  The LIBOR Market Model (LMM) is a widely used model for pricing interest rate
derivatives. While the Black-Scholes model is well-known for pricing stock
derivatives such as stock options, a larger portion of derivatives are based on
interest rates instead of stocks. Pricing interest rate derivatives used to be
challenging, as their previous models employed either the instantaneous
interest or forward rate that could not be directly observed in the market.
This has been much improved since LMM was raised, as it uses directly
observable interbank offered rates and is expected to be more precise.
Recently, quantum computing has been used to speed up option pricing tasks, but
rarely on structured interest rate derivatives. Given the size of the interest
rate derivatives market and the widespread use of LMM, we employ quantum
computing to price an interest rate derivative, caps, based on the LMM. As caps
pricing relates to path-dependent Monte Carlo iterations for different tenors,
which is common for many complex structured derivatives, we developed our
hybrid classical-quantum approach that applies the quantum amplitude estimation
algorithm to estimate the expectation for the last tenor. We show that our
hybrid approach still shows better convergence than pure classical Monte Carlo
methods, providing a useful case study for quantum computing with a greater
diversity of derivatives.
