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International Conference on Computer Application and System Modeling

Hybrid intelligent quantum algorithm and its application

Shanshan Wan, Chuyuan Wei, Xuhua Sun

Published Oct 1, 2010• 1 citation
Publisher

Abstract

Quantum algorithm has powerful parallel computing power and parallel optimization capabilities. It is capable of solving Np- complete problem such as Qos problem. In this paper, based on the parallelism performance of quantum algorithms, some advanced strategies are put forward to design a dynamic rotation gate. The different rotation angle and amplitude of rotation gate can be applied to adjust and control the evolutionary process. These strategies aim to enforce and guarantee the quantum's parallel computing feature and enhance its global search performance. The dynamic rotation gate based quantum algorithm improved algorithm is applied to solve the QoS constrained multicast routing problem with uncertain parameters constrained and the result shows the advanced quantum algorithm has the prominent efficiency and high searching performance and it is much robust on parameters, initial conditions and solution scale.

DOI
10.1109/iccasm.2010.5620391

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Quantum algorithm has powerful parallel computing power and parallel optimization capabilities. It is capable of solving Np- complete problem such as Qos problem. In this paper, based on the parallelism performance of quantum algorithms, some advanced strategies are put forward to design a dynamic rotation gate. The different rotation angle and amplitude of rotation gate can be applied to adjust and control the evolutionary process. These strategies aim to enforce and guarantee the quantum's parallel computing feature and enhance its global search performance. The dynamic rotation gate based quantum algorithm improved algorithm is applied to solve the QoS constrained multicast routing problem with uncertain parameters constrained and the result shows the advanced quantum algorithm has the prominent efficiency and high searching performance and it is much robust on parameters, initial conditions and solution scale.
Compared with other optimization algorithm quantum algorithm is a distinctive type for its intervention and powerful parallel computing power and parallel optimization capabilities which enables it to solve combinatorial optimization problems. Quantities of researchers cast problems to its applications and parameters settings. In this paper, we use quantum algorithm to Qos constrained multicast routing problem with uncertain parameters constrained optimization and design a dynamic rotation gate which can adjust the choice probability according to the evolutionary process and guarantee the correct direction and avoid the pre-mature phenomenon. The algorithm is compared to PBIL algorithm because both of these two algorithms are based on probability strategy. The result to simulation examples shows the quantum algorithm based on dynamic rotation gate is powerful to solve such optimization problems and the design of dynamic rotation gate is reasonable and effective.
Based on the probability amplitude ratio of the corresponding states of quantum bits, the rotation angle of quantum rotation gate is calculated adaptively and a new quantum rotation gate is constructed. A novel quantum evolutionary algorithms (NQEA), whose core is that the designed quantum rotation gate is used as evolutionary strategy is introduced, rapid convergence and good global search capability characterize the performance of NQEA, and NQEA is able to possess the two characteristics of exploration and exploitation, simultaneously. NQEA is applied on the physical distribution vehicle routing problem - a typical combinatorial optimization problem, the results show that NQEA is superior to the previous quantum evolutionary algorithm as well as other conventional genetic algorithms
The method of calculating rotation angle of quantum rotation gate plays an important role to the performance of quantum evolutionary algorithm (QEA). This paper proposes an improved quantum evolutionary algorithm (IQEA), whose core is that a new approach of adaptive calculating rotation angle of quantum rotation gate is designed on the basis of the probability amplitude ratio of the corresponding states. Rapid convergence and good global search capability characterize the performance of IQEA. Based on a typical combinatorial optimization problem - 0/1 knapsack problems, the influence of the relative parameter to the performance of IQEA is demonstrated, and then comparing experiments have been done. The results show that IQEA is superior to the previous quantum evolutionary algorithm.
The method of calculating the rotational angle of a quantum rotary gate palys an important role in the performance of quantum evolutionary algorithm(QEA). An improved quantum evolutionary algorithm(IQEA) is proposed, which adaptively calculates the rotational angle of quantum rotary gate based on the probability amplitude ratio of the corresponding states. Rapid convergence and good global search capability characterize the performance of IQEA. The influence of the relative parameter to the performance of IQEA is demonstrated on 0/1 knapsack problem, and then IQEA is applied to a real distribution routing problem, the results show that IQEA is superior to the previous quantum evolutionary algorithm and other conventional evolutionary algorithms.
In this paper, quantum rotate gate is improved, which is the main operation in the population update of the traditional quantum evolutionary algorithm. A new rotation angle is defined, preventing the algorithm from easily falling into local optimum state in the middle and late term. Based on the characteristics of TSP, a modified quantum rotate gate is proposed in this paper to adaptively adjust the rotation angle, according to the evolution generations and the adapt to degree of the value to adaptive dynamic adjustment of the rotation angle, resulting a better global search capability. At the same time, in order to prevent the extramalisation of the probability amplitudes α and β falling into local optimal algorithm, this paper adopted the Hε gate on the probability amplitude of the rotation to make the corrective manipulation. The comparative experimental results showed that the algorithm's stability and accuracy have been greatly improved in solving the TSP problem, compared with the conventional quantum evolutionary algorithm.
In this paper, Quantum Rotate Gate is improved, which is the main operation in the Population Update of the traditional Quantum Evolutionary Algorithm. A new rotation angle is defined, preventing the algorithm from easily falling into local optimum state in the middle and late term. Based on the characteristics of TSP, a modified quantum rotate gate is proposed in this paper to adaptively adjust the rotation angle, according to the evolution generations and the adapt to degree of the value to adaptive dynamic adjustment of the rotation angle, resulting a better global search capability. At the same time, in order to prevent the extramalization of the probability amplitudes and falling into local optimal algorithm, this paper adopted the H ε gate on the probability amplitude of the rotation to make the corrective manipulation. The comparative experimental results showed that the algorithm's stability and accuracy have been greatly improved in solving the TSP problem, compared with the conventional quantum evolutionary algorithm.