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Quantum Optimization Quantum Machine Learning

Kaiwu-PyTorch-Plugin: Bridging Deep Learning and Photonic Quantum Computing for Energy-Based Models and Active Sample Selection

arXiv
Authors: Hongdong Zhu, Qi Gao, Yin Ma, Shaobo Chen, Haixu Liu, Fengao Wang, Tinglan Wang, Chang Wu, Kai Wen

Year

2026

Paper ID

12108

Status

Preprint

Abstract Read

~2 min

Abstract Words

78

Citations

N/A

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.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2026 reference point for readers tracking recent quantum research.
  • This paper introduces the Kaiwu-PyTorch-Plugin (KPP) to bridge Deep Learning and Photonic Quantum Computing across multiple dimensions.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2602.19114 [2] arXiv https://arxiv.org/abs/2602.19114 [3] Publisher https://arxiv.org/abs/2602.19114

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