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Keynote Lectures

Connecting the Dots in Quantum Computing: How Applications Drive Forward Developments
Jeanette Miriam Lorenz, Fraunhofer IKS, Germany, Germany

Characterization and Applications of Near-term Photonic Quantum Computers
Leonardo Novo, INL – International Iberian Nanotechnology Laboratory, Portugal, Portugal

Connecting the Dots in Quantum Computing: How Applications Drive Forward Developments

Jeanette Miriam Lorenz
Fraunhofer IKS, Germany

Short Bio
Since the beginning of 2023, PD Dr. habil. Jeanette Miriam Lorenz has been head of the department »Quantum Computing « at the Fraunhofer Institute for Cognitive Systems IKS. She has already been working as a Senior Scientist at Fraunhofer IKS since April 2021 on enabling reliable and robust quantum computing. She also leads the consortium “Quantum Algorithms for Application, Cloud & Industry” at the Munich Quantum Valley. Jeanette Lorenz studied physics and mathematics at the Friedrich-Alexander-Universität (FAU) Erlangen and the Ludwig-Maximilians-Universität (LMU) Munich. Her studies were followed by many years of research in experimental high-energy particle physics at CERN (European Organization for Nuclear Research) and in Munich. She has led numerous international research groups both at LMU Munich and at CERN. Her specialty was the search for dark matter particle candidates at the Large Hadron Collider. In 2014, she also received her PhD with honors from LMU Munich. In 2020, she habilitated and has since been teaching at the Faculty of Physics at LMU as an associate professor. At Fraunhofer IKS, she leads the projects in the field of quantum computing. The goal of her work is to significantly advance the development of quantum technologies in Germany and in particular to ensure that quantum computing can be used in a safe manner in applications.

Abstract
Quantum hardware makes significant progress in the direction of both scalability and fault-tolerance. At the same time, the question for which applications quantum computers will turn out to be useful turns into a very nuanced picture. Generally, we expect quantum computing to lead to disruptive changes in simulation, optimization and machine learning challenges, ranging from drug discovery to medical image classification tasks. However, to make this happen, we need to advance every single piece in the puzzle of quantum computing technologies: hardware, algorithms and software – always checking and assessing the impact of developments on eventually achieving benefits or advantages for concrete industrial and academic applications. In most practical applications, quantum computers will enter as quantum accelerators in a more complex quantum-classical workflow, where they are expected to handle complex computational tasks intractable for classical computers, while other parts of the computation are handled by classical computers. To realize then an advantage of the whole quantum-classical workflow over pure classical workflows, it is fundamentally important to ensure that the whole (hardware, software and algorithmic) pipeline works very efficiently. Within this talk, we would look at example applications and how they tell us which part of this pipeline to advance: this brings us from fundamental quantum algorithm issues to constructing efficient software stacks all the way up to applications.

Characterization and Applications of Near-term Photonic Quantum Computers

Leonardo Novo
INL – International Iberian Nanotechnology Laboratory, Portugal

Short Bio
Leonardo Novo is a research group leader at the International Iberian Nanotechnology Laboratory (INL) in Braga, Portugal. His main research interests are photonic quantum computation, quantum algorithms and the complexity of simulating quantum systems. Leonardo completed his PhD in 2017 at the University of Lisbon, with a thesis about quantum walks and their applications to quantum search algorithms and quantum transport problems. Afterwards, he joined the Centre for Quantum Information and Communication at Université Libre de Bruxelles as a postdoctoral researcher, a position he held until he joined INL in August 2022 as a Research Scientist. He is leading the Quantum and Linear-Optical Computation group at INL since September 2025.

Abstract
In this talk, I will start by discussing the boson sampling problem and how it led to one of the first experimental claims of quantum computational speedup. A boson sampler is a non-universal photonic quantum computer that is able to linearly interfere multiple photons over many optical modes and detect where the photons are at the output. I will discuss techniques to efficiently validate the correct functioning of a boson sampler even in regimes where the device cannot be simulated with classical computers. I will also show a how linear-optical devices can be used for efficient estimation of multivariate traces of quantum states, with applications ranging from quantum machine learning, eigenspectrum estimation, and the characterization of multiphoton indistinguishability.