Evaluating the Q-score of Quantum Annealers

We report the Atos Q-score for D-Wave’s quantum devices, classical algorithms and hybrid quantum-classical solver. Computing the Q-score entails solving the Max-Cut problem for increasingly large graphs. This work presents the first computation of the Q-score on a quantum device and shows how these quantum devices compare to classical devices at...

A Multi Service Capacitated Facility Location Problem with Stochastic Demand

This paper considers the problem of identifying optimal locations for wireless service installations in smart cities. The problem is modelled as a facility location problem with multiple service types, known as the Multi Service Facility Location Problem (MSCFLP). Given a set of potential facility locations and demand point data, the goal is to...

Quantum Computing for Radar and Sonar Information Processing

Radar and sonar information processing is a promising application area of quantum computing in the near future. Many use cases in this area can are computational heavy and might benefit greatly from a quantum approach. In this paper, an overview of use cases in this application area is given and scored on quantum readiness, added value and...

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...

Quantum-Classical Solution Methods for Binary Compressive Sensing Problems

Compressive sensing is a signal processing technique used to acquire and reconstruct sparse signals using significantly fewer measurement samples. Compressive sensing requires finding the most sparse solution to an underdetermined linear system, which is an NP-hard problem and as a consequence in practise is only solved approximately. In our...

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...

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...