Quantum Circuit Optimization via Geometric Topology: A Novel Adaptive Optimization Approach

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum circuit optimization algorithm based on geometric topology. Traditional circuit optimization methods often rely on manual design, lacking the dynamic adaptability necessary to maintain optimal circuit performance. Our proposed algorithm leverages the inherent properties of quantum circuits, specifically their geometric topology, to dynamically adjust parameters and achieve enhanced circuit fidelity. We demonstrate the effectiveness of this approach through rigorous analysis and simulation, showcasing its ability to optimize quantum circuit designs with unprecedented precision. The core mechanism centers around constructing a dynamic topology that responds to variations in circuit parameters, ensuring a continuous and robust optimization process. This offers a significant advancement over existing methods, particularly in complex and highly parameterized quantum circuits.

DOI: 10.5281/zenodo.22188191

Related papers

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, 20260 citations

Self-Adaptive Quantum Topology Dynamic Optimization Algorithm

Jincheng Zhang

Quantum topology, a novel framework for manipulating quantum states, offers unprecedented opportunities for algorithm design and optimization. This paper presents a self-adaptive quantum topology dynamic optimization algorithm, leveraging the inherent properties of quantum topology to dynamically adjust quantum circuit topologies and enhance algorithm performance. We demonstrate the algorithm's efficacy through a series of rigorous analyses and simulations, showcasing significant improvements in key performance metrics compared to traditional optimization techniques. The core mechanism centers around the continuous exploration and refinement of quantum topology through iterative adjustments, effectively circumventing the limitations of static optimization strategies. This approach provides a promising pathway toward the development of robust and highly adaptable quantum algorithms.

Aug 31, 20260 citations

Core Claim: A Dynamic Adaptive Quantum Topological Model Optimization Algorithm

Jincheng Zhang

Quantum topologies offer unique opportunities for quantum computation and information processing. However, designing and optimizing quantum circuits often requires careful control over the underlying topology. This paper presents a novel optimization algorithm based on dynamic self-adaptive quantum topology models. We introduce a mechanism that automatically adjusts quantum topology parameters based on input data, enabling precise control and optimization of these models. This approach overcomes limitations of traditional static optimization methods, particularly for complex and highly parameterized quantum systems. We demonstrate the effectiveness of this algorithm through several illustrative examples, showcasing its ability to achieve significant improvements in quantum circuit performance.

Aug 29, 20260 citations

Dynamic Quantum Topology Adaptive Topology Optimization

Jincheng Zhang

This paper explores the application of dynamic quantum topology optimization to enhance the design of quantum systems, particularly focusing on the optimization of quantum crystals and circuits. Traditional approaches to topology optimization rely on manual parameter adjustments, limiting flexibility and potentially hindering the discovery of novel solutions. We introduce a novel algorithm that dynamically adjusts the quantum topology structure during the optimization process, enabling a more intuitive and powerful approach to finding optimal configurations. The core mechanism involves iteratively refining the quantum topology through a feedback loop, leveraging the inherent properties of quantum systems to guide the optimization toward desired outcomes. This approach promises significant improvements in the design of complex quantum devices and circuits.

Sep 4, 20260 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, 20260 citations

Dynamic Topology Optimization for Quantum Computing

Jincheng Zhang

This paper introduces a novel dynamic topology optimization system designed to enhance the performance of quantum computation. Traditional parameter adjustment methods often rely on manual intervention, lacking the inherent advantages of topology in shaping quantum systems. This system dynamically adjusts quantum system topology during computation, optimizing performance through a sophisticated interplay of geometric transformations. The core mechanism leverages a continuous, iterative process of topology manipulation guided by a computational model. We demonstrate the system's effectiveness through simulations and preliminary experimental results, showcasing significant improvements in quantum gate fidelity and coherence time.

Aug 31, 20260 citations