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在量子电路内实现，因此被证明比现有方法具有更高的状态制备效率。该方法有望加速蒙特卡洛分析等算法。具体而言，在验证网络学习任务有效性后，该技术被应用于亚式期权衍生产品定价，为其他路径依赖期权的进一步研究奠定了基础。