QUANTUM-INSPIRED MACHINE LEARNING FOR 6G FUNDAMENTALS, SECURITY, RESOURCE ALLOCATIONS, CHALLENGES, AND FUTURE RESEARCH DIRECTIONS

Abstract : Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search.Recently, there has been a proliferating growth of the size of multi-dimensional datasets, the input-output space dimensionality, and data structures. Hence, the conventional machine learning approaches in data training and processing have exhibited their limited computing capabilities to support the sixth-generation (6G) networks with highly dynamic applications and servicesIn this regard, the fast developing quantum computing with machine learning for 6G networks is investigated. Quantum machine learning algorithm can significantly enhance the processing efficiency and exponentially computational speed-up for effective quantum data representation and superposition framework, highly capable of guaranteeing high data storage and secured communications.We present the state-of-the-art in quantum computing and provide a comprehensive overview of its potential, via machine learning approaches.
 EXISTING SYSTEM :
 ? Building fault-tolerant solutions for deploying quantum-inspired ML is an important direction to explore, and it is worth considering how existing solutions would be scaled to fit the requirements of security for the new generation of radios. ? The only difference in their operations lies in the communication between the network layers. For classical NNs, at the end of a given process, the existing perceptron duplicates its output to the next perceptron layer of the network.
 DISADVANTAGE :
 ? Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search. ? “The curse of dimensionality” coupled with the exploration strategy problem leads to difficult problems in practical applications as the number of datasets grows exponentially with its dimension. ? The emerging QC is a promising paradigm and can play a significant role in addressing computationally complex problems, such as large-number factorization non-convex optimization, and exhaustive computation and search.
 PROPOSED SYSTEM :
 ? QC capabilities incorporated into ML are proposed to address this challenge. The integration of QC capability with the ML algorithms can help in the effective utilization of big data analytic in the IoT environments ? A graph neural network-based framework was proposed to address resource allocation problems in wireless IoT networks ? To address the issue of big data, a quantum-inspired SVM algorithm has been proposed to enhance achieve exponential speed-up for least squares SVM ? Specifically a topic like a hybrid algorithm, proposed algorithms that partially run both on quantum computers and classical computers can be further investigated.
 ADVANTAGE :
 ? A cost function is used to determine the effectiveness of a NN, which measures the network output proximity to the expected output ? An unsupervised machine learning (UML) algorithm is used to analyze and classify unlabeled datasets ? The role of oracles, which are used differently for algorithms providing security to the networks and infrastructures. Security of quantum-inspired ML for 6G would need a considerable understanding of the post-quantum attacks as the protocols built for quantum-enabled systems are breachable with enhanced capacity, computing power and cloning
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