Kaiwu-PyTorch-Plugin: Bridging Deep Learning and Photonic Quantum Computing for Energy-Based Models and Active Sample Selection
🤖 AI Summary
This work addresses the low sampling efficiency and data redundancy inherent in classical energy-based models by integrating a coherent Ising machine into the PyTorch ecosystem for the first time, enabling seamless fusion of photonic quantum computing with deep learning frameworks. Leveraging quantum-accelerated Boltzmann sampling and active sample selection, the authors develop hybrid quantum-classical architectures such as QBM-VAE and Q-Diffusion. Evaluated on single-cell and OpenWebText datasets, the proposed approach achieves state-of-the-art performance, demonstrating the efficacy of quantum-enhanced energy-based models in efficient training and active learning scenarios.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Energy-Based Models
Boltzmann sampling
Active Sample Selection
Quantum-Classical Integration
Computational Efficiency
Innovation
Methods, ideas, or system contributions that make the work stand out.
Photonic Quantum Computing
Energy-Based Models
Boltzmann Sampling
Active Sample Selection
Hybrid Quantum-Classical Architecture
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