Quantum state tomography (QST) is a technique used to reconstruct the density matrix of unknown quantum states based on experimentally obtained measurements. QST is a fundamental tool in the field of quantum in-format...
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Quantum state tomography (QST) is a technique used to reconstruct the density matrix of unknown quantum states based on experimentally obtained measurements. QST is a fundamental tool in the field of quantum in-formation and quantum technology. It is commonly employed to assess the quality and limitations of experi-mental platforms. However, the density matrix reconstructed using the standard QST method often fails to guarantee semi-positive definiteness, which is physically unacceptable, due to limitations imposed by the randomness of quantum state measurements, noise in practical applications, and the number of measurements. To address this issue, a method is proposed that combines maximum likelihood (ML) estimation with population intelligence optimization algorithms. First, the issue of guaranteeing the semi-positive definiteness of the density matrix reconstructed using standard QST methods is analyzed. Subsequently, the ML method is introduced, and four commonly used population intelligence optimization algorithms are applied to find the density matrix that maximizes the likelihood of reproducing the experimental measurements. Finally, the superiority of the proposed method is demonstrated using an IBM Quantum (IBMQ) processor in scenarios involving separable and entangled states of multiple qubits, and compared with the standard QST method.
Traditional intelligent algorithm cannot optimize multiple parameters at the same time,so the optimization effect is *** order to solve such problem,swarm intelligenceoptimizationalgorithm is used in the antenna ***...
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Traditional intelligent algorithm cannot optimize multiple parameters at the same time,so the optimization effect is *** order to solve such problem,swarm intelligenceoptimizationalgorithm is used in the antenna *** analyzing the influence parameters,we design the objective function and adopt chaos optimizationalgorithm to optimize the initial population selection of particle swarm ***,according to the actual design requirements,the improved particle swarm optimization(PSO) algorithm is used to solve multiple optimization problems,and the optimal design of the antenna is also *** simulation results show that our scheme optimizes the design effect,and the antenna has larger directional gain,which effectively improves the comprehensive performance of antenna.
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