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自适应量子电路的量子自适应算法

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

This paper presents a novel quantum circuit design tool leveraging an adaptive quantum algorithm to automatically adjust quantum parameters. Traditional quantum circuit design relies heavily on manual parameter tuning, which is a time-consuming and error-prone process. This research introduces a system that dynamically adjusts parameters based on the circuit's current state, drastically reducing the need for manual intervention and enhancing the efficiency of quantum computation and information processing. The core mechanism focuses on a self-tuning algorithm that iteratively refines the circuit's parameters to optimize performance. We detail the design of this algorithm and demonstrate its effectiveness through simulations and preliminary results. The advantages of this approach are enhanced adaptability, reduced parameter tuning effort, and improved quantum circuit performance.

DOI
10.5281/zenodo.22465313

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Quantum circuits, while promising for quantum computation, present significant challenges in terms of design, optimization, and resource utilization. Current optimization techniques often rely on manual parameter tuning, limiting scalability and adaptability. This paper proposes a dynamic adaptive quantum circuit optimization algorithm that automatically adjusts quantum circuit parameters to enhance circuit performance and reduce hardware resource consumption. The core mechanism involves a feedback loop that continuously monitors the circuit's performance and adjusts the parameters to achieve optimal results. This approach overcomes the limitations of traditional methods by providing a truly automated optimization process. This work presents a novel methodology leveraging dynamic adjustments to improve circuit efficiency and resource utilization, demonstrating significant potential for advancing the field of quantum circuit design.
This paper introduces a novel dynamic adjustment algorithm for quantum circuits, leveraging self-adaptive parameter adjustment to optimize the performance of topological information theory (TIT) algorithms. Traditional quantum circuit design relies on manual parameter tuning, which is a time-consuming and error-prone process. Our algorithm dynamically adjusts circuit parameters to achieve superior performance, significantly accelerating the implementation of TIT algorithms. We demonstrate the effectiveness of this approach through a series of simulations and analysis, showcasing substantial improvements in algorithm execution time and qubit fidelity. The design incorporates a robust feedback mechanism to maintain optimal circuit characteristics throughout the optimization process. The core mechanism revolves around iteratively refining circuit parameters based on a learned objective function, providing a practical and efficient pathway for accelerating TIT algorithm development.
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Quantum computing promises revolutionary advancements across diverse fields, but its current limitations stem from the complexities of quantum circuit design and optimization. This paper introduces an Adaptive Quantum Circuit Optimization framework, leveraging self-learning to dynamically adjust quantum circuit parameters, thereby enhancing both efficiency and accuracy. We detail the core mechanism, showcasing a method for automated parameter tuning based on a learned model. The framework's advantages include reduced manual intervention, improved adaptability to varying problem instances, and a demonstrable increase in quantum computation performance. We provide a comprehensive illustration of the framework's capabilities through a simplified, yet illustrative, quantum circuit optimization process. The research presents a novel approach to quantum circuit optimization, offering a significant step toward more robust and scalable quantum computing solutions.
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