Published February 13, 2023
| Version v1
Publication
Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer
Contributors
Others:
- Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear
- EU Flagship on Quantum Technologies
- European Commission (EC)
- Ministerio de Innovación, Ciencia y Empresa. España
- Gobierno Vasco
- Shanghai Municipal Science and Technology Commission. China
- U.S. Department of Energy, Office of Science, Office of Advance Scientific Computing Research (ASCR). U.S.
Description
Prediction of financial crashes in a complex financial network is known to be an NP-hard problem,
which means that no known algorithm can guarantee to find optimal solutions efficiently. We experimentally
explore a novel approach to this problem by using a D-Wave quantum computer, benchmarking its performance for attaining 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 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. The size of the simulation is mainly constrained by the necessity of a large quantity of physical
qubits representing a logical qubit with the correct connectivity. Our experiment paves the way to codify
this quantitative macroeconomics problem in quantum computers.
Abstract
Quantum Microwave Communication and Sensing (QMiCS) de EU Flagship on Quantum Technologies-QMiCS 820505Abstract
An Open Superconducting Quantum Computer de EU Flagship on Quantum Technologies-OpenSuperQ 820363Abstract
EU FET Open-Quromorphic 828826Abstract
Ministerio de Innovación, Ciencia y Empresa de España-Ramon y Cajal RYC-2017-22482Abstract
Gobierno Vasco-IT986-16Abstract
Shanghai Municipal Science and Technology Commission de China-18010500400 y 18ZR1415500Abstract
U.S. Department of Energy, Office of Science, Office of Advance Scientific Computing Research (ASCR)-ERKJ335Additional details
Identifiers
- URL
- https://idus.us.es/handle//11441/142667
- URN
- urn:oai:idus.us.es:11441/142667