# A Quantum-Inspired Predator-Prey Algorithm for Real-Parameter Optimization
## 1 Introduction  
Metaheuristic optimization algorithms [1] have gained attention as a promising alternative to traditional optimization methods such as linear programming, nonlinear programming, and dynamic programming [2, 3]. Metaheuristic algorithms have the ability to explore large solution spaces efficiently and handle complex problems that are difficult to model mathematically or have a large number of variables and constraints [4]. These algorithms aim to solve optimization problems by mimicking the behavior of natural processes such as biological evolution [5, 6]. Some of the most prominent examples include _genetic algorithms_[6], which are inspired by biological evolution and natural selection, and _particle swarm optimization_, inspired by the behavior of a swarm of birds [7]. Another widely used form of swarm optimization is _ant colony optimization_, based on the behavior of ants as they forage for food [8]. Metaheuristics repeatedly create new solutions through operators such as crossover and mutation and combine them to obtain better solutions over the course of evolution (optimization) [6]. Metaheuristics are typically gradient-free optimization methods and hence suitable for models where gradient information is difficult to obtain, such as geoscientific models [9]. Since metaheuristic algorithms do not rely on gradient information, one of the major limitations is slow convergence; however, parallel implementations have been shown to address this limitation partly, for certain types of problems [9, 10, 11]. Apart from inspiration from biology and natural science, physics and mechanics have also inspired the design of metaheuristic optimization algorithms. Physics-based optimization algorithms are inspired by physical phenomena such as gravity, electricity, and magnetism [12, 13]. These algorithms simulate the physical behavior of particles or agents in the search space to find optimal solutions. The _gravitational search algorithm_ (GSA) [14] is based on the law of gravity and mass interactions where agents are a collectionof masses that interact with each other based on Newtonian laws of motion. GSA has the advantage of being fast and easy to implement, and it is also computationally efficient when compared to related algorithms. The _magnetic optimization algorithm_ (MOA) has a measure of the mass and magnetic field according to its fitness [15]. The fitter magnetic particles are those with larger magnetic fields and a higher mass. These particles are located in a lattice-like environment that uses the force of attraction onto their neighbors. The _electromagnetism-like mechanism algoritlm_ (EMO) is based on an attraction-repulsion mechanism to move the sample points toward optimality [16]. The _chaos-based optimization algorithm_ (COA) [17] employs a search within nonlinear, discrete-time dynamical systems to optimize complex functions of multiple variables. It generates chaotic trajectories to systematically explore the search space, effectively seeking global minima for intricate criterion functions, and provides a dynamic strategy for nonlinear optimization challenges. Quantum computing [18; 19] has opened up various opportunities the enhancement of computational power in the coming decades. Quantum computing has the potential to provide better infrastructure for artificial intelligence [20] and also pose threats when it comes to cyber-security, as the current security systems, such as as quantum computers with enhanced computational power, are anticipated to break encryption in the future [21]. However, major challenges in the implementation of quantum computing remain [22]. Even with an implementation that is functional, the design of algorithms for quantum computing would need to be revisited [23].