Non-linear Adaptive Computation Graph Optimization via Quantum Computing

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper investigates the application of quantum computing to optimize non-linear adaptive computation graphs. Traditional optimization techniques often struggle with the inherent complexity and non-linearity of these graphs, limiting their effectiveness in solving challenging optimization problems. We propose a novel quantum-enhanced optimization algorithm, leveraging quantum circuit simulation and quantum entanglement to accelerate the optimization process. The core mechanism involves utilizing quantum circuits to emulate the optimization process, enabling the algorithm to explore a larger solution space and potentially find optimal solutions more efficiently. This research aims to demonstrate the potential of quantum computing to address limitations in existing optimization methods, particularly for complex computational graphs. The paper discusses the theoretical foundations, implementation details, and preliminary results of the proposed algorithm, highlighting its advantages and challenges.

DOI: 10.5281/zenodo.22224033

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