  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.
