Physical Foundations of IT Security

Current Research Projects

Optical Systems for Classical and Quantum Computing for Machine Learning in Orbit (OMLO)

Machine learning (ML) is now the principal approach to processing complex sensor data (such as image and video data). The standard method for this is the use of artificial neural networks simulated on binary computer architectures. This creates the need for ever-increasing computing power, which can be served on the ground in part by specialized digital hardware such as graphics cards, tensor-flow processors, etc. For data processing in orbit, these possibilities are only available to a limited extent, since the special requirements for energy consumption and radiation hardness are generally not met. Data transfer to the ground and processing there is also difficult to realize due to the immense amounts of data. Therefore, high-performance computers for on-board data processing would be desirable. Optical systems for classical and quantum computing have a high potential to fill this gap and enable ML and other complex computations even in space environments. This would result in a technological leap in the field of space science. 

Funded by: BMWK

Projektpartner: Enrico Stoll, TU Berlin


Quanten-Token based on alkaline metals and Xenon (Q-ToRX)

Quantum communication is an important building block for the future security of digital infrastructures. In quantum communication, the exchange of cryptographic keys is based on fundamental physical laws that ensure security even in the event of attacks by quantum computers. In addition to secure data transmission, quantum communication also offers new ways to securely authenticate users of digital systems and store private data on a network. So-called quantum tokens could ensure all of this in the future. Analogous to today's security tokens such as bank cards, transponders or transaction numbers, quantum tokens are conceivable as an authentication solution using quantum physical properties. On the way to their realization, it is important for research to further improve important key parameters of quantum physical systems, such as quantum memories, and to find efficient application possibilities.

In order to make quantum communication methods usable, e.g. for the secure authentication of system users by means of quantum tokens, a long-term stable and transportable quantum memory is needed. The aim of the project "Quantum tokens based on alkaline metals and xenon (Q-ToRX)" is therefore to extend the storage time of quantum information in quantum memories at room temperature to the range of hours. Gas cells containing xenon and alkali atoms are used for this purpose. Research will be conducted on how to combine the long storage time of xenon with the efficient optical interface of alkalis. In parallel, the robustness and technological simplicity of the system used will be further developed in a multidimensional approach.

The storage of light quanta as carriers of quantum information in warm atomic gases is of particular interest, since neither complex cooling mechanisms nor large magnetic fields are required. This makes such quantum memories ideal for field applications. The results of the project are also highly relevant for a variety of current research areas where the storage of quantum information is required, for example in quantum encryption.

Funded by: BMBF

Project partner: Physikalisch-Technische Bundesanstalt, Leibniz Universität Hannover


Photonic Integrated Quantum Computer (QPIC-1)

Quantum computers have the potential to perform complex computations much more efficiently than classical computers by specifically exploiting the remarkable properties of quantum physics. The expected speed advantage is so substantial that problems become computable that are considered unsolvable with classical computers. However, in order to solve problems from practical applications, systems must be developed that can work with a considerably larger number of quantum bits, so-called qubits, than previous prototypes.

The goal of this project is the development of a novel platform for a quantum computer using single light particles, so-called photons, as qubits. For this purpose, novel sources are to be developed that generate quantum light, as well as integrated photonic circuits in which the information processing takes place.

The processor reads out the generated so-called cluster state, which consists of a large number of entangled photons and thus qubits, one after the other, i.e. qubit by qubit. Thus, it is possible to work with a much larger number of qubits than can be addressed by the processor simultaneously. This project therefore forms the basis for scalable photonic quantum computers that can operate with thousands of qubits, making quantum computing practical for real-world applications. The results achieved in this project will be protected by patents and subsequently exploited commercially, thus securing Germany a leading role in this emerging technology.

Funded by: BMBF

Project partner: TU München, University of Paderborn, HU Berlin, Ferdinand-Braun-Institute, Universität des Saarlandes, FU Berlin, Q.Ant GmbH,


Heterogenous quantum systems for single photon delay and pulse shaping (HQ.Sys)

Photonic quantum technology is an exciting field in science and technology. Potential applications include secure quantum communication, quantum computing and on the long-term the Quantum Internet. These have in common that information is encoded in single photons acting as flying qubits. Importantly, these flying qubits need to be efficiently interfaced with stationary qubits to implement quantum memories and quantum gates. The overarching goal of this project is to develop and test a quantum memory for the storage and retrieval as well as for the efficient spectral/temporal waveform manipulation of single quantum dot photons. Our project realizes for the first time a heterogeneous quantum interface between semiconductor quantum dots and a quantum memory realized in alkaline atoms. This key building block in quantum nanophotonics enables the generation of almost perfectly indistinguishable photons and near unity entanglement swapping fidelity in quantum repeater protocols. At the same time, we envision that quantum information can be encoded into the temporal envelope and phase of the single photons allowing for high capacity quantum information transfer with large alphabet. The underlying technological approach is to combine the efficient and on-demand photon generation in semiconductor quantum dots with quantum memories implemented in warm atomic vapor. The source is realized deterministically by in-situ electron beam lithography of single-QD CBR devices. Here, the advanced in- situ EBL nanotechnology platform guarantees the fabrication of QD quantum light source with well-controlled emission wavelength and high photon extraction efficiency. The memory follows a fast ladder EIT scheme in warm Cs vapor.

Funded by: DFG

Projektpartner: Prof. Dr. Stephan Reitzenstein, TU Berlin 

Hybrid photonic computing in delay-coupled non-linear systems with memory (HyPCom)

In recent years, artificial intelligence (AI) as a groundbreaking innovation has developed into a driver of digitization and autonomous systems in all areas of life. This has created great potential for mastering global challenges, such as environmental, resource and climate protection, as well as the security and performance of communication and IT systems. The current progress of AI, especially in the field of machine learning, is based on the exponential increase in hardware performance and its use for processing large amounts of data. However, despite the famous nature of Moore’s Law, the overall increase in hardware performance has slowed down in recent years, as for example measured by transistor-density. This motivates research into other approaches. Reservoir computing is one such promising novel paradigm, which has emerged in analogue neuromorphic computing. It shows great potential to overturn the digital transistor-hegemony and explore novel computational mechanisms and substrates for artificial intelligence. In a joint theoretical and experimental effort, this project aims at realizing non- linear optical networks with reconfigurable topology, enabled by combining feedback-coupled optical amplifiers with coherent optical memories. The potential of these systems for neuro-inspired information processing in the reservoir computing approach is explored.

Funded by: DFG

Project partner: Prof. Dr. Kathy Lüdge, TU Ilmenau



Quantum Memories for Secure Communication in Tomorrow‘s Society (QuMSeC)

Photonic quantum memories are so far missing key components for the second quantum revolution and enable a plethora of novel applications. For example, quantum networks promise provable security in communication and also the possibility for connecting quantum computers and simulators for calculations on distributed machines.

We focus on the one hand on the development of non-classical light sources and quantum memories for single photons. On the other hand, security-relevant applications of these key components in the emerging quantum technologies are explored. Most prominent, quantum secured communication and optical computation in the quantum and classical regime are in the research focus. At the beginning of the PhD work, a quantum memory for single photons in alkaline vapor at room temperature is built and optimized with respect to noise, efficiency, bandwidth and storage time. Special remark is on using components suitable for future airborne and space missions. Later, the memory is tested in applications.

Funded by: INNOspace Masters, BMWi through DLR.

Project partner: Dr. Markus Krutzik, FBH/HU Berlin

Escape Challenge Quantum Technologies (EsCQuTe)

Quantum technologies will fundamentally change our world. Their potential should be made tangible and understandable for many people. Therefore, we develop a live escape game, which challenges the players to collaboratively solve exciting puzzles. The players are immersed in a world where second generation quantum technologies are already being applied. In a playful way, the visitors are motivated to deal with the remarkable properties of quantum mechanics.

Funded by: BMBF

Project partner: Dr. Robert Richter

Building a Photonic Processor for Energy-Efficient AI

Classical digital computer architectures are visibly approaching their technological and physical limits. Thus, there is a growing interest in developing post-digital computing approaches to overcome these limitations. Besides quantum computers, approaches that emulate neuromorphic processes represent a very promising alternative because they mimic the massively parallel, energy-efficient computations carried out by the human brain. Such computations constitute the building blocks of the pattern recognition algorithms underpinning the success of machine learning and artificial intelligence (AI). Optically integrated systems promise 2–3 orders of magnitude higher energy efficiency compared to today's electronic approaches [Pen18]. Among others, post-digital computer concepts will enable numerous new applications for AI in places like data centers or security systems, as well as autonomous vehicles, drones and satellites – any area where massive amounts of computations need to be done but is limited by power and time. 

In this project we will realize machine learning with optical neural networks in free-space bulk optics. That is, we want to use light to power machine learning, instead of electrons, due to the potential advantages that a light-based neural network system has over one that utilizes conventional GPU chips.

[Hue19] T.W. Hughes, M. Minkov, Y. Shi, and S. Fan, ”Training of photonic neural networks through in situ backpropagation and gradient measurement,” Optica 5, 864 (2018)

[Pen18] H.-T. Peng et al. “Neuromorphic Photonic Integrated Circuits” IEEE JSTQE, 2018


Funded through HEIBRiDS.

Project partner: Prof. Guillermo Gallego, Technische Universität Berlin