Marc Geitz

Sumit Chaudhary, Davide Li Calsi, JinHyeock Choi et al.

Quantum Key Distribution (QKD) offers provably secure, information-theoretic key exchange, but in long-distance scenarios without quantum repeaters, Trusted Nodes (TNs) are commonly employed despite introducing critical security risks. We propose a redundant key management method for QKD network that combines Twin Field QKD (TF-QKD) (or Measurement-Device Independent (MDI)-QKD) with a novel key-routing scheme to eliminate the need for truly trusted TNs. Quantum measurements are handled entirely within the network, minimizing end-user hardware requirements. Multiple QKD links connect intermediate nodes such that a successful attack requires the collusion of multiple adversarial nodes, greatly enhancing security over the traditional TN model. In this contribution, we discuss the tradeoff between security, key rates, and distances supported by the new method. Our analysis reveals that the improved redundant key management system may enable true end-to-end connectivity over several thousand kilometers while maintaining high security standards.

Wafa Labidi, Holger Boche, Christian Deppe et al.

The coordination of large populations of highly constrained devices, such as micro- and nanoscale agents in biomedical applications, poses fundamental challenges to classical communication paradigms. In scenarios such as targeted drug delivery, devices operate under severe limitations in energy, size, and communication capabilities, while requiring precise and selective activation within spatially localized regions. In this work, we propose the framework of Joint Detection and Identification (JDAI) as a system-level approach for scalable control under such constraints. The key idea is to shift from reliable message transmission to a control-oriented paradigm, in which devices locally decide whether a broadcast signal is relevant. This enables implicit addressing and subset activation without the need for explicit per-device communication. We demonstrate how message identification can be combined with sensing. This enables the realization of a closed-loop system that integrates detection, communication, and actuation. Using the example of targeted nanorobot therapy, we analyze the interplay between sensing resolution, communication constraints, and system dynamics. In particular, we show that while identification exhibits favorable asymptotic scaling, practical implementations are governed by finite blocklength effects, noise, and latency. The proposed framework complements existing physical-layer communication approaches, including molecular, electromagnetic, and acoustic techniques, by providing a control-layer abstraction for scalable subset selection. Overall, JDAI connects identification-theoretic principles with system-level design to control large, resource-limited environments.

Holger Boche, Ning Cai, Yiqi Chen et al.

We compare the strong secrecy capacities of Arbitrarily Varying Wiretap Channels (AVWCs) and General Arbitrary Varying Wiretap Channels (GAVWCs) with their capacities under semantic secrecy constraint and other equivalent cryptographic secrecy constraints. It turns out that the average error and strong secrecy capacity of an AVWC is always equal to its maximal error and semantic secrecy capacity. However, this equivalence does not hold for all general communication systems, and we prove this by a counterexample. We also show that, for the GAVWC, semantic security and the other cryptographic security measures considered achieve the same capacity values. Finally, we bound the gap between the strong secrecy capacity and the semantic secrecy capacity for the GAVWC. The gap vanishes if the choice of the jammer is sub-double-exponential with respect to the block length n, which gives a sufficient condition for the strong and semantic secrecy capacities to be equal for GAVWCs.

Yiqi Chen, Holger Boche, Marc Geitz

This paper studies the hierarchical joint source-channel coding with information leakage constraint in the first-phase reconstruction and distortion constraints. The receiver's access to the data varies and is evaluated by the quality of the side information. Due to the consideration of channel capacity limitation or the efficiency of the system performance, the encoder may send some additional information in Phase 1 that can only be decoded in Phase 2 with higher-quality side information. While this can optimize the overall performance, the additional information causes excessive information leakage. We provide general inner and outer bounds for the conditions such that a given distortion-leakage pair $(D_1,D_2,L)$ is achievable, together with a capacity-achieving condition.

Tobias Müller, Kyrill Schmid, Daniëlle Schuman et al.

The expansion of Fiber-To-The-Home (FTTH) networks creates high costs due to expensive excavation procedures. Optimizing the planning process and minimizing the cost of the earth excavation work therefore lead to large savings. Mathematically, the FTTH network problem can be described as a minimum Steiner Tree problem. Even though the Steiner Tree problem has already been investigated intensively in the last decades, it might be further optimized with the help of new computing paradigms and emerging approaches. This work studies upcoming technologies, such as Quantum Annealing, Simulated Annealing and nature-inspired methods like Evolutionary Algorithms or slime-mold-based optimization. Additionally, we investigate partitioning and simplifying methods. Evaluated on several real-life problem instances, we could outperform a traditional, widely-used baseline (NetworkX Approximate Solver) on most of the domains. Prior partitioning of the initial graph and the presented slime-mold-based approach were especially valuable for a cost-efficient approximation. Quantum Annealing seems promising, but was limited by the number of available qubits.

Marc Geitz, Cristian Grozea, Wolfgang Steigerwald et al.

The subject of Job Scheduling Optimisation (JSO) deals with the scheduling of jobs in an organization, so that the single working steps are optimally organized regarding the postulated targets. In this paper a use case is provided which deals with a sub-aspect of JSO, the Job Shop Scheduling Problem (JSSP or JSP). As many optimization problems JSSP is NP-complete, which means the complexity increases with every node in the system exponentially. The goal of the use case is to show how to create an optimized duty rooster for certain workpieces in a flexible organized machinery, combined with an Autonomous Ground Vehicle (AGV), using Constraint Programming (CP) and Quantum Computing (QC) alternatively. The results of a classical solution based on CP and on a Quantum Annealing model are presented and discussed. All presented results have been elaborated in the research project PlanQK.