Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Hyper Trajectory Optimization with Particle Swarm Optimization (PSO) Algorithm in Quantum Pattern Space

Sedat Ozturk¹, Dublin City University

Published Jan 1, 2024• 0 citations
Publisher
DOI
10.13140/rg.2.2.33534.14409

Related papers

Hybrid quantum particle swarm optimization algorithm and its application

Yukun Wang, Xuebo Chen

Science China Information Sciences•Sep 10, 2019•28 citations

A Modified Quantum-Inspired Particle Swarm Optimization Algorithm

Ling Wang, Mingde Zhang, Qun Niu +1 more

Lecture notes in computer science•Jan 1, 2011•5 citations

An improved quantum-behaved particle swarm optimization algorithm

Panchi Li, Hong Xiao

Applied Intelligence•Sep 17, 2013•27 citations

Particle Swarm Optimization for Quantum Circuit Synthesis: Performance Analysis and Insights

Mirza Hizriyan Nubli Hidayat, Tan Chye Cheah

This paper discusses how particle swarm optimization (PSO) can be used to generate quantum circuits to solve an instance of the MaxOne problem. It then analyzes previous studies on evolutionary algorithms for circuit synthesis. With a brief introduction to PSO, including its parameters and algorithm flow, the paper focuses on a method of quantum circuit encoding and representation as PSO parameters. The fitness evaluation used in this paper is the MaxOne problem. The paper presents experimental results that compare different learning abilities and inertia weight variations in the PSO algorithm. A comparison is further made between the PSO algorithm and a genetic algorithm for quantum circuit synthesis. The results suggest PSO converges more quickly to the optimal solution.

arXiv:2507.02898•Jun 22, 2025•0 citations

Modified Log-BP decoding algorithm combined with PSO for Low-Density Parity Check Codes

Xiali Yan, Wenquan Feng, Qi Zhao +1 more

Message-Passing is the main decoding algorithm for Low-Density Parity Check (LDPC) Codes, in which Logarithmic Belief Propagation (Log-BP) algorithm is the most popularly used one. Particle Swarm Optimization (PSO) can also be regarded as an optimization algorithm by message-passing, which makes it possible to combine Log-BP algorithm with PSO. PSO was introduced into Log-BP algorithm by regarding all messages that bit nodes send to check nodes as the swarm with each message individually as a particle. Move the particles around in the search-space by certain iterations in each BP iteration processing, guided by the two best current values respectively found by particles themselves and that of the whole swarm. Those two best values were continually updated in each PSO iteration. In this way, the output of decoder is more consistent with accurate input sequence, in other words, the decoder performance was improved. Simulation results show that this new algorithm combined with Log-BP and PSO can get an additional 0.1 dB coding gain at Bit Error Rate (BER) of 10-5compared with Log-BP algorithm.

International Conference on Advanced Computer Theory and Engineering•Aug 1, 2010•0 citations

An improved Quantum Particle Swarm Optimization and its application

Xuan Jiao, Ming Huang

IEEE Conference Proceedings•Jan 1, 2017•1 citation
This paper discusses how particle swarm optimization (PSO) can be used to generate quantum circuits to solve an instance of the MaxOne problem. It then analyzes previous studies on evolutionary algorithms for circuit synthesis. With a brief introduction to PSO, including its parameters and algorithm flow, the paper focuses on a method of quantum circuit encoding and representation as PSO parameters. The fitness evaluation used in this paper is the MaxOne problem. The paper presents experimental results that compare different learning abilities and inertia weight variations in the PSO algorithm. A comparison is further made between the PSO algorithm and a genetic algorithm for quantum circuit synthesis. The results suggest PSO converges more quickly to the optimal solution.
Message-Passing is the main decoding algorithm for Low-Density Parity Check (LDPC) Codes, in which Logarithmic Belief Propagation (Log-BP) algorithm is the most popularly used one. Particle Swarm Optimization (PSO) can also be regarded as an optimization algorithm by message-passing, which makes it possible to combine Log-BP algorithm with PSO. PSO was introduced into Log-BP algorithm by regarding all messages that bit nodes send to check nodes as the swarm with each message individually as a particle. Move the particles around in the search-space by certain iterations in each BP iteration processing, guided by the two best current values respectively found by particles themselves and that of the whole swarm. Those two best values were continually updated in each PSO iteration. In this way, the output of decoder is more consistent with accurate input sequence, in other words, the decoder performance was improved. Simulation results show that this new algorithm combined with Log-BP and PSO can get an additional 0.1 dB coding gain at Bit Error Rate (BER) of 10-5compared with Log-BP algorithm.