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

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.

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Quantum Microwave Communication and Sensing (QMiCS) de EU Flagship on Quantum Technologies-QMiCS 820505

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An Open Superconducting Quantum Computer de EU Flagship on Quantum Technologies-OpenSuperQ 820363

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EU FET Open-Quromorphic 828826

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Ministerio de Innovación, Ciencia y Empresa de España-Ramon y Cajal RYC-2017-22482

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Gobierno Vasco-IT986-16

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Shanghai Municipal Science and Technology Commission de China-18010500400 y 18ZR1415500

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U.S. Department of Energy, Office of Science, Office of Advance Scientific Computing Research (ASCR)-ERKJ335

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