Quantum Channel Optimization: Integrating Quantum-Inspired Machine Learning With Genetic Adaptive Strategies

Abstract

There is an ever-growing need for optimizing efficient nano and quantum communication systems and this demand calls for more advanced optimization techniques than those that have been around for quite some time. The conventional genetic algorithms (GAs) proved to be quite helpful in general but proved to be poor when it comes to fast convergence and adaptability to the changing communication environment. Moreover, fault tolerance and resource efficiency in these communication channels are poorly addressed, giving rise to a huge roadblock to practical implementations in a number of different use cases. This research presents a set of novel hybrid optimization methods aimed at addressing the issues previously mentioned. In the first place, a Quantum-inspired Genetic Algorithm (QIGA) with Simulated Annealing (SA) Initialization is proposed through augmenting traditional genetic operations with the quantum computing paradigm. In the first instance, the hybrid approach works by utilizing the parameters derived from SA while initializing the GA population, hence effective exploration of solution space. Additionally, the Adaptive Genetic Algorithm with Reinforcement Learning (AGA-RL) is presented, which uses real-time feedback from the current environment to modify the GA parameters. This way, the system will be much more adaptive and hence be able to adjust the GA so that it can match the rapidly changing channel conditions. Simultaneously, the Fault-Tolerant Genetic Algorithm with Error-Correcting Codes (FTGA-ECC) is developed to ensure reliable data transmission, especially in an error-prone environment. To make up for resource constraints, the Energy-Aware Genetic Algorithm with Dynamic Resource Allocation (EAGA-DRA) strategy ensures that resources are well-managed by dynamically changing allocation based on the optimization process and the particular needs of the channel, hence reducing energy overhead and spatial overhead without compromising channel quality. A great leap is in the integration of Machine Learning through the Deep Reinforcement Learning-guided Genetic Algorithm (DRL-GA). The above approach exploits deep reinforcement learning to fine-tune the exploration and exploitation strategies of the GA, hence providing faster convergence and improved solution quality. All of these methods are validated by the Quantum-inspired Benchmarking Framework (QIBF), which forms a systematized comparative platform to evaluate different optimization techniques from the perspective of QCA-based communication systems. The above approaches not only provide the improvement in efficiency and reliability but also contribute to enhancing the adaptability of communication systems and provide a very good base for future research and development in the field of quantum communications and beyond, with potential applications extending to secure communication, quantum computing, and nanotechnology-based devices & scenarios. This work is multifold, promising significant advancements in the field of quantum communication and beyond. The empirical findings showed that the suggested algorithms perform significantly better than traditional approaches in several important performance metrics, such as robustness to channel errors, adaptability to changing signal-to-noise ratios, convergence speed under dynamic conditions, and solution quality in highly variable environments.INDEX TERMS Quantum-inspired genetic algorithm, adaptive genetic algorithm, fault-tolerance, energy-efficient optimization, deep reinforcement learning.

Keywords

• Quantum-inspired Genetic Algorithm
• Adaptive Genetic Algorithm
• Fault-Tolerance
• Energy-Efficient Optimization
• Deep Reinforcement Learning