论文标题
培训高斯玻色子抽样分布
Training Gaussian Boson Sampling Distributions
论文作者
论文摘要
高斯玻色子采样（GBS）是光子量子计算的近期平台。已经开发了依赖于直接编程GBS设备的应用程序，但是训练和优化电路的能力一直是开发新算法的关键成分。在这项工作中，我们为GBS分布提供了分析梯度公式，该公式可用于使用基于梯度下降的标准方法来训练设备。我们引入了分布的参数化，该参数允许通过从正在优化的同一设备中进行采样来估算梯度。在使用kullback-leibler差异或对数可能成本函数的训练的情况下，我们表明可以经典地计算梯度，从而进行快速训练。我们通过随机优化和无监督学习的数值实验来说明这些结果。作为一个特殊的例子，我们介绍了变异iSing求解器，这是一种用于训练GBS设备的混合算法，以对具有很高概率的经典ISING模型的基态进行采样。
Gaussian Boson Sampling (GBS) is a near-term platform for photonic quantum computing. Applications have been developed which rely on directly programming GBS devices, but the ability to train and optimize circuits has been a key missing ingredient for developing new algorithms. In this work, we derive analytical gradient formulas for the GBS distribution, which can be used to train devices using standard methods based on gradient descent. We introduce a parametrization of the distribution that allows the gradient to be estimated by sampling from the same device that is being optimized. In the case of training using a Kullback-Leibler divergence or log-likelihood cost function, we show that gradients can be computed classically, leading to fast training. We illustrate these results with numerical experiments in stochastic optimization and unsupervised learning. As a particular example, we introduce the variational Ising solver, a hybrid algorithm for training GBS devices to sample ground states of a classical Ising model with high probability.
