Financial risk analysis using Quantum Computing

Unpredictability and Risk management are some of the key issues that are plaguing the financial world today. We explore the use of quantum computing in prediction and risk assessment using a toy-model of a network of financial institutions. To this end the prediction of the behaviour of the toy model is mapped on to an optimization problem that we attempt to solve with the Quantum Approximate Optimisation Algorithm (QAOA). QAOA is a powerful algorithm for gate based quantum computers that can be used to solve combinatorial optimisation problems. We considerably improve on prior encodings of the problem with the help of Walsh functions. Such an encoding also significantly reduces the circuit depth and the qubit resources required when compared to prior works. We preform extensive numerical experiments evaluating the properties of the toy model in a wide range of parameters. Thus our work provides a viable, scalable and efficient solution to avert the failures and manage risks in reasonable time frame.