Chimera graphs define the topology of one of the first commercially available quantum computers. A variety of optimization problems have been mapped to this topology to evaluate the behavior of quantum enhanced optimization heuristics in relation to other optimizers, being able to efficiently solve problems classically to use them as benchmarks for quantum machines. In this paper we investigate for the first time the use of Evolutionary Algorithms (EAs) on Ising spin glass instances defined on the Chimera topology. Three genetic algorithms (GAs) and three estimation of distribution algorithms (EDAs) are evaluated over 1000 hard instances of the Ising spin glass constructed from Sidon sets. We focus on determining whether the information about the topology of the graph can be used to improve the results of EAs and on identifying which are the characteristics of the Ising instances that influence the success rate of GAs and EDAs. Chimera graphs define the topology of one of the first commercially available quantum computers. A variety of optimization problems have been mapped to this topology to evaluate the behavior of quantum enhanced optimization heuristics in relation to other optimizers, being able to efficiently solve problems classically to use them as benchmarks for quantum machines. In this paper we investigate for the first time the use of Evolutionary Algorithms (EAs) on Ising spin glass instances defined on the Chimera topology. Three genetic algorithms (GAs) and three estimation of distribution algorithms (EDAs) are evaluated over 1000 hard instances of the Ising spin glass constructed from Sidon sets. We focus on determining whether the information about the topology of the graph can be used to improve the results of EAs and on identifying the characteristics of the Ising instances that influence the success rate of GAs and EDAs.Chimera graphs define the topology of one of the first commercially available quantum computers. A variety of optimization problems have been mapped to this topology to evaluate the behavior of quantum enhanced optimization heuristics in relation to other optimizers, being able to efficiently solve problems classically to use them as benchmarks for quantum machines. In this paper we investigate for the first time the use of Evolutionary Algorithms (EAs) on Ising spin glass instances defined on the Chimera topology. Three genetic algorithms (GAs) and three estimation of distribution algorithms (EDAs) are evaluated over 1000 hard instances of the Ising spin glass constructed from Sidon sets. We focus on determining whether the information about the topology of the graph can be used to improve the results of EAs and on identifying which are the characteristics of the Ising instances that influence the success rate of GAs and EDAs. Chimera graphs define the topology of one of the first commercially available quantum computers. A variety of optimization problems have been mapped to this topology to evaluate the behavior of quantum enhanced optimization heuristics in relation to other optimizers, being able to efficiently solve problems classically to use them as benchmarks for quantum machines. In this paper we investigate for the first time the use of Evolutionary Algorithms (EAs) on Ising spin glass instances defined on the Chimera topology. Three genetic algorithms (GAs) and three estimation of distribution algorithms (EDAs) are evaluated over 1000 hard instances of the Ising spin glass constructed from Sidon sets. We focus on determining whether the information about the topology of the graph can be used to improve the results of EAs and on identifying the characteristics of the Ising instances that influence the success rate of GAs and EDAs.

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Quantum Topology Adaptive Evolution Algorithm

Jincheng Zhang

Quantum Topology, a novel framework, presents a groundbreaking approach to evolutionary optimization through the manipulation of quantum entanglement and its associated topological properties. This paper introduces an Adaptive Evolution Algorithm (AEA) predicated on quantum topology, designed to accelerate the evolution of complex systems by leveraging the inherent adaptability and responsiveness of quantum systems. We detail the core mechanism of this algorithm, emphasizing the utilization of quantum entanglement as a key driver for evolutionary change, and demonstrate its efficacy in generating novel solutions to predefined challenges. The algorithm's unique approach promises significant advancements in areas such as materials design, optimization problems, and the exploration of complex systems. The paper concludes with a discussion of the potential applications and future research directions of this innovative approach.Quantum Topology, a novel framework, presents a groundbreaking approach to evolutionary optimization through the manipulation of quantum entanglement and its associated topological properties. This paper introduces an Adaptive Evolution Algorithm (AEA) predicated on quantum topology, designed to accelerate the evolution of complex systems by leveraging the inherent adaptability and responsiveness of quantum systems. We detail the core mechanism of this algorithm, emphasizing the utilization of quantum entanglement as a key driver for evolutionary change, and demonstrate its efficacy in generating novel solutions to predefined challenges. The algorithm's unique approach promises significant advancements in areas such as materials design, optimization problems, and the exploration of complex systems. The paper concludes with a discussion of the potential applications and future research directions of this innovative approach.

Aug 31, 20260 citations

1000

hard instances of the Ising spin glass constructed from Sidon sets. We focus on determining whether the information about the topology of the graph can be used to improve the results of EAs and on identifying the characteristics of the Ising instances that influence the success rate of GAs and EDAs.