Quantum Information as a Dynamic Landscape – Landscape-Based Algorithms

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Information represents a fundamentally different paradigm from classical information, offering the potential for exponential speedups in computation and communication. This paper explores the application of Landscape-Based Algorithms (LBAs) to optimization problems, specifically focusing on the challenge of efficiently navigating complex search spaces. We propose a novel algorithmic approach that models the problem space as a dynamic landscape, iteratively moving across this landscape to reward solutions that are closer to the optimal solution. This framework allows for exploration of vast possibilities, dramatically improving the search efficiency compared to traditional optimization methods. The core mechanism involves a dynamic reward function that adjusts based on the landscape's characteristics, encouraging the algorithm to escape local optima and converge to a global optimum. This paper provides a detailed explanation of the Landscape model, explores its implementation, and discusses its potential impact on optimization problems across diverse domains.

DOI: 10.5281/zenodo.22294993

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