This paper investigates the application of an evolutionary algorithm, evolution strategy (ES)( $\mu+\lambda$ ) to the control design in several inhomogeneous open quantum ensembles. We apply the ES ( $\mu+\lambda$ ) to assist the sampling-based learning control (SLC) technique, by which a set of control signals is designed to drive the inhomogeneous open quantum ensemble to a given target state. We illustrate our algorithm in two-level and four-level inhomogeneous open quantum ensembles. Numerical results show the effectiveness of the proposed control algorithm. The comparison with other evolutionary algorithms such as differential evolution (DE) and genetic algorithm (GA) shows the superiority of our ES ( $\mu+\lambda$ ) both in average fidelity and stability. In a four-level open quantum ensemble, for example, the fitness error after optimization using the ES ( $\mu+\lambda$ ) is decreased by around 59% compared to DE, and the standard deviation is lowered by about 47%.This paper investigates the application of an evolutionary algorithm, evolution strategy (ES)($\mu+\lambda$) to the control design in several inhomogeneous open quantum ensembles. We apply the ES ($\mu+\lambda$) to assist the sampling-based learning control (SLC) technique, by which a set of control signals is designed to drive the inhomogeneous open quantum ensemble to a given target state. We illustrate our algorithm in two-level and four-level inhomogeneous open quantum ensembles. Numerical results show the effectiveness of the proposed control algorithm. The comparison with other evolutionary algorithms such as differential evolution (DE) and genetic algorithm (GA) shows the superiority of our ES ($\mu+\lambda$) both in average fidelity and stability. In a four-level open quantum ensemble, for example, the fitness error after optimization using the ES ($\mu+\lambda$) is decreased by around 59% compared to DE, and the standard deviation is lowered by about 47%.

Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and CyberneticsOct 9, 20220 citations

Individuality for Quantum-Inspired Individuals Based on Nonuniform Convergence Speed in Maximum Cut Problem

Yoshifumi Moriyama, Ichiro Iimura, Shigeru Nakayama

The quantum-inspired evolutionary algorithm (QEA) and QEA with a pair-swap strategy (QEAPS) have quantum-inspired individuals, where each gene is represented by a quantum bit (qubit). QEA and QEAPS iterate the evolution using the unitary transformation of probability amplitudes in each qubit, and can automatically shift the evolution from a global search to a local search. Here, the convergence speed depends on the rotation angle of each qubit toward either the |0〉 or |1〉 state vector, and the probability amplitude diverges or the population is likely to fall into a local solution if the rotation angle is too large. If the rotation angle is too small, the convergence speed may become low and the search performance will degrade. In this study, we introduce nonuniform convergence speeds into the quantum-inspired individuals and regard the convergence speed as the individual feature (individuality) of each quantum-inspired individual. Introducing the proposed individuality can eliminate the cumbersome process required to design a rotation angle while ensuring the quality of the obtained solution.The quantum-inspired evolutionary algorithm (QEA) and QEA with a pair-swap strategy (QEAPS) have quantum-inspired individuals, where each gene is represented by a quantum bit (qubit). QEA and QEAPS iterate the evolution using the unitary transformation of probability amplitudes in each qubit, and can automatically shift the evolution from a global search to a local search. Here, the convergence speed depends on the rotation angle of each qubit toward either the |0〉 or |1〉 state vector, and the probability amplitude diverges or the population is likely to fall into a local solution if the rotation angle is too large. If the rotation angle is too small, the convergence speed may become low and the search performance will degrade. In this study, we introduce nonuniform convergence speeds into the quantum-inspired individuals and regard the convergence speed as the individual feature (individuality) of each quantum-inspired individual. Introducing the proposed individuality can eliminate the cumbersome process required to design a rotation angle while ensuring the quality of the obtained solution.

Journal of Signal ProcessingNov 24, 20180 citations

A Comprehensive Learning Quantum-Inspired Evolutionary Algorithm

Yanhui Qin, Gexiang Zhang, Yuquan Li +1 more

Communications in computer and information scienceJan 1, 20128 citations

This paper investigates the application of an evolutionary algorithm, evolution strategy (ES)(

\mu+\lambda

) to the control design in several inhomogeneous open quantum ensembles. We apply the ES (

\mu+\lambda

) to assist the sampling-based learning control (SLC) technique, by which a set of control signals is designed to drive the inhomogeneous open quantum ensemble to a given target state. We illustrate our algorithm in two-level and four-level inhomogeneous open quantum ensembles. Numerical results show the effectiveness of the proposed control algorithm. The comparison with other evolutionary algorithms such as differential evolution (DE) and genetic algorithm (GA) shows the superiority of our ES (

\mu+\lambda

) both in average fidelity and stability. In a four-level open quantum ensemble, for example, the fitness error after optimization using the ES (

\mu+\lambda

) is decreased by around 59% compared to DE, and the standard deviation is lowered by about 47%.