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

Illustration of satellite-based memory assisted quantum key distribution.
This project was selected as the overall winner of the INNOspace Masters 2020.

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, HU Berlin

Building a Photonic Processor for Energy-Efficient AI

Fig. 1: Concept of a neural network implemented on an optical circuit. From [Hue19].

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

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

Sketch of the employed reservoir computing scheme. From L. Appeltant, et al., Information processing using a single dynamical node as complex System, Nat. Commun. 2, 468 (2011).

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 through: DFG

Project Partner: Prof. Dr. Kathy Lüdge, TU Berlin 

Heterogenous quantum systems for single photon delay and pulse shaping

Sketch of the envisioned experiment: Single photons are generated in a quantum dot (QD) single photon source and subsequently stored in the Cs vapor quantum memory with on-demand readout.

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 through: DFG

Project Partner: Prof. Dr. Stephan Reitzenstein, TU Berlin 

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