基于图的量子态的自适应计算模型

Abstract

This paper introduces a novel quantum state computation model based on graph structures, leveraging the inherent properties of quantum states to enable adaptive computation. Traditional quantum computing approaches often struggle with complex quantum states, presenting a significant challenge for efficient computation. Our model proposes a framework that utilizes graph structures to represent quantum states, enabling dynamic modifications to the state's attributes through graph manipulation. This approach offers a potentially transformative path toward enhanced quantum simulation and algorithm development. The core mechanism involves representing quantum states as graphs, where nodes represent states and edges represent relationships between them, allowing for iterative and adaptive computational processes. This research explores the design of such a model and its potential to overcome limitations of existing quantum computation techniques. The paper details the model's architecture, demonstrates its ability to perform specific quantum operations, and discusses future research directions with a focus on scalability and practical applications.