Optimizing adiabatic quantum pathways via a learning algorithm

Yang, Xiaodongvi1,2,3; Liu, Ran1,2,3; Li, Jun4,5; Peng, Xinhua1,2,3,6

2020-07-16

10.1103/PhysRevA.102.012614

PHYSICAL REVIEW A   影响因子和分区

1050-2947

1094-1622

Designing proper time-dependent control fields for slowly varying the system to the ground state that encodes the problem solution is crucial for adiabatic quantum computation. However, inevitable perturbations in real applications demand us to accelerate the evolution so that the adiabatic errors can be prevented from accumulation. Here, by treating this trade-off task as a multiobjective optimization problem, we propose a gradient-free learning algorithm with pulse smoothing technique to search optimal adiabatic quantum pathways and apply it to the Landau-Zener Hamiltonian and Grover search Hamiltonian. Numerical comparisons with a linear schedule, local adiabatic theorem induced schedule, and gradient-based algorithm searched schedule reveal that the proposed method can achieve significant performance improvements in terms of the adiabatic time and the instantaneous ground-state population maintenance. The proposed method can be used to solve more complex and real adiabatic quantum computation problems.

SCI ; EI

英语

通讯

National Key Research and Development Program of China[2018YFA0306600] ; National Natural Science Foundation of China[11975117][11605005][11875159][U1801661] ; Anhui Initiative in Quantum Information Technologies[AHY050000] ; Guangdong Provincial Key Laboratory[2019B121203002]

Optics ; Physics

Optics ; Physics, Atomic, Molecular & Chemical

WOS:000550189300003

AMER PHYSICAL SOC

20203108989224

Economic and social effects ; Hamiltonians ; Population statistics ; Scheduling ; Quantum computers ; Computation theory ; Computational efficiency ; Multiobjective optimization ; Quantum theory ; Learning algorithms ; Numerical methods ; Scheduling algorithms

Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Computer Systems and Equipment:722 ; Machine Learning:723.4.2 ; Management:912.2 ; Optimization Techniques:921.5 ; Numerical Methods:921.6 ; Quantum Theory; Quantum Mechanics:931.4 ; Social Sciences:971

PHYSICS

Web of Science

1.Univ Sci & Technol China, Hefei Natl Lab Phys Sci Microscale, Hefei 230026, Peoples R China
2.Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China
3.Univ Sci & Technol China, CAS Key Lab Microscale Magnet Resonance, Hefei 230026, Peoples R China
4.Southern Univ Sci & Technol, Shenzhen Inst Quantum Sci & Engn, Shenzhen 518055, Peoples R China
5.Southern Univ Sci & Technol, Guangdong Prov Key Lab Quantum Sci & Engn, Shenzhen 518055, Peoples R China
6.Univ Sci & Technol China, Synerget Innovat Ctr Quantum Informat & Quantum P, Hefei 230026, Peoples R China

Yang, Xiaodongvi,Liu, Ran,Li, Jun,et al. Optimizing adiabatic quantum pathways via a learning algorithm[J]. PHYSICAL REVIEW A,2020,102(1).

Yang, Xiaodongvi,Liu, Ran,Li, Jun,&Peng, Xinhua.(2020).Optimizing adiabatic quantum pathways via a learning algorithm.PHYSICAL REVIEW A,102(1).

Yang, Xiaodongvi,et al."Optimizing adiabatic quantum pathways via a learning algorithm".PHYSICAL REVIEW A 102.1(2020).