Quantitative finance will benefit significantly from the advancements offered by quantum and quantum-inspired methods. A noteworthy method that's taking off is the use of Tensor Networks, sitting at the intersection of quantum computing and machine learning, providing logarithmic complexity for solving a broad spectrum of important numerical challenges. Tensor Networks can be deployed on both conventional CPU and GPU architectures, with potential future implementations on quantum processors, making it a versatile and accessible solution for current financial models. There is high value potential across 3 areas, especially for innovative Global Heads of Markets and Risk: - **Option Pricing:** Improving the efficiency and cost-effectiveness of pricing and hedging for Autocallables, powered by a Tensor Network Monte Carlo engine. - **XVA:** Applicable to counterparty risk and margin valuation adjustments, through allowing for more intricate time discretisation and a higher volume of external Monte Carlo paths that enable the adoption of more refined models. - **Deep Hedging and Artificial Data Generation:** Dynamic portfolio hedging strategies through Reinforcement Learning and enhancing risk management through the creation of synthetic data via diffusion models. #boat