Towards Prediction of Financial Crashes with a D-Wave Quantum Computer

Prediction of financial crashes in a complex financial network is known to be an NP-hard problem, i.e., a problem which cannot be solved efficiently with a classical computer. We experimentally explore a novel approach to this problem by using a D-Wave quantum computer to obtain financial equilibrium more efficiently. To be specific, the equilibrium condition of a nonlinear financial model is embedded into a higher-order unconstrained binary optimization (HUBO) problem, which is then transformed to a spin-$1/2$ Hamiltonian with at most two-qubit interactions. The problem is thus equivalent to finding the ground state of an interacting spin Hamiltonian, which can be approximated with a quantum annealer. Our experiment paves the way to study quantitative macroeconomics, enlarging the number of problems that can be handled by current quantum computers

Solving combinatorial optimization problems on D-Wave annealers

The D-Wave machine is a powerful annealer based on superconducting qubits to attack complex optimization tasks. In this thesis, the student will first learn the physical principle of quantum annealing and how the D-Wave processor implements and exploits it. In the second part, the student will learn how to formulate and solve a simple optimization problem using the D-Wave annealer. Depending on the development pace, problems of increasing complexity will be analized. Throughout this work, the student will acquire the needed theoretical and practical knowledge to face more general and sophisticated optimization tasks using a D-Wave annealer