Summer School on 6G and Quantum Communication Networks

The 2nd edition of the quantum communication networks summer school is coming up! This time with a 6G supplement: the transCampus Summer School on 6G and Quantum Communication Networks.

We are delighted to have renowned experts from academia and industry as our guests again this year. They will present the results and methods of their research in quantum and 6G technologies, focusing on the role that quantum technologies can play in 6G standardisation.

You can find the complete programme below.

Registration

Are you interested? Please register on eventbrite for the two-day event taking place in London on 19-20 June 2025.

Lecturer

Programme

Venue

Bush House, Strand Campus, King’s College London, London WC2R 2LS.

Thursday, 19 June

9 to 11.30 a.m.

Lecture and work session I: 5G and 6G: Architectural Characteristics, Use Cases, and Standardisation Efforts

Enrico Buracchini (TIM)

The lecture will be dedicated to 5G main use cases and rationale, 5G main enablers such as MIMO, Slicing, NSA and SA,etc…, 5G releases and 5G Adv evolution, 6G main R&D streams and standardization progress and status in 3GPP.

12 a.m. to 1 p.m. | 2 to 3:30 p.m.

Lecture and work session II: Quantum Vector Magnetometry 1 + 2

Zoran Radivojevic (NOKIA Bell Labs)

This session will provide insights into the theory of quantum sensing, focusing on Quantum Vector Magnetometry. Next, it will highlight the applications in different fields where quantum sensing will be part of 6G infrastructure.  

4 to 6:30 p.m.

Lecture and work session III: To be determined

Simone Severini (AWS)

to be determined

Friday, 20 June

9 to 11:30 a.m.

Lecture and work session IV: Exploring the Potential of Quantum Computing for Telecommunication Networks

Ahsan Javed Awan (Ericsson)

The morning session will present insights from our research on multi-chip quantum processors and the evaluation of quantum methods across a range of telco workloads. Our findings suggest that quantum computing has the potential to offer computational advantages in the control and management planes of telco networks, provided key challenges such as scalability and fault tolerance are addressed. However, the complexity of optimizing non-linear cost functions limits the applicability of quantum computing in the radio domain. To fully harness the power of quantum computing in telecommunications, we propose hybrid processing models that integrate multi-chip quantum systems with classical computing to achieve optimal performance. Additionally, the lecture will highlight other promising developments, such as training quantum machine learning models with fewer parameters and data points, as well as leveraging classically simulable quantum circuits for network design and optimization.

12 a.m. to 1 p.m. | 2 to 3:30 p.m.

Lecture and work session V: Algorithmic Advances in Quantum Computing with Neutral Atoms 1 + 2

Savvas Varsamopoulos (Pasqal)

Arrays of optically trapped neutral atoms have recently been highlighted as a good candidate platform for Quantum Computers, by demonstrating high levels of scalability, precise control, and low power consumption. Furthermore, they allow the possibility to run both analogue protocols which are native to this architecture, as well as more mainstream digital quantum algorithms, as seen by recent demonstrations of error correction protocols.
This talk will explore the core characteristics and benefits of neutral atom quantum processing units (QPUs), outline their different operational modes, examine current technological limitations, and demonstrate their potential through key use cases in diverse physics and engineering challenges—paving the way toward applications at an industrial scale.
In particular, it will focus on problems such as: (i) graph-based approaches to Machine Learning, which are naturally suiting the analogue mode of neutral atom machines; (ii) Scientific Machine Learning (SciML) approaches, targeting the solution of (stochastic) Differential equations that can seamlessly embed actual data where available and exploit generalization properties; (iii) extremization & combinatorial optimization, which is a common target of quantum methodologies that we address via appropriate embeddings to reduce such optimizations to either Quantum-SciML or graph theoretic strategies.

4 to 6:30 p.m.

Lecture and work session VI: Quantum Kernels for Supervised Learning

Alessandra Di Pierro (University of Verona)

Kernel methods are a powerful class of machine learning algorithms that allow us to perform complex, non-linear transformations of data without explicitly computing the transformed feature space. 
The quantum kernel methods emphasize the importance of quantum feature map which describes how the classical data vectors is mapped to the quantum states. 
The use of this seemingly easy approach to quantum machine learning actually brings about several problems in the choice of the appropriate embedding leading to an effective quantum advantage.
In this lecture, I will discuss some of these problems and present possible solutions and their applications.

Organisers