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

Jincheng Zhang

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

Quantum simulation, a cornerstone of modern scientific research, relies heavily on the accurate representation of quantum systems. Traditional simulation methods often struggle with inherent noise and limited computational resources. This paper proposes a novel quantum algorithm, termed "Adaptive Topological Quantum Algorithm" (ATQA), designed to address these challenges by leveraging the principles of self-organized topology in quantum systems. We explore the potential of this approach to significantly enhance the precision and efficiency of quantum simulations, particularly within the context of optimization problems. This work presents a framework that dynamically adjusts quantum parameters to optimize the simulation process, offering a fundamentally different perspective on quantum algorithm design.

DOI
10.5281/zenodo.22184441

Related papers

基于动态拓扑的自适应多体量子计算

Jincheng Zhang

This paper introduces a novel approach to quantum computation leveraging dynamic topology, specifically designed for adaptive multi-body simulations. Traditional quantum algorithms often struggle with the inherent limitations of multi-body systems, hindering the ability to achieve high precision and complex simulations. We propose a method that utilizes dynamic topology to dynamically adjust the quantum system's structure, enabling significantly improved accuracy and simulation capabilities. This work addresses the fundamental challenge of multi-body systems by introducing a self-adaptive framework centered around dynamic topology. The proposed system demonstrates enhanced fidelity and the potential for tackling previously intractable problems within the realm of quantum simulation.

•Sep 3, 2026•0 citations

Quantum Simulation of Adaptive Topology Optimization Algorithm

Jincheng Zhang

Quantum simulation has emerged as a powerful tool for simulating complex systems, offering the potential for unprecedented accuracy and speed. This paper introduces a novel quantum simulation algorithm, termed "Adaptive Topology Optimization," designed to automatically adjust the simulation's topology during the process, thereby reducing simulation time and improving accuracy. The core mechanism revolves around leveraging the inherent dynamics of quantum systems to dynamically modify the simulation's structure, effectively optimizing the process for specific outcomes. This approach addresses a critical challenge in quantum simulation – the reliance on meticulously crafted parameters – and presents a promising path towards more efficient and precise simulations of intricate phenomena. The algorithm's potential applications span diverse fields, including materials science, drug discovery, and fundamental physics.

•Sep 2, 2026•0 citations

Quantum Adaptive Topological Optimization Algorithm

Jincheng Zhang

This paper introduces a novel quantum adaptive topology optimization algorithm designed to enhance the performance of quantum computation. Traditional optimization methods often rely on static parameters, limiting the algorithm's adaptability. This research proposes a quantum-enhanced approach that dynamically adjusts the topology of a quantum system based on its internal state, enabling more efficient and precise solutions. We demonstrate the effectiveness of this algorithm through a series of simulations and analysis, highlighting its potential to overcome limitations of existing quantum optimization techniques. This algorithm leverages the inherent quantum properties to achieve a level of flexibility and robustness not previously achievable.

•Sep 3, 2026•0 citations

自适应拓扑的量子计算的参数调整算法

Jincheng Zhang

This paper presents a novel algorithm for parameter adjustment in quantum computation, leveraging the principles of adaptive topology. Traditional parameter adjustment methods often rely on fixed algorithms, struggling to effectively handle complex environments. We propose a dynamic adjustment approach that utilizes the self-assembling process of quantum systems, dynamically modifying topological control parameters to optimize quantum computation performance and efficiency. The core mechanism involves a sophisticated optimization process guided by the inherent properties of the quantum system, ultimately leading to enhanced computational capabilities. This research explores the potential of this adaptive approach to overcome the limitations of conventional parameter tuning and advance the field of quantum computing.

•Sep 7, 2026•0 citations

Adaptive Topology Quantum Computing Algorithm Design

Jincheng Zhang

This paper presents a novel quantum computing algorithm, termed Adaptive Topology Quantum Algorithm (ATQA), designed to address the challenges of large-scale geometric optimization problems. Traditional approaches often rely on classical optimization techniques, while ATQA leverages the principles of adaptive topology to achieve improved efficiency and accuracy. We detail the algorithm's core mechanism, the underlying theory behind the adaptive topology, and its potential for tackling complex geometric optimization tasks. The paper concludes with a discussion of the algorithm's implementation, performance evaluation, and future research directions.

•Sep 3, 2026•0 citations

基于自适应量子计算的拓扑优化算法

Jincheng Zhang

This paper introduces a novel quantum optimization algorithm, termed "Adaptive Topological Optimization," designed to enhance the performance of quantum computations. Traditional optimization methods often struggle with the inherent complexity of quantum systems, necessitating a shift towards quantum-specific approaches. This research focuses on leveraging the unique properties of quantum mechanics – particularly the topological nature of quantum states – to guide optimization processes, offering a potential paradigm shift in quantum algorithm design. The core of this algorithm utilizes a dynamically adjusted quantum state to explore the solution space more efficiently and identify optimal solutions with superior performance. We demonstrate the effectiveness of this approach through a series of simulations and preliminary results, highlighting its potential to significantly improve the efficiency and scalability of quantum computations. The paper concludes with a discussion of the algorithm's underlying principles, its potential applications, and future research directions.

•Sep 2, 2026•0 citations
Quantum simulation, a cornerstone of modern scientific research, relies heavily on the accurate representation of quantum systems. Traditional simulation methods often struggle with inherent noise and limited computational resources. This paper proposes a novel quantum algorithm, termed "Adaptive Topological Quantum Algorithm" (ATQA), designed to address these challenges by leveraging the principles of self-organized topology in quantum systems. We explore the potential of this approach to significantly enhance the precision and efficiency of quantum simulations, particularly within the context of optimization problems. This work presents a framework that dynamically adjusts quantum parameters to optimize the simulation process, offering a fundamentally different perspective on quantum algorithm design.
This paper introduces a novel approach to quantum computation leveraging dynamic topology, specifically designed for adaptive multi-body simulations. Traditional quantum algorithms often struggle with the inherent limitations of multi-body systems, hindering the ability to achieve high precision and complex simulations. We propose a method that utilizes dynamic topology to dynamically adjust the quantum system's structure, enabling significantly improved accuracy and simulation capabilities. This work addresses the fundamental challenge of multi-body systems by introducing a self-adaptive framework centered around dynamic topology. The proposed system demonstrates enhanced fidelity and the potential for tackling previously intractable problems within the realm of quantum simulation.
Quantum simulation has emerged as a powerful tool for simulating complex systems, offering the potential for unprecedented accuracy and speed. This paper introduces a novel quantum simulation algorithm, termed "Adaptive Topology Optimization," designed to automatically adjust the simulation's topology during the process, thereby reducing simulation time and improving accuracy. The core mechanism revolves around leveraging the inherent dynamics of quantum systems to dynamically modify the simulation's structure, effectively optimizing the process for specific outcomes. This approach addresses a critical challenge in quantum simulation – the reliance on meticulously crafted parameters – and presents a promising path towards more efficient and precise simulations of intricate phenomena. The algorithm's potential applications span diverse fields, including materials science, drug discovery, and fundamental physics.
This paper introduces a novel quantum adaptive topology optimization algorithm designed to enhance the performance of quantum computation. Traditional optimization methods often rely on static parameters, limiting the algorithm's adaptability. This research proposes a quantum-enhanced approach that dynamically adjusts the topology of a quantum system based on its internal state, enabling more efficient and precise solutions. We demonstrate the effectiveness of this algorithm through a series of simulations and analysis, highlighting its potential to overcome limitations of existing quantum optimization techniques. This algorithm leverages the inherent quantum properties to achieve a level of flexibility and robustness not previously achievable.
This paper presents a novel algorithm for parameter adjustment in quantum computation, leveraging the principles of adaptive topology. Traditional parameter adjustment methods often rely on fixed algorithms, struggling to effectively handle complex environments. We propose a dynamic adjustment approach that utilizes the self-assembling process of quantum systems, dynamically modifying topological control parameters to optimize quantum computation performance and efficiency. The core mechanism involves a sophisticated optimization process guided by the inherent properties of the quantum system, ultimately leading to enhanced computational capabilities. This research explores the potential of this adaptive approach to overcome the limitations of conventional parameter tuning and advance the field of quantum computing.
This paper presents a novel quantum computing algorithm, termed Adaptive Topology Quantum Algorithm (ATQA), designed to address the challenges of large-scale geometric optimization problems. Traditional approaches often rely on classical optimization techniques, while ATQA leverages the principles of adaptive topology to achieve improved efficiency and accuracy. We detail the algorithm's core mechanism, the underlying theory behind the adaptive topology, and its potential for tackling complex geometric optimization tasks. The paper concludes with a discussion of the algorithm's implementation, performance evaluation, and future research directions.
This paper introduces a novel quantum optimization algorithm, termed "Adaptive Topological Optimization," designed to enhance the performance of quantum computations. Traditional optimization methods often struggle with the inherent complexity of quantum systems, necessitating a shift towards quantum-specific approaches. This research focuses on leveraging the unique properties of quantum mechanics – particularly the topological nature of quantum states – to guide optimization processes, offering a potential paradigm shift in quantum algorithm design. The core of this algorithm utilizes a dynamically adjusted quantum state to explore the solution space more efficiently and identify optimal solutions with superior performance. We demonstrate the effectiveness of this approach through a series of simulations and preliminary results, highlighting its potential to significantly improve the efficiency and scalability of quantum computations. The paper concludes with a discussion of the algorithm's underlying principles, its potential applications, and future research directions.