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[2602.19114] Kaiwu-PyTorch-Plugin: Bridging Deep Learning and Photonic Quantum Computing for Energy-Based Models and Active Sample Selection
Summary
The Kaiwu-PyTorch-Plugin (KPP) integrates Deep Learning with Photonic Quantum Computing, enhancing Energy-Based Models and Active Sample Selection, achieving state-of-the-art performance on various datasets.
Why It Matters
This research is significant as it addresses inefficiencies in classical machine learning frameworks by leveraging quantum computing. The integration of KPP into the PyTorch ecosystem could lead to advancements in model training and data optimization, potentially transforming the landscape of AI and quantum computing.
Key Takeaways
- KPP bridges Deep Learning and Photonic Quantum Computing.
- It improves Energy-Based Models through quantum integration.
- Empirical results show KPP achieves state-of-the-art performance.
- The framework supports accelerated Boltzmann sampling and active data selection.
- Hybrid architectures like QBM-VAE and Q-Diffusion are facilitated.
Quantum Physics arXiv:2602.19114 (quant-ph) [Submitted on 22 Feb 2026] Title:Kaiwu-PyTorch-Plugin: Bridging Deep Learning and Photonic Quantum Computing for Energy-Based Models and Active Sample Selection Authors:Hongdong Zhu, Qi Gao, Yin Ma, Shaobo Chen, Haixu Liu, Fengao Wang, Tinglan Wang, Chang Wu, Kai Wen View a PDF of the paper titled Kaiwu-PyTorch-Plugin: Bridging Deep Learning and Photonic Quantum Computing for Energy-Based Models and Active Sample Selection, by Hongdong Zhu and 8 other authors View PDF HTML (experimental) Abstract:This paper introduces the Kaiwu-PyTorch-Plugin (KPP) to bridge Deep Learning and Photonic Quantum Computing across multiple dimensions. KPP integrates the Coherent Ising Machine into the PyTorch ecosystem, addressing classical inefficiencies in Energy-Based Models. The framework facilitates quantum integration in three key aspects: accelerating Boltzmann sampling, optimizing training data via Active Sampling, and constructing hybrid architectures like QBM-VAE and Q-Diffusion. Empirical results on single-cell and OpenWebText datasets demonstrate KPPs ability to achieve SOTA performance, validating a comprehensive quantum-classical paradigm. Subjects: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.19114 [quant-ph] (or arXiv:2602.19114v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.19114 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: ...