Distributed Quantum Machine Learning

Quantum computers can solve specific complex tasks for which no reasonable-time classical algorithm is known. Quantum computers do however also offer inherent security of data, as measurements destroy quantum states. Using shared entangled states, multiple parties can collaborate and securely compute quantum algorithms. In this paper we propose...

Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches

Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods. However, computer power is increasingly becoming a limiting factor in machine learning tasks. The rise of quantum computers...

Quantum Machine Learning: Benefits and Practical Example

A quantum computer that is useful in practice, is expected to be developed in the next few years. An important application is expected to be machine learning, where benefits are expected on run time, capacity and learning efficiency. In this paper, these benefits are presented and for each benefit an example application is presented. A quantum...

Three Quantum Machine Learning Approaches or Mobile User Indoor-Outdoor Detection

There is a growing trend in using machine learning techniques for detecting environmental context in communication networks. Machine learning is one of the promising candidate areas where quantum computing can show a quantum advantage over their classical algorithmic counterpart on near term Noisy Intermediate-Scale Quantum (NISQ) devices. The...

Quantum Hopfield neural networks: A new approach and its storage capacity