Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer.
Entropy (Basel)
; 25(2)2023 Feb 10.
Article
en En
| MEDLINE
| ID: mdl-36832689
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.
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Base de datos:
MEDLINE
Tipo de estudio:
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Entropy (Basel)
Año:
2023
Tipo del documento:
Article
País de afiliación:
China