论文标题
高效的张量网络算法，用于多资产傅里叶选项定价
A highly efficient tensor network algorithm for multi-asset Fourier options pricing
论文作者
论文摘要
风险评估，尤其是衍生品定价是计算融资的核心领域之一，并且是金融行业全球计算资源的相当一部分。我们概述了一种量子启发的算法，用于多资产选项定价。该算法基于张量网络，该网络允许在许多身体物理和量子计算的量子中进行重大概念和数值突破。在探索的概念验证示例中，张量网络方法在香草蒙特卡洛模拟上产生了几个数量级的速度。我们认为这是充分的证据表明，张量网络方法的使用具有巨大的希望，可以减轻金融和其他行业中风险评估的计算负担，从而有可能降低这些模拟的碳足迹。
Risk assessment and in particular derivatives pricing is one of the core areas in computational finance and accounts for a sizeable fraction of the global computing resources of the financial industry. We outline a quantum-inspired algorithm for multi-asset options pricing. The algorithm is based on tensor networks, which have allowed for major conceptual and numerical breakthroughs in quantum many body physics and quantum computation. In the proof-of-concept example explored, the tensor network approach yields several orders of magnitude speedup over vanilla Monte Carlo simulations. We take this as good evidence that the use of tensor network methods holds great promise for alleviating the computation burden of risk evaluation in the financial and other industries, thus potentially lowering the carbon footprint these simulations incur today.
