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Quantum circuit mapping based on discrete particle swarm optimization and deep reinforcement learning

swarm AND EVOLUTIONARY COMPUTATION 2025年 95卷

作者： Li, Yang-Zhi Liu, Wen Xu, Guo-Sheng Li, Mao-Duo Chen, Kai He, Shou-Li Commun Univ China Sch Comp & Cyber Sci Beijing 100024 Peoples R China Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China Commun Univ China Lab Convergent Media & Intelligent Technol Minist Educ Beijing 100034 Peoples R China Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China

Current quantum devices are constrained by a limited number of physical qubits and their sparse connectivity. When executing logical quantum circuits on these devices, it is necessary to map them into equivalent circuits adhering to these constraints. The initial allocation of logical qubits to physical ones and the strategic addition of SWAP gates to meet connectivity requirements are critical decisions impacting the performance of mapped circuits. To tackle these challenges, this paper presents a novel quantum circuit mapping method that integrates discrete particle swarm optimization and deep reinforcement learning. Initially, a qubit allocation algorithm using discrete particle swarm optimization with a sorting selection strategy quickly maps logical to physical qubits. Then, an enhanced double deep-Q-network based quantum gate scheduling algorithm with an action space search strategy obtains a SWAP addition scheme that results in shallower depth and fewer additional quantum gates. Comparisons on benchmark datasets (B131 and B114) and the IBM Q20 quantum device show that our method outperforms others regarding algorithm runtime, mapped circuit depth, and the number of added SWAP gates. It also demonstrates scalability on large-scale circuits and IBM Q127. Compared to heuristic methods (subgraph isomorphism and filtered depth-limited search based quantum circuit mapping algorithms), it reduces the average number of added SWAP gates by 29.1% and the average runtime by 41.95%. Compared to a recent reinforcement learning method using Monte Carlo Tree Search for mapping, it decreases the average added depth by 26.05% and the average number of added SWAP gates by 25.58%. Moreover, compared to commercial compilers tket and qiskit, the proposed method results in 14.73% and 16.55% fewer SWAP gates, respectively.

关键词： Quantum circuit mapping Deep reinforcement learning discrete particle swarm optimization Qubit allocation Gate scheduling SWAP gate

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discrete particle swarm optimization based on estimation of distribution for polygonal approximation problems

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第5期36卷 9398-9408页

作者： Wang, Jiahai Kuang, Zhanghui Xu, Xinshun Zhou, Yalan Sun Yat Sen Univ Dept Comp Sci Guangzhou 510275 Guangdong Peoples R China Shandong Univ Middle Shunhua Rd Sch Comp Sci & Technol Jinan 250101 Shandong Peoples R China Guangdong Univ Business Studies Informat Sci Sch Guangzhou 510320 Guangdong Peoples R China

The polygonal approximation is an important topic in the area of pattern recognition, computer graphics and computer vision. This paper presents a novel discrete particle swarm optimization algorithm based on estimation of distribution (DPSO-EDA), for two types of polygonal approximation problems. Estimation of distribution algorithms sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The DPSO-EDA incorporates the global statistical information collected front local best solution of all particles into the particle swarm optimization and therefore each particle has comprehensive learning and search ability. Further, constraint handling methods based on the split-and-merge local search is introduced to satisfy the constraints of the two types of problems. Simulation results on several benchmark problems show that the DPSO-EDA is better than previous methods such as genetic algorithm, tabu search, particle swarm optimization, and ant colony optimization. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： discrete particle swarm optimization Estimation of distribution Split-and-merge Polygonal approximation problem

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discrete particle swarm optimization based on estimation of distribution for terminal assignment problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2011年第4期60卷 566-575页

作者： Wang, Jiahai Cai, Yiqiao Zhou, Yalan Wang, Ronglong Li, Caiwei Sun Yat Sen Univ Dept Comp Sci Guangzhou 510275 Guangdong Peoples R China Guangdong Univ Business Studies Informat Sci Sch Guangzhou 510320 Guangdong Peoples R China Univ Fukui Fac Engn Fukui 9108507 Japan

Terminal assignment problem (TEAP) is to determine minimum cost links to form a network by connecting a given set of terminals to a given collection of concentrators. This paper presents a novel discrete particle swarm optimization (PSO) based on estimation of distribution (EDA), named DPSO-EDA, for TEAP. EDAs sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The DPSO-EDA incorporates the global statistical information collected from personal best solutions of all particles into the PSO, and therefore each particle has comprehensive learning and search ability. In the DPSO-EDA, a modified constraint handling method based on Hopfield neural network (HNN) is also introduced to fit nicely into the framework of the PSO and thus utilize the merit of the PSO. The DPSO-EDA adopts the asynchronous updating scheme. Further, the DPSO-EDA is applied to a problem directly related to TEAP, the task assignment problem (TAAP), in order to show that the DPSO-EDA can be generalized to other related combinatorial optimization problems. Simulation results on several problem instances show that the DPSO-EDA is better than previous methods. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： discrete particle swarm optimization Estimation of distribution Terminal assignment problem Task assignment problem Combinatorial optimization

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discrete particle swarm optimization algorithms for two variants of the static data segment location problem

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APPLIED INTELLIGENCE 2018年第3期48卷 771-790页

作者： Sen, Goutam Krishnamoorthy, Mohan IIT Kharagpur Ind & Syst Engn Kharagpur 721302 W Bengal India Monash Univ Dept Mech & Aerosp Engn Clayton Vic 3168 Australia

We consider the static data segment location problem in information networks. This problem was introduced by Sen et al. (Comput Oper Res, 62:282-295 2015). We consider the problem of optimally locating large volumes of digital content that is accessed via a distributed network. A database is pre-partitioned into multiple segments and the problem is one of placing these segments at servers located in different regions. We need to jointly consider four specific subproblems: (1) the problem of locating servers in the network, (2) the problem of allocating specific data segments to each of the servers, (3) the problem of assigning users to the servers based on their query patterns, and, (4) routing queries through the network. We consider two variants of this problem depending on the topology of the network through which the servers are connected: a mesh topology and a tree topology. In this paper, we develop a solution approach based on a discrete particle swarm optimization approach. We demonstrate the superiority of our approach by comparing its performance against solutions to benchmark instances obtained previously using a simulated annealing approach (Networks, 68(1):4-22 2016b).

关键词： Content distribution networks Database segment location Hub location discrete particle swarm optimization

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discrete particle swarm optimization for the minimum labelling Steiner tree problem

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NATURAL COMPUTING 2010年第1期9卷 29-46页

作者： Consoli, Sergio Andres Moreno-Perez, Jose Darby-Dowman, Kenneth Mladenovic, Nenad Brunel Univ CARISMA & NET ACE Sch Informat Syst Comp & Math Uxbridge UB8 3PH Middx England

particle swarm optimization is a population-based method inspired by the social behaviour of individuals inside swarms in nature. Solutions of the problem are modelled as members of the swarm which fly in the solution space. The improvement of the swarm is obtained from the continuous movement of the particles that constitute the swarm submitted to the effect of inertia and the attraction of the members who lead the swarm. This work focuses on a recent discrete particle swarm optimization for combinatorial optimization, called Jumping particle swarm optimization. Its effectiveness is illustrated on the minimum labelling Steiner tree problem: given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes, whose edges have the smallest number of distinct labels.

关键词： Combinatorial optimization discrete particle swarm optimization Graphs and networks Heuristics Minimum labelling Steiner tree problem

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discrete particle swarm optimization for the Orienteering Problem

Discrete Particle Swarm Optimization for the Orienteering Pr...

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2010 IEEE World Congress on Computational Intelligence

作者： Sevkli, Zulal Sevilgen, F. Erdogan Fatih Univ Dept Comp Engn TR-34500 Istanbul Turkey Gebze Inst Technol Dept Comp Engn Gebze Turkey

ISBN: (纸本)9781424481262

In this paper a novel discrete particle swarm optimization (PSO) algorithm is proposed to solve the Orienteering Problem (OP). discrete evolution is achieved by re-defining all operators and operands used in PSO. To obtain better results, Strengthened-PSO which improves both exploration and exploitation during the search process is employed for experimental evaluation. Our proposed algorithm either achieves or improves the best known solutions compared to previous heuristics for the OP.

关键词： discrete evolution discrete particle swarm optimization orienteering problem particle swarm optimisation

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discrete particle swarm optimization Based Filter Feature Selection Technique for the Severity of Road Traffic Accident Prediction 4th

Discrete Particle Swarm Optimization Based Filter Feature Se...

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4th International Conference of Reliable Information and Communication Technology (IRICT)

作者： Haruna, Lawal Sallehuddin, Roselina Radzi, Haizan Mohammed Univ Technol Malaysia Comp Sci Dept Skudai Malaysia Fed Univ Dutsinma Comp Sci Dept Dutsin Ma Nigeria

ISBN: (纸本)9783030335823;9783030335816

The rate of traffic accident is increasing worldwide, which resulted to severe injuries, fatalities and economic loses. More than 1.25 million people lose their lives every year and 50 million are non-fatally wounded in road accident worldwide. The severity of road traffic accident are attributed to many factors which include human (age, gender), road (road alignment, roads surface), and environment (moisture, weather) among others. Nevertheless, some factors are more significant than others in increasing the severity level of injuries sustained by victims of accident. In previous studies, few feature selection techniques were used to get the most relevant and non-redundant features prior to prediction of severity of road traffic accident. Among the techniques of feature selection which includes filter, wrapper, embedded and hybrid, only filter technique has been considered. However, the filter feature selection techniques (filter correlation, mutual information) are inadequate at finding optimal features. Additionally, the redundant feature doesn't only affect the performance of the classification algorithm but also computational time. To this end, this paper, Proposed a hybrid discrete particle swarm optimization with Fast Correlation Based Feature Selection (DPSO_FCBF) technique for selecting the most significant features for the prediction of severity of road traffic accident. In this technique, the discrete particle swarm optimization (DPSO) search for the optimum values and the fast correlation based feature selection (FCBF) evaluate the selected optimum values to select the most relevant features, then DPSO update the personal best (Pbest) and global best (Gbest) for the optimal solution. The proposed technique has been evaluated using benchmark road traffic accident dataset. The experimental result are compared with the result obtained from DPSO and FCBF individually, the proposed technique showed better result in terms of number of features selected, predic

关键词： discrete particle swarm optimization Feature selection Prediction Filter Severity of road traffic accident

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Novel discrete particle swarm optimization Based on Huge Value Penalty for Solving Engineering Problem

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Chinese Journal of Mechanical Engineering 2009年第3期22卷 410-418页

作者： YU Ying YU Xiaochun LI Yongsheng Pharmacy College China Pharmaceutical University Nanjing 210009 China College of Mechanical Engineering Nanjing University of Technology Nanjing 210009 China

For the purpose of solving the engineering constrained discrete optimization problem, a novel discrete particle swarm optimization（DPSO） is proposed. The proposed novel DPSO is based on the idea of normal particle swarm optimization（PSO）, but deals with the variables as discrete type, the discrete optimum solution is found through updating the location of discrete variable. To avoid long calculation time and improve the efficiency of algorithm, scheme of constraint level and huge value penalty are proposed to deal with the constraints, the stratagem of reproducing the new particles and best keeping model of particle are employed to increase the diversity of particles. The validity of the proposed DPSO is examined by benchmark numerical examples, the results show that the novel DPSO has great advantages over current algorithm. The optimum designs of the 100-1 500 mm bellows under 0.25 MPa are fulfilled by DPSO. Comparing the optimization results with the bellows in-service, optimization results by discrete penalty particle swarm optimization（DPPSO） and theory solution, the comparison result shows that the global discrete optima of bellows are obtained by proposed DPSO, and confirms that the proposed novel DPSO and schemes can be used to solve the engineering constrained discrete problem successfully.

关键词： discrete particle swarm optimization location updating scheme of constraints level huge value penalty optimization design bellows

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discrete particle swarm optimization to network reconfiguration for loss reduction and load balancing

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2008年第5期36卷 513-524页

作者： Sivanagaraju, S. Rao, J. Viswanatha Raju, P. Sangameswara JNTU Coll Engn Dept Elect Engn Anantapur Andhra Pradesh India MITS Coll Engn Dept Elect Engn Madanapalle India

particle swarm optimization (PSO) has emerged as a useful optimization tool for handling non-linear programming problems. Various modifications to the basic method have been proposed with a view to enhance speed and robustness. This method has been applied successfully on some benchmark mathematical problems. A few PSO applications have been reported on real-world problems, such as network reconfiguration for loss reduction, load balancing, and even capacitor placement in distribution systems being discrete in nature. PSO is modified to discrete particle swarm optimization (DPSO). The results of DPSO are compared with the results of the genetic algorithm.

关键词： network reconfiguration load balancing particle swarm optimization discrete particle swarm optimization

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discrete particle swarm optimization FOR THE ORIENTEERING PROBLEM

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2011年第2期18卷 92-102页

作者： Muthuswamy, Shanthi Lam, Sarah No Illinois Univ Dept Technol De Kalb IL 60115 USA SUNY Binghamton Syst Sci & Ind Engn Dept Binghamton NY 13902 USA

discrete particle swarm optimization (DPSO) is gaining popularity in the area of combinatorial optimization in the recent past due to its simplicity in coding and consistency in performance. A DPSO algorithm has been developed for orienteering problem (OP) which has been shown to have many practical applications. It uses reduced variable neighborhood search as a local search tool. The DPSO algorithm was compared with ten heuristic models from the literature using benchmark problems. The results show that the DPSO algorithm is a robust algorithm that can optimally solve the well known OP test problems.

关键词： discrete particle swarm optimization reduced variable neighborhood search orienteering problem

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