Quantum harmonic oscillator model for simulation of intercity population mobility

被引:0
作者
Hu, Xu [1 ]
Qian, Lingxin [1 ]
Niu, Xiaoyu [1 ]
Gao, Ming [1 ]
Luo, Wen [1 ,2 ,3 ]
Yuan, Linwang [1 ,2 ,3 ]
Yu, Zhaoyuan [1 ,2 ,3 ]
机构
[1] Nanjing Normal Univ, Sch Geog, Nanjing 210023, Peoples R China
[2] Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Peoples R China
[3] Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China
关键词
intercity population mobility; coupling driving factors; quantum harmonic oscillator model; probability distribution pattern; optimization strategy; MIGRATION; OPPORTUNITIES; HYPOTHESIS;
D O I
10.1007/s11442-024-2213-3
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
The simulation of intercity population mobility helps to deepen the understanding of intercity population mobility and its underlying laws, which has great importance for epidemic prevention and control, social management, and even urban planning. There are many factors that affect intercity population mobility, such as socioeconomic attributes, geographical distance, and industrial structure. The complexity of the coupling among these factors makes it difficult to simulate intercity population mobility. To address this issue, we propose a novel method named the quantum harmonic oscillator model for simulation of intercity population mobility (QHO-IPM). QHO-IPM describes the intercity population mobility as being affected by coupled driving factors that work as a multioscillator-coupled quantum harmonic oscillator system, which is further transformed by the oscillation process of an oscillator, namely, the breaking point of intercity population mobility. The intercity population mobility among seven cities in the Beijing-Tianjin-Hebei region and its surrounding region is taken as an example for verifying the QHO-IPM. The experimental results show that (1) compared with the reference methods (the autoregressive integrated moving average (ARIMA) and long and short-term memory (LSTM) models), the QHO-IPM achieves better simulation performance regarding intercity population mobility in terms of both overall trend and mutation. (2) The simulation error in the QHO-IPM for different-level intercity population mobility is small and stable, which illustrates the weak sensitivity of the QHO-IPM to intercity population mobility under different structures. (3) The discussion regarding the influence degree of different driving factors reveals the significant "one dominant and multiple auxiliary" factor pattern of driving factors on intercity population mobility in the study area. The proposed method has the potential to provide valuable support for understanding intercity population mobility laws and related decision-making on intercity population mobility control.
引用
收藏
页码:459 / 482
页数:24
相关论文
共 75 条
[1]   Data-driven analysis and forecasting of highway traffic dynamics [J].
Avila, A. M. ;
Mezic, I .
NATURE COMMUNICATIONS, 2020, 11 (01)
[2]   Human mobility: Models and applications [J].
Barbosa, Hugo ;
Barthelemy, Marc ;
Ghoshal, Gourab ;
James, Charlotte R. ;
Lenormand, Maxime ;
Louail, Thomas ;
Menezes, Ronaldo ;
Ramasco, Jose J. ;
Simini, Filippo ;
Tomasini, Marcello .
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS, 2018, 734 :1-74
[3]   Complex networks from classical to quantum [J].
Biamonte, Jacob ;
Faccin, Mauro ;
De Domenico, Manlio .
COMMUNICATIONS PHYSICS, 2019, 2 (1)
[4]   Quantum mechanics and hidden superconformal symmetry [J].
Bonezzi, R. ;
Corradini, O. ;
Latini, E. ;
Waldron, A. .
PHYSICAL REVIEW D, 2017, 96 (12)
[5]   Challenges and opportunities in quantum machine learning [J].
Cerezo, M. ;
Verdon, Guillaume ;
Huang, Hsin-Yuan ;
Cincio, Lukasz ;
Coles, Patrick J. .
NATURE COMPUTATIONAL SCIENCE, 2022, 2 (09) :567-576
[6]   Estimation of COVID-19 prevalence in Italy, Spain, and France [J].
Ceylan, Zeynep .
SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 729
[7]  
Chalumuri Avinash, 2020, Procedia Computer Science, V171, P568, DOI 10.1016/j.procs.2020.04.061
[8]   Mobility network models of COVID-19 explain inequities and inform reopening [J].
Chang, Serina ;
Pierson, Emma ;
Koh, Pang Wei ;
Gerardin, Jaline ;
Redbird, Beth ;
Grusky, David ;
Leskovec, Jure .
NATURE, 2021, 589 (7840) :82-U54
[9]   Toward the first quantum simulation with quantum speedup [J].
Childs, Andrew M. ;
Maslov, Dmitri ;
Nam, Yunseong ;
Ross, Neil J. ;
Su, Yuan .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2018, 115 (38) :9456-9461
[10]   Practical quantum advantage in quantum simulation [J].
Daley, Andrew J. ;
Bloch, Immanuel ;
Kokail, Christian ;
Flannigan, Stuart ;
Pearson, Natalie ;
Troyer, Matthias ;
Zoller, Peter .
NATURE, 2022, 607 (7920) :667-676