@article{nokey,
title = {Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer},
author = {Ding, Y. and Gonzalez-Conde, J. and Lamata, L., Martín-Guerrero, J. D. and Lizaso, E. and Mugel, S. and Chen, X. and Orús, R. and Solano, E. and Sanz, M.
},
url = {https://quantumspain-project.es/wp-content/uploads/2023/05/entropy-25-00323-v2-1.pdf},
doi = {doi.org/10.3390/e25020323},
isbn = {1099-4300},
journal = {Entropy},
volume = {25},
issue = {2},
pages = {323},
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.},
keywords = {quantum, quantum annealer, UV},
pubstate = {published},
tppubtype = {article}
}