论文标题
带量子傅里叶变换采样器的量子自学习蒙特卡洛
Quantum self-learning Monte Carlo with quantum Fourier transform sampler
论文作者
论文摘要
自我学习的大都市杂货算法是一种强大的蒙特卡洛方法，借助机器学习，可以自适应地生成易于样本的概率分布，以近似给定的难以示例分布。本文提供了一种新的自学习蒙特卡洛方法，该方法利用量子计算机输出提案分布。特别是，我们根据量子傅立叶变换电路显示了该一般方案的新型子类。该采样器在经典上可以模拟，而比常规方法具有一定的优势。某些数值模拟证明了该“量子启发”算法的性能。
The self-learning Metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distribution for approximating a given hard-to-sample distribution. This paper provides a new self-learning Monte Carlo method that utilizes a quantum computer to output a proposal distribution. In particular, we show a novel subclass of this general scheme based on the quantum Fourier transform circuit; this sampler is classically simulable while having a certain advantage over conventional methods. The performance of this "quantum inspired" algorithm is demonstrated by some numerical simulations.
