Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer
Yongcheng Ding,
Javier Gonzalez-Conde,
Lucas Lamata,
José D. Martín-Guerrero,
Enrique Lizaso,
Samuel Mugel,
Xi Chen,
Román Orús,
Enrique Solano,
Mikel Sanz
Affiliations
Yongcheng Ding
International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Department of Physics, Shanghai University, Shanghai 200444, China
Javier Gonzalez-Conde
Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain
Lucas Lamata
Departamento de Física Atómica, Molecular y Nuclear, Universidad de Sevilla, 41080 Sevilla, Spain
José D. Martín-Guerrero
IDAL, Electronic Engineering Department, University of Valencia, Avgda. Universitat s/n, 46100 Burjassot, Spain
Enrique Lizaso
Multiverse Computing, Pio Baroja 37, 20008 San Sebastián, Spain
Samuel Mugel
Multiverse Computing, Pio Baroja 37, 20008 San Sebastián, Spain
Xi Chen
Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain
Román Orús
Multiverse Computing, Pio Baroja 37, 20008 San Sebastián, Spain
Enrique Solano
International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Department of Physics, Shanghai University, Shanghai 200444, China
Mikel Sanz
Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain
The prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can efficiently find optimal solutions. We experimentally explore a novel approach to this problem by using a D-Wave quantum annealer, benchmarking its performance for attaining a financial equilibrium. 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 into 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. The size of the simulation is mainly constrained by the necessity of a large number of physical qubits representing a logical qubit with the correct connectivity. Our experiment paves the way for the codification of this quantitative macroeconomics problem in quantum annealers.
