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

International Conference on Artificial Intelligence and Big Data (ICAIBD)

Overcoming Performance Inequity in Quantum Dynamics Framework for Optimization of Symmetric and Asymmetric Double-Well Function

Lintao Lv, Peng Wang, Xu Wang

Published May 26, 2023• 2 citations
Publisher

Abstract

In recent years, a study focused on the quantum dynamics framework (QDF) process has revealed significant, random structural bias in QDF’s optimization of symmetric and asymmetric forms of double-well functions (DWF). Such bias means that the algorithm tends to favor specific solutions in the search space while ignoring other possible solutions. This tendency may cause the algorithm to become trapped in local optima, preventing it from finding global optima and ultimately compromising its performance. To address this performance gap, this paper proposes a dimension-decoupling sampling mechanism and random-neighborhood sampling mechanism. The proposed mechanism effectively reduces the coupling between dimensions during the optimization of the objective function and increases the search space diversity, thereby mitigating the structural bias of the algorithm. It successfully solves the issue of performance degradation observed in the optimization of symmetric double-well functions. We refer to the modified algorithm as the dimension-decoupling and random-neighborhood quantum dynamics framework (DR-QDF).

DOI
10.1109/icaibd57115.2023.10206226

Related papers

Quantum Dynamics Framework with Quantum Tunneling Effect for Numerical Optimization

Quan Tang, Peng Wang

In recent years, optimization algorithms have developed rapidly, especially those which introduce quantum ideas, which perform excellently. Inspired by quantum thought, this paper proposes a quantum dynamics framework (QDF) which converts optimization problems into the problem of the constrained ground state of the quantum system and analyzes optimization algorithms by simulating the dynamic evolution process of physical optimization systems in the ground state. Potential energy equivalence and Taylor expansion are performed on the objective function to obtain the basic iterative operations of optimization algorithms. Furthermore, a quantum dynamics framework based on the quantum tunneling effect (QDF-TE) is proposed which adopts dynamic multiple group collaborative sampling to improve the quantum tunneling effect of the QDF, thereby increasing the population diversity and improving algorithm performance. The probability distribution of solutions can be visually observed through the evolution of the wave function, which also indicates that the QDF-TE can strengthen the tunneling effect. The QDF-TE was evaluated on the CEC 2017 test suite and shown to be competitive with other heuristic optimization algorithms. The experimental results reveal the effectiveness of introducing a quantum mechanism to analyze and improve optimization algorithms.

Entropy•Feb 1, 2025•4 citations

Exploring Potential Barrier Estimation Mechanism Based on Quantum Dynamics Framework

Quan Tang, Peng Wang

Due to the probability characteristics of quantum mechanism, the combination of quantum mechanism and intelligent algorithm has received wide attention. Quantum dynamics theory uses the Schrödinger equation as a quantum dynamics equation. Through three approximation of the objective function, quantum dynamics framework (QDF) is obtained which describes basic iterative operations of optimization algorithms. Based on QDF, this paper proposes a potential barrier estimation (PBE) method which originates from quantum mechanism. With the proposed method, the particle can accept inferior solutions during the sampling process according to a probability which is subject to the quantum tunneling effect, to improve the global search capacity of optimization algorithms. The effectiveness of the proposed method in the ability of escaping local minima was thoroughly investigated through double well function (DWF), and experiments on two benchmark functions sets show that this method significantly improves the optimization performance of high dimensional complex functions. The PBE method is quantized and easily transplanted to other algorithms to achieve high performance in the future.

Chinese Journal of Electronics•Jan 1, 2025•0 citations

Investigating the Dynamical Behavior of Quantum Dynamics Framework Algorithm with the Rastrigin-like Test Benchmark

Limeng Luo, Peng Wang, Minghui Zhao

With the emergence of various optimization algorithms, studying the specific dynamic evolution process of algorithms is of significant importance for proposing new algorithms and optimizing classical ones. The Quantum Dynamics Framework(QDF) Optimization Algorithm is derived from the Taylor expansion of the Schrödinger equation, providing a rigorous mathematical and physical foundation. Its iterative behavior can reflect the generality of evolution in most optimization algorithms. However, the iteration process of QDF on the multi-modal Rastrigin function faces challenges in convergence, necessitating meticulous evaluation and research of test functions. Therefore, this paper constructs an adjustable, scalable, and diverse Rastrigin-like Test Benchmark (RTB) collection, extending, shifting, and rotating the Rastrigin function in multi-modal scenarios. By viewing the algorithm's iteration process from the perspective of quantum dynamics as the evolution of sampling particles over time, and utilizing RTB to delve into the dynamical behavior of QDF algorithm, this research approach effectively supports the observation and improvement of algorithmic iteration processes, providing fundamental guidance for algorithmic optimization decisions.

International Conference on Algorithms, Computing and Systems (ICACS)•Oct 11, 2024•0 citations

Title: Quantum-Inspired Constraint Optimization with Adaptive Sampling

Jincheng Zhang

Quantum-inspired constraint optimization is a promising approach to accelerate the discovery of optimal solutions in complex problems. This paper explores the application of adaptive sampling techniques to enhance the effectiveness of this method. We introduce a novel 'quantum-perturbed sampling' algorithm that leverages controlled randomness to guide the search process. The core mechanism centers around dynamically adjusting the sampling frequency based on the algorithm's performance, thereby improving the exploration of the solution space. The research demonstrates the potential of this approach to achieve faster convergence and better solution quality compared to traditional optimization methods. We evaluate the algorithm on a range of benchmark problems, showcasing its efficacy in tackling challenging optimization tasks.

•Sep 2, 2026•0 citations

Title: Quantum Entanglement-Driven Constraint Satisfaction

Jincheng Zhang

Quantum entanglement, a peculiar phenomenon in quantum mechanics, offers a unique potential for optimization algorithms. This paper explores the application of entanglement to constraint satisfaction problems (CSPs) to generate solution diversity, addressing a key limitation of classical methods. We propose a novel constrained optimization framework that leverages quantum-inspired constraint representation and entanglement-induced correlations to overcome the pitfalls of local optima. We detail the design of our algorithm, including the quantum-inspired constraint representation and the entanglement-based exploration strategy. The core claim is that this approach significantly enhances solution diversity compared to existing methods, particularly in complex, multi-objective CSPs. This work contributes to the field of constrained optimization by investigating a fundamentally new approach to mitigate local optima, potentially opening new avenues for creative problem-solving.

•Sep 3, 2026•0 citations

Title: Quantum-Inspired Constraint Optimization with Adaptive Noise

Jincheng Zhang

Quantum-inspired constraint optimization presents a novel approach to solving complex optimization problems by leveraging quantum-mechanical principles. This paper introduces a framework for optimizing systems using quantum-inspired algorithms, incorporating adaptive noise to mitigate the risk of local optima. The core mechanism involves a 'quantum state exploration' – where the algorithm explores the solution space using quantum principles, guided by adaptive noise. This approach aims to overcome limitations of traditional optimization methods, particularly in scenarios where classical methods struggle to find optimal solutions. The study highlights the potential of quantum-inspired algorithms to enhance the robustness and efficiency of optimization processes, offering a promising pathway for tackling complex challenges across diverse domains.

•Sep 5, 2026•0 citations
In recent years, a study focused on the quantum dynamics framework (QDF) process has revealed significant, random structural bias in QDF’s optimization of symmetric and asymmetric forms of double-well functions (DWF). Such bias means that the algorithm tends to favor specific solutions in the search space while ignoring other possible solutions. This tendency may cause the algorithm to become trapped in local optima, preventing it from finding global optima and ultimately compromising its performance. To address this performance gap, this paper proposes a dimension-decoupling sampling mechanism and random-neighborhood sampling mechanism. The proposed mechanism effectively reduces the coupling between dimensions during the optimization of the objective function and increases the search space diversity, thereby mitigating the structural bias of the algorithm. It successfully solves the issue of performance degradation observed in the optimization of symmetric double-well functions. We refer to the modified algorithm as the dimension-decoupling and random-neighborhood quantum dynamics framework (DR-QDF).
In recent years, optimization algorithms have developed rapidly, especially those which introduce quantum ideas, which perform excellently. Inspired by quantum thought, this paper proposes a quantum dynamics framework (QDF) which converts optimization problems into the problem of the constrained ground state of the quantum system and analyzes optimization algorithms by simulating the dynamic evolution process of physical optimization systems in the ground state. Potential energy equivalence and Taylor expansion are performed on the objective function to obtain the basic iterative operations of optimization algorithms. Furthermore, a quantum dynamics framework based on the quantum tunneling effect (QDF-TE) is proposed which adopts dynamic multiple group collaborative sampling to improve the quantum tunneling effect of the QDF, thereby increasing the population diversity and improving algorithm performance. The probability distribution of solutions can be visually observed through the evolution of the wave function, which also indicates that the QDF-TE can strengthen the tunneling effect. The QDF-TE was evaluated on the CEC 2017 test suite and shown to be competitive with other heuristic optimization algorithms. The experimental results reveal the effectiveness of introducing a quantum mechanism to analyze and improve optimization algorithms.
Due to the probability characteristics of quantum mechanism, the combination of quantum mechanism and intelligent algorithm has received wide attention. Quantum dynamics theory uses the Schrödinger equation as a quantum dynamics equation. Through three approximation of the objective function, quantum dynamics framework (QDF) is obtained which describes basic iterative operations of optimization algorithms. Based on QDF, this paper proposes a potential barrier estimation (PBE) method which originates from quantum mechanism. With the proposed method, the particle can accept inferior solutions during the sampling process according to a probability which is subject to the quantum tunneling effect, to improve the global search capacity of optimization algorithms. The effectiveness of the proposed method in the ability of escaping local minima was thoroughly investigated through double well function (DWF), and experiments on two benchmark functions sets show that this method significantly improves the optimization performance of high dimensional complex functions. The PBE method is quantized and easily transplanted to other algorithms to achieve high performance in the future.
With the emergence of various optimization algorithms, studying the specific dynamic evolution process of algorithms is of significant importance for proposing new algorithms and optimizing classical ones. The Quantum Dynamics Framework(QDF) Optimization Algorithm is derived from the Taylor expansion of the Schrödinger equation, providing a rigorous mathematical and physical foundation. Its iterative behavior can reflect the generality of evolution in most optimization algorithms. However, the iteration process of QDF on the multi-modal Rastrigin function faces challenges in convergence, necessitating meticulous evaluation and research of test functions. Therefore, this paper constructs an adjustable, scalable, and diverse Rastrigin-like Test Benchmark (RTB) collection, extending, shifting, and rotating the Rastrigin function in multi-modal scenarios. By viewing the algorithm's iteration process from the perspective of quantum dynamics as the evolution of sampling particles over time, and utilizing RTB to delve into the dynamical behavior of QDF algorithm, this research approach effectively supports the observation and improvement of algorithmic iteration processes, providing fundamental guidance for algorithmic optimization decisions.
Quantum-inspired constraint optimization is a promising approach to accelerate the discovery of optimal solutions in complex problems. This paper explores the application of adaptive sampling techniques to enhance the effectiveness of this method. We introduce a novel 'quantum-perturbed sampling' algorithm that leverages controlled randomness to guide the search process. The core mechanism centers around dynamically adjusting the sampling frequency based on the algorithm's performance, thereby improving the exploration of the solution space. The research demonstrates the potential of this approach to achieve faster convergence and better solution quality compared to traditional optimization methods. We evaluate the algorithm on a range of benchmark problems, showcasing its efficacy in tackling challenging optimization tasks.
Quantum entanglement, a peculiar phenomenon in quantum mechanics, offers a unique potential for optimization algorithms. This paper explores the application of entanglement to constraint satisfaction problems (CSPs) to generate solution diversity, addressing a key limitation of classical methods. We propose a novel constrained optimization framework that leverages quantum-inspired constraint representation and entanglement-induced correlations to overcome the pitfalls of local optima. We detail the design of our algorithm, including the quantum-inspired constraint representation and the entanglement-based exploration strategy. The core claim is that this approach significantly enhances solution diversity compared to existing methods, particularly in complex, multi-objective CSPs. This work contributes to the field of constrained optimization by investigating a fundamentally new approach to mitigate local optima, potentially opening new avenues for creative problem-solving.
Quantum-inspired constraint optimization presents a novel approach to solving complex optimization problems by leveraging quantum-mechanical principles. This paper introduces a framework for optimizing systems using quantum-inspired algorithms, incorporating adaptive noise to mitigate the risk of local optima. The core mechanism involves a 'quantum state exploration' – where the algorithm explores the solution space using quantum principles, guided by adaptive noise. This approach aims to overcome limitations of traditional optimization methods, particularly in scenarios where classical methods struggle to find optimal solutions. The study highlights the potential of quantum-inspired algorithms to enhance the robustness and efficiency of optimization processes, offering a promising pathway for tackling complex challenges across diverse domains.