A framework to learn a multi-modal distribution is proposed, denoted as the Conditional Quantum Generative Adversarial Network (C-qGAN). The neural network structure is strictly within a quantum circuit and, as a consequence, is shown to represent a more efficient state preparation procedure than current methods. This methodology has the potential to speed-up algorithms, such as Monte Carlo analysis. In particular, after demonstrating the effectiveness of the network in the learning task, the technique is applied to price Asian option derivatives, providing the foundation for further research on other path-dependent options.

翻译：提出了一种学习多模态分布的框架，称为条件量子生成对抗网络（C-qGAN）。该神经网络结构严格限定在量子电路内，并因此被证明能比现有方法更高效地实现量子态制备过程。该技术有望加速蒙特卡罗分析等算法。具体而言，在验证该网络在任务中的学习有效性后，该技术被应用于亚洲期权衍生品的定价，为其他路径依赖型期权的进一步研究奠定了基础。