标题： 光子受限玻尔兹曼机用于内容生成任务 显示英文标题

标题： Photonic restricted Boltzmann machine for content generation tasks

摘要： 受限玻尔兹曼机（RBM）是一种基于伊辛模型的神经网络，以其学习概率分布和随机生成新内容的能力而闻名。 然而，在内容生成任务中吉布斯采样的高计算成本对电子实现构成了显著瓶颈。 在此，我们提出了一种光子受限玻尔兹曼机（PRBM），利用光子计算加速吉布斯采样，从而实现高效的的内容生成。 通过引入一种高效的编码方法，PRBM消除了计算密集型矩阵分解的需要，并将吉布斯采样的计算复杂度从$O(N)$降低到$O(1)$。 此外，其非冯·诺依曼的光子计算架构避开了交互矩阵的存储，为大规模RBM提供了显著优势。 我们通过模拟二维伊辛模型验证了光子加速的吉布斯采样，其中观察到的相变温度与理论预测非常接近。 除了物理启发的任务，PRBM在生成和恢复各种内容方面表现出强大的能力，包括图像和时间序列，即使在存在噪声和畸变的情况下也是如此。 PRBM框架的可扩展性和降低的训练成本突显了其在生成式人工智能中推进光子计算的潜力。 显示英文摘要

摘要： The restricted Boltzmann machine (RBM) is a neural network based on the Ising model, well known for its ability to learn probability distributions and stochastically generate new content. However, the high computational cost of Gibbs sampling in content generation tasks imposes significant bottlenecks on electronic implementations. Here, we propose a photonic restricted Boltzmann machine (PRBM) that leverages photonic computing to accelerate Gibbs sampling, enabling efficient content generation. By introducing an efficient encoding method, the PRBM eliminates the need for computationally intensive matrix decomposition and reduces the computational complexity of Gibbs sampling from $O(N)$ to $O(1)$. Moreover, its non-Von Neumann photonic computing architecture circumvents the memory storage of interaction matrices, providing substantial advantages for large-scale RBMs. We experimentally validate the photonic-accelerated Gibbs sampling by simulating a two-dimensional Ising model, where the observed phase transition temperature closely matches the theoretical predictions. Beyond physics-inspired tasks, the PRBM demonstrates robust capabilities in generating and restoring diverse content, including images and temporal sequences, even in the presence of noise and aberrations. The scalability and reduced training cost of the PRBM framework underscore its potential as a promising pathway for advancing photonic computing in generative artificial intelligence.