Towards Prediction of Financial Crashes with a D-Wave Quantum Computer

• URL: http://arxiv.org/abs/1904.05808v4
• Date: Thu, 16 Feb 2023 08:39:39 GMT
• Title: Towards Prediction of Financial Crashes with a D-Wave Quantum Computer
• Authors: Yongcheng Ding, Javier Gonzalez-Conde, Lucas Lamata, Jos\'e D. Mart\'in-Guerrero, Enrique Lizaso, Samuel Mugel, Xi Chen, Rom\'an Or\'us, Enrique Solano, Mikel Sanz
• Abstract summary: Prediction of financial crashes in a complex financial network is known to be an NP-hard problem. We experimentally explore a novel approach to this problem by using a D-Wave quantum computer.
• Score: 2.733342606024131
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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