论文标题
高能量物理学中的无监督量子电路学习
Unsupervised Quantum Circuit Learning in High Energy Physics
论文作者
论文摘要
无监督的生成模型培训是一项机器学习任务，在科学计算中具有许多应用。在这项工作中，我们评估了使用基于量子电路的生成模型生成高能物理过程的合成数据的功效。我们使用对量子电路出生机器的非对抗性，基于梯度的培训来生成2和3个变量的关节分布。
Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. In this work we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy physics processes. We use non-adversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over 2 and 3 variables.
