Sixth-generation wireless (6G) technology has been focused on in the wireless research community. Global coverage, massive spectrum usage, complex new applications, and strong security are among the new paradigms introduced by 6G. However, realizing such features may require computation capabilities transcending those of present (classical) computers. Large technology companies are already exploring quantum computers, which could be adopted as potential technological enablers for 6G. This is a promising avenue to explore because quantum computers exploit the properties of quantum states to perform certain computations significantly faster than classical computers. This paper focuses on routing optimization in wireless mesh networks using quantum computers, explicitly applying the quantum approximate optimization algorithm (QAOA). Single-objective and multi-objective examples are presented as robust candidates for the application of quantum machine learning. Moreover, a discussion about quantum supremacy estimation for this problem is provided.

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6G will lay its foundations on new paradigms and requirements. This new technology is expected to provide global coverage, exploring a huge spectral chunk (sub-6 GHz, mmWave, THz and optical frequency bands) to further increase data rates and connection density. In addition, 6G networks will enable a new range of smart applications with the aid of Artificial Intelligence, Big Data technologies and the emerging paradigms of Quantum Computing and Quantum Machine Learning. This paper focuses on these new paradigms and proposes an indoor location method based on the well known Euclidean Distance in its quantum version. Specifically, an example of this use case is shown, which is executed in one quantum computer from IBM Quantum Experience.The paper analyses the obtained results while exploring new challenges and fields of application of the technology. Results show that the quantum approach is accurate enough to calculate Euclidean Distance between two vectors while outperforming classical computation if vector size is big enough.

This demonstration presents FoReCo [4], a solution to recover lost control commands in remotely controlled robots. In the demonstration, visitors use a joystick to remotely control a robotic arm under the presence of packet losses in the wireless medium. The lost control commands result in a distorted trajectory of the robotic arm, thus, we deploy FoReCo to recover lost control commands using an ML model that we train with a real-world dataset. The demonstration shows how FoReCo recovers the lost commands, and how the robot arm operates smoothly despite the losses that are present in the wireless medium. CCS CONCEPTS• Networks → Cyber-physical networks.