# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 1 Introduction  
In recent years, path planning has been a hot problem in the research field, such as in robot path planning and evacuation path planning [1; 2]. Commonly used path planning algorithms include the ant colony algorithm [1], artificial potential field algorithm [3], genetic algorithm [4], and A* algorithm [5]. The core requirement of path planning is to plan the optimal and safest path from the starting point to the target one according to the optimization characteristics, such as shortest distance, shortest time, and lowest energy consumption [6; 7; 8]. With the development of emerging technologies, methods based on deep learning and quantum optimization algorithms have also been applied to the field of path planning [9; 10; 11; 12]. [PERSON] et al. employed a deep deterministic policy gradient algorithm to model continuous environments, concurrently proposing an innovative inner product reward function, thereby enhancing the objective tracking and obstacle avoidance capabilities of unmanned aerial vehicles [9]. [PERSON] et al. delved into the study of deep Q-learning models, greedy table-based Q-learning models, and online optimization frameworks for addressing the shortest path problem between two points and the issue of random obstacles [10]. The Quantum Optimization Algorithm (QOA) is a brand new evolutionary algorithm proposed by [PERSON] and [PERSON], who combined quantum computing theory into the classical genetic algorithm in 1996 [13]. Built upon quantum principles, the quantum optimization algorithm utilizes quantum bit encoding and updates populations through quantum gates to pursue the global optimal solution. Contrasting with traditional evolutionary algorithms, it boosts features like smaller population size, faster computationspeed, and robust global optimality detection. The Quantum Genetic Algorithm (QGA) amalgamates the merits of quantum computing and genetic algorithms, finding applications in various domains such as function optimization, combinatorial optimization, image processing, data mining, and production scheduling. Consequently, it offers significant advantages and exhibits robust vitality, holding substantial theoretical value and promising application prospects for QOA. [PERSON] et al. utilized Bloch spherical descriptions to model systems during the design of quantum reversible logic. Subsequently, Bloch spherical coordinates have frequently been employed in quantum logic operations and algorithms [14; 15]. A QGA for asymptotic Bloch spherical search was proposed by [PERSON], and applied to the extreme value optimization problem of multivariate functions [16]. Compared with the traditional optimization algorithm for solving complex nonlinear problems, the quantum genetic algorithm is generated under the guiding idea of de-coarsening, inheriting the strengths of the genetic algorithm while also mitigating some of the drawbacks, such as oversized populations and long convergence time, to some extent [17]. It achieves better optimization results than traditional algorithms. With the rapid development of the social economy, building designs are becoming more complex and taller, resulting in more intricate internal passage distributions. However, most current algorithm research focuses on solving problems in two-dimensional building environments, which presents numerous limitations when dealing with the relatively complex three-dimensional space of modern buildings [18; 19]. [PERSON] et al. characterized the indoor path planning problem as a dynamic net-work flow problem, applying dictionary optimization algorithms to minimize evacuation time [20]. Pursals improved evacuation and inverse evacuation functions, proposing a novel evacuation formula to address evacuation problems [21]. [PERSON], building upon shortest path indoor path planning, considering the unpredictability of hazardous conditions, introduced evacuation betweenness centrality and evacuation centrality, thereby enhancing the safety of evacuation routes [22]. [PERSON] et al.