27.png–LOUS 发表于 2025-3-26 10:28:39
http://reply.papertrans.cn/17/1673/167209/167209_28.pngobeisance 发表于 2025-3-26 14:31:17
A Surrogate Assisted Approach for Fitness Computation in Robust Optimization over Time,t this approach can achieve significantly superior performances to the existing framework, especially for specific surrogate model configurations. Furthermore, we show that in certain cases where our algorithms are less efficient than the existing approach, such inefficiency is compensated by improvements in error.ARY 发表于 2025-3-26 20:15:14
,An Experimental Comparison of Qiskit and Pennylane for Hybrid Quantum-Classical Support Vector Machnce of both frameworks remains stable for up to 20 qubits, indicating their suitability for practical applications. Overall, our findings provide valuable insights into the strengths and limitations of Qiskit and Pennylane for hybrid quantum-classical machine learning.