arXiv subjects

Andreas Trügler

Publications and source records attributed to Andreas Trügler.

14 recordsLinked to original sources

arXiv2026

When Routes Run Out: Adversarial Co-Learning and Explainable Robustness in Quantum Repeater Networks

We study an adversarial bandit problem for entanglement-based quantum-network routing over a modest graph corpus. Alice selects an end-to-end repeater route for an Ekert-91 protocol (E91) representing her move, while Eve selects an attack surface, either edge intercept--resend or repeater memory degradation. Payoffs are drawn from cached SeQUeNCe-simulated E91 transcripts, and Alice accepts a turn when the finite-sample statistic violates the Clauser-Horne-Shimony-Holt (CHSH) bound. Performing adversarial co-learning across 50 structured topologies, we find that learned retention tracks a full-matrix minimax reference closely (Pearson $r=0.99$): under a one-surface Eve action model, bottleneck families have zero retention, while non-bottleneck families follow a $1-1/N$ coverage principle. We then fit decision-tree explanation models to graph-, attack-, and route-level topology-corpus targets and report their faithfulness. Finally, we construct prompt records for local language models to summarize the tree evidence, resulting in an open-source explanation workflow for quantum-repeater network games.

quant-ph↗

arXiv2026

Hardware-Agnostic Modeling of Quantum Side-Channel Leakage via Conditional Dynamics and Learning from Full Correlation Data

We study a sequential coherent side-channel model in which an adversarial probe qubit interacts with a target qubit during a hidden gate sequence. Repeating the same hidden sequence for $N$ shots yields an empirical \emph{full-correlation record}: the joint histogram $\widehat{P}_g(b)$ over probe bit-strings $b\in\{0,1\}^k$, which is a sufficient statistic for classical post-processing under identically and independently distributed (i.i.d.)\ shots but grows exponentially with circuit depth. We first describe this sequential probe framework in a coupling- and measurement-agnostic form, emphasizing the scaling of the observation space and why exact analytic distinguishability becomes intractable with circuit depth. We then specialize to a representative instantiation (a controlled-rotation probe coupling with fixed projective readout and a commuting $R_x$ gate alphabet) where we (i) derive a depth-dependent leakage envelope whose maximizer predicts a coupling band as a function of depth {if the measurement data is reduced to marginal statistics}, and (ii) provide an operational decoder, via machine learning, a single parameter-conditioned map from $\widehat{P}_g$ to Alice's per-step gate labels, generalizing across coupling and noise settings without retraining.

quant-ph↗

arXiv2025

Private and Fair Machine Learning: Revisiting the Disparate Impact of Differentially Private SGD

Differential privacy (DP) is a prominent method for protecting information about individuals during data analysis. Training neural networks with differentially private stochastic gradient descent (DPSGD) influences the model's learning dynamics and, consequently, its output. This can affect the model's performance and fairness. While the majority of studies on the topic report a negative impact on fairness, it has recently been suggested that fairness levels comparable to non-private models can be achieved by optimizing hyperparameters for performance directly on differentially private models (rather than re-using hyperparameters from non-private models, as is common practice). In this work, we analyze the generalizability of this claim by 1) comparing the disparate impact of DPSGD on different performance metrics, and 2) analyzing it over a wide range of hyperparameter settings. We highlight that a disparate impact on one metric does not necessarily imply a disparate impact on another. Most importantly, we show that while optimizing hyperparameters directly on differentially private models does not mitigate the disparate impact of DPSGD reliably, it can still lead to improved utility-fairness trade-offs compared to re-using hyperparameters from non-private models. We stress, however, that any form of hyperparameter tuning entails additional privacy leakage, calling for careful considerations of how to balance privacy, utility and fairness. Finally, we extend our analyses to DPSGD-Global-Adapt, a variant of DPSGD designed to mitigate the disparate impact on accuracy, and conclude that this alternative may not be a robust solution with respect to hyperparameter choice.

cs.LG↗

arXiv2023

Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey

Differential Privacy has become a widely popular method for data protection in machine learning, especially since it allows formulating strict mathematical privacy guarantees. This survey provides an overview of the state-of-the-art of differentially private centralized deep learning, thorough analyses of recent advances and open problems, as well as a discussion of potential future developments in the field. Based on a systematic literature review, the following topics are addressed: auditing and evaluation methods for private models, improvements of privacy-utility trade-offs, protection against a broad range of threats and attacks, differentially private generative models, and emerging application domains.

cs.LG↗

arXiv2023

Testing robustness of predictions of trained classifiers against naturally occurring perturbations

Correctly quantifying the robustness of machine learning models is a central aspect in judging their suitability for specific tasks, and ultimately, for generating trust in them. We address the problem of finding the robustness of individual predictions. We show both theoretically and with empirical examples that a method based on counterfactuals that was previously proposed for this is insufficient, as it is not a valid metric for determining the robustness against perturbations that occur ``naturally'', outside specific adversarial attack scenarios. We propose a flexible approach that models possible perturbations in input data individually for each application. This is then combined with a probabilistic approach that computes the likelihood that a ``real-world'' perturbation will change a prediction, thus giving quantitative information of the robustness of individual predictions of the trained machine learning model. The method does not require access to the internals of the classifier and thus in principle works for any black-box model. It is, however, based on Monte-Carlo sampling and thus only suited for input spaces with small dimensions. We illustrate our approach on the Iris and the Ionosphere datasets, on an application predicting fog at an airport, and on analytically solvable cases.

cs.LG↗

arXiv2023

A conceptual model for leaving the data-centric approach in machine learning

For a long time, machine learning (ML) has been seen as the abstract problem of learning relationships from data independent of the surrounding settings. This has recently been challenged, and methods have been proposed to include external constraints in the machine learning models. These methods usually come from application-specific fields, such as de-biasing algorithms in the field of fairness in ML or physical constraints in the fields of physics and engineering. In this paper, we present and discuss a conceptual high-level model that unifies these approaches in a common language. We hope that this will enable and foster exchange between the different fields and their different methods for including external constraints into ML models, and thus leaving purely data-centric approaches.

cs.LG↗

arXiv2023

Modelling the long-term fairness dynamics of data-driven targeted help on job seekers

The use of data-driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This raises ethical concerns, as it has adversely affected minorities and historically discriminated groups. In this paper, we use an approach that combines statistics and data-driven approaches with dynamical modeling to assess long-term fairness effects of labor market interventions. Specifically, we develop and use a model to investigate the impact of decisions caused by a public employment authority that selectively supports job-seekers through targeted help. The selection of who receives what help is based on a data-driven intervention model that estimates an individual's chances of finding a job in a timely manner and rests upon data that describes a population in which skills relevant to the labor market are unevenly distributed between two groups (e.g., males and females). The intervention model has incomplete access to the individual's actual skills and can augment this with knowledge of the individual's group affiliation, thus using a protected attribute to increase predictive accuracy. We assess this intervention model's dynamics -- especially fairness-related issues and trade-offs between different fairness goals -- over time and compare it to an intervention model that does not use group affiliation as a predictive feature. We conclude that in order to quantify the trade-off correctly and to assess the long-term fairness effects of such a system in the real-world, careful modeling of the surrounding labor market is indispensable.

cs.LG↗

arXiv2022

CryptoTL: Private, Efficient and Secure Transfer Learning

Big data has been a pervasive catchphrase in recent years, but dealing with data scarcity has become a crucial question for many real-world deep learning (DL) applications. A popular methodology to efficiently enable the training of DL models to perform tasks in scenarios with low availability of data is transfer learning (TL). TL allows to transfer knowledge from a general domain to a specific target one. However, such a knowledge transfer may put privacy at risk when it comes to sensitive or private data. With CryptoTL we introduce a solution to this problem, and show for the first time a cryptographic privacy-preserving TL approach based on homomorphic encryption that is efficient and feasible for real-world use cases. We achieve this by carefully designing the framework such that training is always done in plain while still profiting from the privacy gained by homomorphic encryption. To demonstrate the efficiency of our framework, we instantiate it with the popular CKKS HE scheme and apply CryptoTL to classification tasks with small datasets and show the applicability of our approach for sentiment analysis and spam detection. Additionally, we highlight how our approach can be combined with differential privacy to further increase the security guarantees. Our extensive benchmarks show that using CryptoTL leads to high accuracy while still having practical fine-tuning and classification runtimes despite using homomorphic encryption. Concretely, one forward-pass through the encrypted layers of our setup takes roughly 1s on a notebook CPU.

cs.CR↗

arXiv2016

Gap plasmonics of silver nanocube dimers

We theoretically investigate gap plasmons for two silver nanocubes coupled through a molecular tunnel junction. In absence of tunneling, the red-shift of the bonding mode saturates with decreasing gap distance. Tunneling at small gap distances leads to a damping and slight blue-shift of the bonding mode, but no low-energy charge transfer plasmon mode appears in the spectra. This finding is in stark contrast to recent work of Tan et al. [Science 343, 1496 (2014)].

cond-mat.mes-hall↗

arXiv2015

Full three-dimensonal reconstruction of the dyadic Green tensor from electron energy loss spectroscopy of plasmonic nanoparticles

Electron energy loss spectroscopy (EELS) has emerged as a powerful tool for the investigation of plasmonic nanoparticles, but the interpretation of EELS results in in terms of optical quantities, such as the photonic local density of states, remains challenging. Recent work has demonstrated that under restrictive assumptions, including the applicability of the quasistatic approximation and a plasmonic response governed by a single mode, one can rephrase EELS as a tomography scheme for the reconstruction of plasmonic eigenmodes. In this paper we lift these restrictions by formulating EELS as an inverse problem, and show that the complete dyadic Green tensor can be reconstructed for plasmonic particles of arbitrary shape. The key steps underlying our approach are a generic singular value decomposition of the dyadic Green tensor and a compressed sensing optimization for the determination of the expansion coefficients. We demonstrate the applicability of our scheme for prototypical nanorod, bowtie, and cube geometries.

cond-mat.mes-hall↗

arXiv2014

Plasmonics simulations with the MNPBEM toolbox: Consideration of substrates and layer structures

Within the MNPBEM toolbox, developed for the simulation of plasmonic nanoparticles using a boundary element method approach, we show how to include substrate and layer structure effects. We develop the methodology for solving Maxwell's equations using scalar and vector potentials within the inhomogeneous dielectric environment of a layer structure. We show that the implementation of our approach allows for fast and efficient simulations of plasmonic nanoparticles situated on top of substrates or embedded in layer structures. The new toolbox provides a number of demo files which can also be used as templates for other simulations.

cond-mat.mes-hall↗

arXiv2013

Surface plasmons in doped topological insulators

We investigate surface plasmons at a planar interface between a normal dielectric and a topological insulator, where the Fermi-energy lies inside the bulk gap of the topological insulator and gives rise to a two-dimensional charge distribution of free Dirac electrons. We develop the methodology for the calculation of plasmon dispersions, using the framework of classical electrodynamics, with modified constituent equations due to Hall currents in the topological insulator, together with a Lindhard-type description for the two-dimensional charge distribution of free Dirac electrons. For a system representative for Bi$_2$X$_3$ binary compounds, we find in agreement with recent related work that the modified constituent equations have practically no impact on the surface plasmon dispersion but lead to a rotation of the magnetic polarization of surface plasmons out of the interface plane.

cond-mat.mes-hall↗

arXiv2013

Tomography of particle plasmon fields from electron energy loss spectroscopy

We theoretically investigate electron energy loss spectroscopy (EELS) of metallic nanoparticles in the optical frequency domain. Using a quasistatic approximation scheme together with a plasmon eigenmode expansion, we show that EELS can be rephrased in terms of a tomography problem. For selected single and coupled nanoparticles we extract the three-dimensional plasmon fields from a collection of rotated EELS maps. Our results pave the way for a fully three-dimensional plasmon-field tomography and establish EELS as a quantitative measurement device for plasmonics.

cond-mat.mes-hall↗

arXiv2011

MNPBEM - A Matlab toolbox for the simulation of plasmonic nanoparticles

MNPBEM is a Matlab toolbox for the simulation of metallic nanoparticles (MNP), using a boundary element method (BEM) approach. The main purpose of the toolbox is to solve Maxwell's equations for a dielectric environment where bodies with homogeneous and isotropic dielectric functions are separated by abrupt interfaces. Although the approach is in principle suited for arbitrary body sizes and photon energies, it is tested (and probably works best) for metallic nanoparticles with sizes ranging from a few to a few hundreds of nanometers, and for frequencies in the optical and near-infrared regime. The toolbox has been implemented with Matlab classes. These classes can be easily combined, which has the advantage that one can adapt the simulation programs flexibly for various applications.

cond-mat.mes-hall↗