  A critical problem in the financial world deals with the management of risk,
from regulatory risk to portfolio risk. Many such problems involve the analysis
of securities modelled by complex dynamics that cannot be captured
analytically, and hence rely on numerical techniques that simulate the
stochastic nature of the underlying variables. These techniques may be
computationally difficult or demanding. Hence, improving these methods offers a
variety of opportunities for quantum algorithms. In this work, we study the
problem of Credit Valuation Adjustments (CVAs) which have significant
importance in the valuation of derivative portfolios. We propose quantum
algorithms that accelerate statistical sampling processes to approximate the
CVA under different measures of dispersion, using known techniques in Quantum
Monte Carlo (QMC) and analyse the conditions under which we may employ these
techniques.
