Quantum Bayesian AI (Q-B) is an emerging field that levers the computational gains available in Quantum computing. The promise is an exponential speed-up in many Bayesian algorithms. Our goal is to apply these methods directly to statistical and machine learning problems. We provide a duality between classical and quantum probability for calculating of posterior quantities of interest. Our framework unifies MCMC, Deep Learning and Quantum Learning calculations from the viewpoint from von Neumann's principle of quantum measurement. Quantum embeddings and neural gates are also an important part of data encoding and feature selection. There is a natural duality with well-known kernel methods in statistical learning. We illustrate the behaviour of quantum algorithms on two simple classification algorithms. Finally, we conclude with directions for future research.
翻译:Quantum Bayesian AI (Q-B) 是一个新兴领域,它拉动了量子计算中可用的计算收益。 承诺是许多巴伊西亚算法的指数加速。 我们的目标是将这些方法直接应用于统计和机器学习问题。 我们的目标是将这些方法直接应用于统计和机器学习问题。 我们提供了计算后方利益数量的经典和量子概率的双重性。 我们的框架统一了从冯纽曼量量子测量原则的角度出发的MCMC、 深学习和量子学习计算。 量子嵌入和神经门也是数据编码和特征选择的一个重要部分。 在统计学习中存在着一个自然的双重性和众所周知的内核方法。 我们展示了两种简单分类算法的量子算法行为。 最后,我们得出了未来研究的方向。