Ding Yongcheng, Gonzalez-Conde Javier, Lamata Lucas, Martín-Guerrero José D, Lizaso Enrique, Mugel Samuel, Chen Xi, Orús Román, Solano Enrique, Sanz Mikel
International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Department of Physics, Shanghai University, Shanghai 200444, China.
Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain.
Entropy (Basel). 2023 Feb 10;25(2):323. doi: 10.3390/e25020323.
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.
在复杂金融网络中预测金融崩溃是一个NP难问题，这意味着没有已知算法能够有效地找到最优解。我们通过使用D-Wave量子退火器对这一问题进行了实验性探索，对其实现金融均衡的性能进行了基准测试。具体而言，将一个非线性金融模型的均衡条件嵌入到一个高阶无约束二元优化（HUBO）问题中，然后将其转化为一个最多具有两比特相互作用的自旋1/2哈密顿量。因此，该问题等同于寻找一个相互作用自旋哈密顿量的基态，这可以用量子退火器进行近似。模拟的规模主要受限于用具有正确连接性的大量物理比特来表示一个逻辑比特的必要性。我们的实验为在量子退火器中对这一定量宏观经济学问题进行编码铺平了道路。
