Optical Machine Learning and Quantum Networks

Yongli Zhao, Kaixin Zhang, Qingcheng Zhu +3 more

Quantum key distribution (QKD) technology can achieve information theoretical secure with one-time pad encryption. Machine learning (ML) provides systems the ability to automatically learn and improve from experience without being explicitly programmed. There are some applications of ML in telecommunication networks, such as traffic prediction and fault prediction. There is increasing interest in the field in applying ML to improve the performance of quantum communication networks. In this talk, I will introduce the applications of machine learning (ML) in quantum key distribution networks (QKDNs), including the applications in quantum layer, key management layer, and control and management layers.

Shahnawaz Ahmed, Carlos Sánchez Muñoz, Franco Nori +1 more

Philipp‐Immanuel Schneider, Lin Zschiedrich, Xavier Garcia‐Santiago +1 more

Shweta Jain, Anju Gandhi, Sanjay Singla +2 more

The two emerging technologies are machine learning and artificial intelligence, and as we all know, quantum computing is one of the most revolutionary developments in technology. Researchers are considering combining machine learning and quantum computing with the advancements in both fields. As a result, quantum machine learning-a fusion of these two fields-has evolved. It has the capacity to effectively address a wide range of real-world issues. Both fields will surely benefit from the combined results of the two fields. When the potential of quantum principles and its peculiarities is employed with machine learning, quantum machine learning reaches a very advanced level. Now that quantum concepts are being incorporated, traditional machine learning algorithms like SVM, PCA, and KNN are being reviewed as QSVM, Q-KNN, and QPCA, which are the most effective and powerful techniques for quantum machine learning. Quantum machine learning is a boom for the future; it will soon vastly surpass even the most advanced neural networks, deep learning systems, and machine learning systems of today. Modern machine learning is quicker than classical computing thanks to quantum machine learning, which is concerned with quantum software. Quantum data from an artificial quantum system is what QML is based on. In this paper, we will also study about the future network that is quantum network, which will be use by replacing classical networking. By enabling quantum communication between any two sites on Earth, a quantum internet is intended to fundamentally advance internet technology.

Ankur Kumar, Harish Dutt Sharma, Pintu Kumar

The integration of quantum networking with future Internet architectures introduces new challenges in resource optimization, routing, and network reliability. Traditional optimization techniques are often insufficient to handle the dynamic and probabilistic nature of quantum networks. This paper proposes a machine learning-assisted framework for quantum network optimization, where data-driven models are used to enhance decision-making in routing, resource allocation, and entanglement management. The approach combines classical learning algorithms with quantum network control mechanisms to improve adaptability and performance under varying network conditions. Experimental evaluation demonstrates improved efficiency in network utilization, reduced latency, and enhanced robustness compared to conventional methods. The results highlight the potential of machine learning-assisted optimization as a key enabler for scalable and intelligent quantum Internet infrastructures.

Nir Rotenberg