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

基于量子拓扑的自适应量子电路设计优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations
Publisher

Abstract

Quantum circuits, with their inherent complexity and sensitivity to environmental fluctuations, pose significant challenges in their design and implementation. This paper presents a novel framework for quantum circuit design leveraging the principles of quantum topology, specifically a topological circuit design. We propose a self-adaptive circuit design algorithm that automatically adjusts circuit parameters to optimize performance and stability. This approach overcomes limitations of traditional circuit design by exploiting the inherent robustness and adaptability of topological structures. The framework utilizes a set of topological constraints and transformations to dynamically modify the circuit's parameters, leading to improved efficiency and stability. The results demonstrate the effectiveness of this method in optimizing circuit performance across a range of parameters.

DOI
10.5281/zenodo.22276660

Related papers

基于拓扑的量子计算的自适应量子电路设计

Jincheng Zhang

Quantum computing offers the potential for exponentially faster computations compared to classical computers, particularly for complex problems. However, the design and implementation of quantum circuits remain a significant challenge due to the inherent complexity of quantum systems. This paper explores a novel approach to quantum circuit design leveraging topological principles to automate the process of circuit optimization and achieve higher computational efficiency. We propose a new framework for automatic circuit adjustment based on topological structure, enabling the system to adapt its design based on the characteristics of the task at hand. This system aims to overcome the limitations of traditional methods by offering a more flexible and robust approach to quantum circuit creation. The research demonstrates the effectiveness of this framework through a series of simulations and analysis, showcasing its ability to generate circuits with improved performance for specific quantum algorithms. The core mechanism is the application of topological topological symmetries to the circuit structure, allowing the system to dynamically adjust parameters to achieve optimal results. This work represents a significant advancement in quantum circuit design, potentially paving the way for more efficient and scalable quantum computations.

•Sep 4, 2026•0 citations

Dynamic Quantum Circuit Topology Optimization

Jincheng Zhang

Dynamic Quantum Circuit Topology Optimization addresses the challenges of optimizing quantum circuits for enhanced performance and stability through adaptive circuit design. This paper introduces a novel method leveraging the inherent topological properties of quantum circuits to dynamically adjust their structure, leading to improved computational efficiency and robustness. We present a framework for automatically optimizing circuit topology, incorporating a self-adaptive mechanism that responds to the circuit's state. The core mechanism centers on utilizing topological transformations to mitigate circuit bottlenecks and enhance coherence. The study demonstrates the effectiveness of this approach in optimizing specific quantum algorithms, showcasing its potential for accelerating complex computations. The research focuses on providing a practical solution for dynamically tailoring quantum circuits, offering a significant advancement over traditional optimization techniques.

•Sep 7, 2026•0 citations

基于量子拓扑的自适应量子电路设计

Jincheng Zhang

Quantum computation, leveraging the unique properties of quantum mechanics, holds immense promise for scientific and technological advancements. However, scaling and maintaining the fidelity of quantum circuits presents significant challenges. This paper introduces a novel approach to quantum circuit design based on the principles of quantum topology, specifically utilizing the concept of topologically protected quantum states. We propose a self-adaptive circuit design methodology that automatically adjusts circuit parameters to optimize performance and efficiency. The core mechanism revolves around leveraging the inherent robustness of topological structures in quantum systems to achieve precise control and manipulation of quantum states. This method offers a significant improvement over conventional methods by automating the optimization process and enhancing the overall reliability of quantum circuits. We explore the theoretical foundations of this approach and present preliminary experimental results demonstrating its effectiveness in achieving targeted circuit improvements. The impact of this design on quantum computation efficiency is expected to be substantial.

•Sep 3, 2026•0 citations

Adaptive Topology for Quantum Circuit Design

Jincheng Zhang

Quantum circuit design presents significant challenges due to the inherent fragility of quantum states and the complexity of controlling their evolution. Traditional methods often rely on static, pre-defined topologies, limiting the potential for optimization and adaptation to specific quantum systems. This paper introduces a novel approach to quantum circuit design, leveraging adaptive topology – a concept where the circuit's topology dynamically adjusts based on the quantum system's characteristics – to enhance circuit performance and stability. We propose a method for automatically generating and optimizing topological circuits using a reinforcement learning framework. The core mechanism involves a self-adaptive topological graph construction, iteratively refining the circuit's structure to minimize errors and maximize fidelity. The study demonstrates the effectiveness of this approach through simulations and experimental validation on a simplified quantum system, showing significant improvements in performance compared to conventional methods. The paper concludes with a discussion of the potential applications and future research directions in this rapidly evolving field.

•Sep 6, 2026•0 citations

自适应量子算法的量子电路优化

Jincheng Zhang

Quantum algorithms are increasingly important for scientific and technological advancements, but their efficiency is often highly dependent on the specific quantum circuits implemented. This paper explores a novel approach to quantum circuit optimization, termed "Adaptive Quantum Circuit Optimization" (AQCO), which dynamically adjusts the structure and parameters of quantum circuits based on their inherent characteristics and computational goals. We introduce a framework leveraging quantum circuit self-adaptability to automatically optimize circuit design, resulting in significant improvements in efficiency compared to traditional methods. The proposed AQCO offers a robust and automated solution for tailoring quantum circuits to specific problems, potentially unlocking new possibilities in quantum computation. This work demonstrates the effectiveness of the framework through theoretical analysis and preliminary experimental validation.

•Sep 7, 2026•0 citations

Quantum Topology-Based Adaptive Quantum Circuit Optimization

Jincheng Zhang

This paper introduces a novel quantum circuit optimization framework predicated on quantum topology. The core concept revolves around utilizing quantum entanglement "pulses" to dynamically adjust circuit parameters, thereby enhancing optimization efficiency. Traditional circuit optimization methods often involve static adjustments, which can be computationally expensive and may not fully exploit the inherent structure of the circuits. This work proposes a dynamic adjustment mechanism based on quantum entanglement, offering a significant advancement in optimizing quantum algorithms and circuit designs. The framework leverages the principles of self-maintenance inherent in topological structures, leading to improved performance and adaptability. This research explores the potential of this approach to address challenges in classical circuit optimization.

•Sep 4, 2026•0 citations
Quantum circuits, with their inherent complexity and sensitivity to environmental fluctuations, pose significant challenges in their design and implementation. This paper presents a novel framework for quantum circuit design leveraging the principles of quantum topology, specifically a topological circuit design. We propose a self-adaptive circuit design algorithm that automatically adjusts circuit parameters to optimize performance and stability. This approach overcomes limitations of traditional circuit design by exploiting the inherent robustness and adaptability of topological structures. The framework utilizes a set of topological constraints and transformations to dynamically modify the circuit's parameters, leading to improved efficiency and stability. The results demonstrate the effectiveness of this method in optimizing circuit performance across a range of parameters.
Quantum computing offers the potential for exponentially faster computations compared to classical computers, particularly for complex problems. However, the design and implementation of quantum circuits remain a significant challenge due to the inherent complexity of quantum systems. This paper explores a novel approach to quantum circuit design leveraging topological principles to automate the process of circuit optimization and achieve higher computational efficiency. We propose a new framework for automatic circuit adjustment based on topological structure, enabling the system to adapt its design based on the characteristics of the task at hand. This system aims to overcome the limitations of traditional methods by offering a more flexible and robust approach to quantum circuit creation. The research demonstrates the effectiveness of this framework through a series of simulations and analysis, showcasing its ability to generate circuits with improved performance for specific quantum algorithms. The core mechanism is the application of topological topological symmetries to the circuit structure, allowing the system to dynamically adjust parameters to achieve optimal results. This work represents a significant advancement in quantum circuit design, potentially paving the way for more efficient and scalable quantum computations.
Dynamic Quantum Circuit Topology Optimization addresses the challenges of optimizing quantum circuits for enhanced performance and stability through adaptive circuit design. This paper introduces a novel method leveraging the inherent topological properties of quantum circuits to dynamically adjust their structure, leading to improved computational efficiency and robustness. We present a framework for automatically optimizing circuit topology, incorporating a self-adaptive mechanism that responds to the circuit's state. The core mechanism centers on utilizing topological transformations to mitigate circuit bottlenecks and enhance coherence. The study demonstrates the effectiveness of this approach in optimizing specific quantum algorithms, showcasing its potential for accelerating complex computations. The research focuses on providing a practical solution for dynamically tailoring quantum circuits, offering a significant advancement over traditional optimization techniques.
Quantum computation, leveraging the unique properties of quantum mechanics, holds immense promise for scientific and technological advancements. However, scaling and maintaining the fidelity of quantum circuits presents significant challenges. This paper introduces a novel approach to quantum circuit design based on the principles of quantum topology, specifically utilizing the concept of topologically protected quantum states. We propose a self-adaptive circuit design methodology that automatically adjusts circuit parameters to optimize performance and efficiency. The core mechanism revolves around leveraging the inherent robustness of topological structures in quantum systems to achieve precise control and manipulation of quantum states. This method offers a significant improvement over conventional methods by automating the optimization process and enhancing the overall reliability of quantum circuits. We explore the theoretical foundations of this approach and present preliminary experimental results demonstrating its effectiveness in achieving targeted circuit improvements. The impact of this design on quantum computation efficiency is expected to be substantial.
Quantum circuit design presents significant challenges due to the inherent fragility of quantum states and the complexity of controlling their evolution. Traditional methods often rely on static, pre-defined topologies, limiting the potential for optimization and adaptation to specific quantum systems. This paper introduces a novel approach to quantum circuit design, leveraging adaptive topology – a concept where the circuit's topology dynamically adjusts based on the quantum system's characteristics – to enhance circuit performance and stability. We propose a method for automatically generating and optimizing topological circuits using a reinforcement learning framework. The core mechanism involves a self-adaptive topological graph construction, iteratively refining the circuit's structure to minimize errors and maximize fidelity. The study demonstrates the effectiveness of this approach through simulations and experimental validation on a simplified quantum system, showing significant improvements in performance compared to conventional methods. The paper concludes with a discussion of the potential applications and future research directions in this rapidly evolving field.
Quantum algorithms are increasingly important for scientific and technological advancements, but their efficiency is often highly dependent on the specific quantum circuits implemented. This paper explores a novel approach to quantum circuit optimization, termed "Adaptive Quantum Circuit Optimization" (AQCO), which dynamically adjusts the structure and parameters of quantum circuits based on their inherent characteristics and computational goals. We introduce a framework leveraging quantum circuit self-adaptability to automatically optimize circuit design, resulting in significant improvements in efficiency compared to traditional methods. The proposed AQCO offers a robust and automated solution for tailoring quantum circuits to specific problems, potentially unlocking new possibilities in quantum computation. This work demonstrates the effectiveness of the framework through theoretical analysis and preliminary experimental validation.
This paper introduces a novel quantum circuit optimization framework predicated on quantum topology. The core concept revolves around utilizing quantum entanglement "pulses" to dynamically adjust circuit parameters, thereby enhancing optimization efficiency. Traditional circuit optimization methods often involve static adjustments, which can be computationally expensive and may not fully exploit the inherent structure of the circuits. This work proposes a dynamic adjustment mechanism based on quantum entanglement, offering a significant advancement in optimizing quantum algorithms and circuit designs. The framework leverages the principles of self-maintenance inherent in topological structures, leading to improved performance and adaptability. This research explores the potential of this approach to address challenges in classical circuit optimization.