arXiv subjects

Lars Fischer

Publications and source records attributed to Lars Fischer.

12 recordsLinked to original sources

arXiv2026

Optimizing Superconducting Microwave Cavities for Gravitational Wave Sensing

Superconducting microwave cavities loaded with radio frequency fields are a powerful tool to search for weak forces or electromagnetic perturbations due to new physics. One source of such signals can be high-frequency gravitational waves emitted from cosmological or unknown astrophysical events. However, large sensitivity improvements are still necessary to reach the parameter space motivated by theoretical models. In this work, we present a formalism to guide the design of such detectors across a broad range of frequencies and signal forms. By incorporating the interdependencies of all relevant parameters and the back-action of the electromagnetic fields on the cavity structure, we describe how figures of merit can be derived for a broad class of relevant experimental setups. Using a two-dimensional model, we demonstrate this formalism and present examples for optimized cavity geometries. We find that the choice of geometry alone can increase the signal-to-noise power ratio by an order of magnitude compared to an existing prototype of the same size. We also show that different physics goals lead to different optimal cavities, thus proving the need to consider such figures of merit at an early stage when designing a new detector.

gr-qc↗

arXiv2026

Digital programming of spin correlations in a fermionic lattice quantum simulator

Analog quantum simulation provides a highly controlled platform to study diverse quantum many-body phenomena. However, current methods for state initialisation are limited to thermal ensembles or uncorrelated product states. Here we present a hybrid approach that complements analog preparation with a digital quantum-gate protocol. This approach enables the engineering of target states with specific, long-range spin-correlations from the same initial resource state. By applying collisional gates to adiabatically prepared and filtered four-fermion singlet chains, we program diverse spin-correlation patterns, including that of a Heisenberg chain. We measure the spin correlations using a sequence of quantum gates followed by singlet-pair measurements. Our method paves the way to the targeted preparation of strongly correlated states of matter.

cond-mat.quant-gas↗

arXiv2026

Rendezvous and Docking of Mobile Ground Robots for Efficient Transportation Systems

In-Motion physical coupling of multiple mobile ground robots has the potential to enable new applications like in-motion transfer that improves efficiency in handling and transferring goods, which tackles current challenges in logistics. A key challenge lies in achieving reliable autonomous in-motion physical coupling of two mobile ground robots starting at any initial position. Existing approaches neglect the modeling of the docking interface and the strategy for approaching it, resulting in uncontrolled collisions that make in-motion physical coupling either impossible or inefficient. To address this challenge, we propose a central mpc approach that explicitly models the dynamics and states of two omnidirectional wheeled robots, incorporates constraints related to their docking interface, and implements an approaching strategy for rendezvous and docking. This novel approach enables omnidirectional wheeled robots with a docking interface to physically couple in motion regardless of their initial position. In addition, it makes in-motion transfer possible, which is 19.75% more time- and 21.04% energy-efficient compared to a non-coupling approach in a logistic scenario.

eess.SY↗

arXiv2025

Protected quantum gates using qubit doublons in dynamical optical lattices

Quantum computing represents a central challenge in modern science. Neutral atoms in optical lattices have emerged as a leading computing platform, with collisional gates offering a stable mechanism for quantum logic. However, previous experiments have treated ultracold collisions as a dynamically fine-tuned process, which obscures the underlying quantum- geometry and statistics crucial for realising intrinsically robust operations. Here, we propose and experimentally demonstrate a purely geometric two-qubit swap gate by transiently populating qubit doublon states of fermionic atoms in a dynamical optical lattice. The presence of these doublon states, together with fermionic exchange anti-symmetry, enables a two-particle quantum holonomy -- a geometric evolution where dynamical phases are absent. This yields a gate mechanism that is intrinsically protected against fluctuations and inhomogeneities of the confining potentials. The resilience of the gate is further reinforced by time-reversal and chiral symmetries of the Hamiltonian. We experimentally validate this exceptional protection, achieving a loss-corrected amplitude fidelity of $99.91(7)\%$ measured across the entire system consisting of more than $17'000$ atom pairs. When combined with recently developed topological pumping methods for atom transport, our results pave the way for large-scale, highly connected quantum processors. This work introduces a new paradigm for quantum logic, transforming fundamental symmetries and quantum statistics into a powerful resource for fault-tolerant computation.

quant-ph↗

arXiv2025

Laboratory Frame Representation for General High-Frequency Gravitational Waveforms

Next-generation gravitational wave (GW) experiments will explore higher frequency ranges, where GW wavelengths approach the size of the detector itself. In this regime, GWs may be detected not just through the well-known mechanical deformation by tidal forces but also via induced effective currents in electromagnetic background fields. However, the calculation of this signal requires the GW metric in laboratory coordinates of the detector, and an accurate transformation to all orders into this frame is necessary. In this work, we derive a closed-form expression for the metric transformation of general chirp-like waveforms expressed in terms of the transverse-traceless GW metric, its integral, and its derivative. For more complex signals, where analytical integration is impractical, we provide an efficient approximation based on Taylor expansions of the retarded time to coalescence. Finally, we demonstrate how these results can be applied to calculate the signal response of a large class of detectors. Our approach provides essential tools for designing and interpreting high-frequency GW experiments that search for compact object mergers at MHz to GHz frequencies beyond the long-wavelength limit.

gr-qc↗

arXiv2025

Personalized and Demand-Based Education Concept: Practical Tools for Control Engineers

This paper presents a personalized lecture concept using educational blocks and its demonstrative application in a new university lecture. Higher education faces daily challenges: deep and specialized knowledge is available from everywhere and accessible to almost everyone. University lecturers of specialized master courses confront the problem that their lectures are either too boring or too complex for the attending students. Additionally, curricula are changing more rapidly than they have in the past 10-30 years. The German education system comprises different educational forms, with universities providing less practical content. Consequently, many university students do not obtain the practical skills they should ideally gain through university lectures. Therefore, in this work, a new lecture concept is proposed based on the extension of the just-in-time teaching paradigm: Personalized and Demand-Based Education. This concept includes: 1) an initial assessment of students' backgrounds, 2) selecting the appropriate educational blocks, and 3) collecting ongoing feedback during the semester. The feedback was gathered via Pingo, ensuring anonymity for the students. Our concept was exemplarily tested in the new lecture "Practical Tools for Control Engineers" at the Karlsruhe Institute of Technology. The initial results indicate that our proposed concept could be beneficial in addressing the current challenges in higher education.

eess.SY↗

arXiv2024

First characterisation of the MAGO cavity, a superconducting RF detector for kHz-MHz gravitational waves

Heterodyne detection using microwave cavities is a promising method for detecting high-frequency gravitational waves or ultralight axion dark matter. In this work, we report on studies conducted on a spherical 2-cell cavity developed by the MAGO collaboration for high-frequency gravitational waves detection. Although fabricated around 20 years ago, the cavity had not been used since. Due to deviations from the nominal geometry, we conducted a mechanical survey and performed room-temperature plastic tuning. Measurements and simulations of the mechanical resonances and electromagnetic properties were carried out, as these are critical for estimating the cavity's gravitational wave coupling potential. Based on these results, we plan further studies in a cryogenic environment. The cavity characterisation does not only provide valuable experience for a planned physics run but also informs the future development of improved cavity designs.

gr-qc↗

arXiv2024

Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learning

Mobile robots are being used on a large scale in various crowded situations and become part of our society. The socially acceptable navigation behavior of a mobile robot with individual human consideration is an essential requirement for scalable applications and human acceptance. Deep Reinforcement Learning (DRL) approaches are recently used to learn a robot's navigation policy and to model the complex interactions between robots and humans. We propose to divide existing DRL-based navigation approaches based on the robot's exhibited social behavior and distinguish between social collision avoidance with a lack of social behavior and socially aware approaches with explicit predefined social behavior. In addition, we propose a novel socially integrated navigation approach where the robot's social behavior is adaptive and emerges from the interaction with humans. The formulation of our approach is derived from a sociological definition, which states that social acting is oriented toward the acting of others. The DRL policy is trained in an environment where other agents interact socially integrated and reward the robot's behavior individually. The simulation results indicate that the proposed socially integrated navigation approach outperforms a socially aware approach in terms of ego navigation performance while significantly reducing the negative impact on all agents within the environment.

cs.RO↗

arXiv2020

Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence

Modern cyber-physical systems (CPS), such as our energy infrastructure, are becoming increasingly complex: An ever-higher share of Artificial Intelligence (AI)-based technologies use the Information and Communication Technology (ICT) facet of energy systems for operation optimization, cost efficiency, and to reach CO2 goals worldwide. At the same time, markets with increased flexibility and ever shorter trade horizons enable the multi-stakeholder situation that is emerging in this setting. These systems still form critical infrastructures that need to perform with highest reliability. However, today's CPS are becoming too complex to be analyzed in the traditional monolithic approach, where each domain, e.g., power grid and ICT as well as the energy market, are considered as separate entities while ignoring dependencies and side-effects. To achieve an overall analysis, we introduce the concept for an application of distributed artificial intelligence as a self-adaptive analysis tool that is able to analyze the dependencies between domains in CPS by attacking them. It eschews pre-configured domain knowledge, instead exploring the CPS domains for emergent risk situations and exploitable loopholes in codices, with a focus on rational market actors that exploit the system while still following the market rules.

cs.CY↗

arXiv2020

Robust and Deterministic Scheduling of Power Grid Actors

Modern power grids need to cope with increasingly decentralized, volatile energy sources as well as new business models such as virtual power plants constituted from battery swarms. This warrants both, day-ahead planning of larger schedules for power plants, as well as short-term contracting to counter forecast deviations or to accommodate dynamics of the intra-day markets. In addition, the geographic distribution of renewable energy sources forces scheduling algorithms with a hugely different communication link qualities. In this paper, we present an extension to the Lightweight Power Exchange Protocol (LPEP), dubbed LPEP++. It draws on the strength of the LPEP to find the optimal solution of the combinatorial power demand-supply problem with string guarantees in acceptable time and extends it with facilities for long-term planning, parallel negotiations and reduces its memory footprint. We furthermore show its robustness towards volatile communication link quality.

cs.MA↗

arXiv2019

Analyzing Cyber-Physical Systems from the Perspective of Artificial Intelligence

Principles of modern cyber-physical system (CPS) analysis are based on analytical methods that depend on whether safety or liveness requirements are considered. Complexity is abstracted through different techniques, ranging from stochastic modelling to contracts. However, both distributed heuristics and Artificial Intelligence (AI)-based approaches as well as the user perspective or unpredictable effects, such as accidents or the weather, introduce enough uncertainty to warrant reinforcement-learning-based approaches. This paper compares traditional approaches in the domain of CPS modelling and analysis with the AI researcher perspective to exploring unknown complex systems.

cs.DC↗

arXiv2018

Adversarial Resilience Learning - Towards Systemic Vulnerability Analysis for Large and Complex Systems

This paper introduces Adversarial Resilience Learning (ARL), a concept to model, train, and analyze artificial neural networks as representations of competitive agents in highly complex systems. In our examples, the agents normally take the roles of attackers or defenders that aim at worsening or improving-or keeping, respectively-defined performance indicators of the system. Our concept provides adaptive, repeatable, actor-based testing with a chance of detecting previously unknown attack vectors. We provide the constitutive nomenclature of ARL and, based on it, the description of experimental setups and results of a preliminary implementation of ARL in simulated power systems.

cs.AI↗