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Adaptive Quantum Circuit Design & Topology Mapping

Jincheng Zhang

Published Sep 3, 2026• 0 citations
Publisher

Abstract

This paper presents a novel approach to quantum circuit design utilizing adaptive parameter adjustment. Traditional circuit design methods are often constrained by fixed parameters, limiting the potential for efficient and flexible quantum computations. We introduce a dynamic adjustment mechanism that allows for real-time adjustments to quantum circuit parameters, enabling a significantly enhanced level of control and optimization. The core principle behind this method lies in leveraging quantum phenomena, specifically entanglement and superposition, to dynamically modify the circuit's evolution, leading to improved performance and novel functionalities. The resulting design offers a pathway to overcome limitations inherent in fixed-parameter approaches, opening doors to more complex and sophisticated quantum algorithms. This work demonstrates the effectiveness of this adaptive strategy through simulations and analysis, showcasing its potential to revolutionize quantum circuit design.

DOI
10.5281/zenodo.22277235

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This paper introduces an innovative approach to quantum algorithm optimization, leveraging the inherent adaptability of quantum algorithms. Traditional optimization methods often rely on fixed parameters, limiting the algorithm's effectiveness. We propose a novel framework centered around dynamically adjusting quantum circuit parameters to enhance algorithm performance. This framework utilizes quantum superposition and entanglement to explore a broader parameter space, leading to significant efficiency gains. The core mechanism involves a self-adaptive optimization process, continuously refining the quantum circuit parameters based on the algorithm's current state. The paper details the theoretical underpinnings of this method, demonstrates its efficacy through simulations, and provides a clear pathway for practical implementation. We analyze the impact of parameter adjustments on various metrics, showcasing the algorithm's superior performance compared to existing methods. The paper concludes with a discussion of the potential implications of this approach for a wide range of quantum computing applications.
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