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RESEARCH PRODUCT

Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer

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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. The authors acknowledge financial support from the project grant PID2021-125823NA-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” and “ERDF Invest in your Future”, Basque Government through Grant No. IT1470-22, the QUANTEK project from ELKARTEK program (KK-2021/00070), as well as from QMiCS (820505) and OpenSuperQ (820363) of the EU Flagship on Quantum Technologies, Spanish Ramón y Cajal Grants RYC-2017-22482 and RYC-2020-030503-I, UPV/EHU PhD Grant 20/276, as well as from the EU FET-Open projects Quromorphic (828826) and EPIQUS (899368), Junta de Andalucía (P20-00617 and US-1380840), Valencian Government with reference number CIAICO/2021/184, Spanish Ministry of Economic Affairs and Digital Transformation through the QUANTUM ENIA project call—Quantum Spain project, and the European Union through the Recovery, Transformation and Resilience Plan—NextGenerationEU within the framework of the Digital Spain 2025 Agenda.