自适应张量聚合的量子计算实现

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel quantum computing framework, termed "Adaptive Tensor Aggregation" (ATA), designed to accelerate quantum machine learning algorithms by leveraging adaptive tensor aggregation. Traditional quantum computation often struggles with the limitations of limited qubit resources, necessitating strategies for efficient tensor manipulation. ATA addresses these challenges through a dynamic adjustment of qubit count and state representation, guided by quantum entanglement and superposition. The core mechanism centers around a novel algorithm that iteratively adjusts the tensor aggregation level based on the current state of the system, optimizing for computational complexity and resource utilization. This approach allows for the efficient execution of complex tensor operations, crucial for many machine learning tasks. The proposed framework demonstrates significant improvements in performance compared to existing quantum algorithms, particularly in the context of large-scale tensor computations.

DOI: 10.5281/zenodo.22263722

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