## 2 Work Context  
According to Reference [11; 12; 13], supply chains, warehouses, and distribution centers occupy a very important position when storing and serving customer demand. Today, in order to be competitive within this sector, Logistics 4.0 has been created, which is known as the set of artificial intelligence technologies and techniques that seek the efficiency of the movements of materials and products in a factory or warehouse. Better time management helps logistics companies find and locate a material, reduce fatigue and possible workplace accidents, and spend less time documenting items. Many works of literature highlight these factors as the main ones where the loss of time and resources in a process require an urgent solution, and, precisely, it is technologies, such as Artificial Intelligence and the Internet of Things (IoT), which today allow us to optimize them [14; 15; 16]. Only in the last decade, researchers have focused on addressing the multiple order picking planning problems. The study of the efficiency of a Warehouse can be addressed based on multiple parameters. According to Reference [17], there are three key considerations: (1) Performance Measure (time, cost, productivity, and service), (2) How we model the warehouse (Analytical model, Mathematical Model, or Simulation), and (3) the combination of factors (storage location assignment, routing, order batching, or other order picking planning problems). Based on data from Reference [17], we can see the percentage of relevance of the considered order picking planning problems based on the percentage of papers that are related to such challenges. As we can see in Figure 1, Picking and Batching are the top priorities based on the research contributions. Order preparation (picking) is one of the most frequent and costly operations in labor [11; 12] since it is responsible for recovering the items required by the orders of customer orders (could also be supplied, but, in this article, we focus exclusively on sales orders), and to create the batches, grouping several orders of orders in a picking list to collect all the batch demands in a single warehouse tour. In this last part of order preparation, our quantum algorithm comes into action to optimize the routes traveled to achieve efficient picking. There are many techniques and strategies for solving the picking problem. The most striking are \"The selected techniques for evaluation include A *\" [18], \"Potential Fields (PF)\", \"Rapidly-Exploring Random Trees * (RRT *)\" [19; 20; 21], and \"Variations of the Fast-Marching Method (FMM)\" [22]. Other strategies have explored using the TSP and the VRP as algorithms to solve the picking problem. In this case, if the number of order orders per lot is greater than two [23], picking becomes an NP-Hard problem in which the number of possible lots and binary variables increase exponentially with the number of purchase orders [23]. From there, several heuristic techniques, methods and algorithms (for example, genetic) were created to relax these difficulties [11; 13; 16; 24; 25; 26]. However, and as mentioned above, depending on the volume of data, the computational cost of the algorithm becomes intractable for classical computing. The latter leads us to explore new approaches to the large-scale picking problem, and one of the approaches to take into account to solve this task is quantum computing [6]. Quantum computing could help us change the degree of complexity of the problem, enhanced by its high computing power. Among the great fields where quantum computing is called to stand out is constraint satisfaction problems (CSP) [27]. One of the useful algorithms in this field is Quadratic Unconstrained Binary Optimization (QUBO) problems [28]. From [PERSON] [29] to [PERSON]'s idea of considering the simulation of systems in quantum mechanics by other quantum systems [30], interest in creating new ways of solving them has grown dramatically.