基于记忆网络的量子计算的参数优化

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum computing holds immense potential for solving complex problems intractable for classical computers. However, the development of efficient quantum algorithms relies heavily on the precise optimization of quantum parameters. This paper investigates a novel approach to quantum parameter optimization utilizing a dynamically learning quantum memory network. We propose a system where quantum states are represented as a memory network, allowing the network to learn and adapt to the system's environment during the optimization process. This dynamic learning mechanism promises to automate the parameter adjustment, leading to improved quantum algorithm performance. The core mechanism of this research is the implementation of a quantum memory network that learns and dynamically adjusts parameters based on observed quantum states. This offers a fundamentally different paradigm compared to traditional parameter optimization methods. The potential benefits include enhanced algorithm performance, reduced computational cost, and a more adaptable system capable of addressing complex quantum problems. We present a preliminary implementation of this system, demonstrating its capabilities through a simplified quantum circuit optimization task.

DOI: 10.5281/zenodo.22253558

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