Adaptive Tensor Aggregation Quantum Algorithm Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel quantum algorithm optimization framework based on adaptive tensor aggregation. We propose a dynamic adjustment of quantum bit (qubit) counts and state configurations within a quantum circuit to achieve efficient and rapid optimization of quantum algorithms. Traditional optimization methods often rely on static configurations, limiting their effectiveness in complex scenarios. Our approach dynamically adapts to the specific characteristics of each quantum circuit, leading to improved performance and potentially enabling the optimization of previously intractable problems. This work presents a practical implementation demonstrating the effectiveness of this framework, showcasing its ability to significantly accelerate the optimization of various quantum algorithms. The core mechanism involves a sophisticated control system that intelligently manages qubit resource allocation and adjusts the algorithm's trajectory. The resulting optimization process is characterized by enhanced convergence rates and improved solution quality compared to existing methods. We detail the architecture of the framework, discuss the key implementation details, and provide a comprehensive analysis of its performance characteristics.

DOI: 10.5281/zenodo.22263766

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