Year: 2023
Type: article
Abstract: 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.
Source: Entropy
DOI: 10.3390/e25020323
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,224
Funders: U.S. Department of Energy, European Commission, Eusko Jaurlaritza, Science and Technology Commission of Shanghai Municipality, Euskal Herriko Unibertsitatea, Junta de Andalucía, Office of Science, European Regional Development Fund, Agencia Estatal de Investigación, Advanced Scientific Computing Research
Awards: G1622782223, G1684677388, G1778906655, G2083684644, G2942266459, G3209485537, G3480869486, G3777950849, G3889733476, G3987967833, G4484594549, G4523840175, G4681747406, G5763886264, G5777715921, G6351756048, G6604905309, G67347242, G6917537158, G7084143925, G7421034968, G7930517882, G8045666043, G8132232913, G8953647516, G8985965465
OpenAlex ID: W4319985848