Tootfinder

Quantifying imaginarity of quantum operations
Chuanfa Wu, Zhaoqi Wu
https://arxiv.org/abs/2506.09747 https://arxiv.org/pdf/2506.09747…

Quantifying imaginarity of quantum operations
Complex numbers are theoretically proved and experimentally confirmed as necessary in quantum mechanics and quantum information, and a resource theory of imaginarity of quantum states has been established. In this work, we establish a framework to quantify the imaginarity of quantum operations from the perspective of the ability to create or detect imaginarity, following the idea by Theurer {\it et al.} [Phys. Rev. Lett. \textbf{122}, 190405 (2019)] used in coherence theory. We present two type…

Empirical Quantification of Spurious Correlations in Malware Detection
Bianca Perasso, Ludovico Lozza, Andrea Ponte, Luca Demetrio, Luca Oneto, Fabio Roli
https://arxiv.org/abs/2506.09662

Empirical Quantification of Spurious Correlations in Malware Detection
End-to-end deep learning exhibits unmatched performance for detecting malware, but such an achievement is reached by exploiting spurious correlations -- features with high relevance at inference time, but known to be useless through domain knowledge. While previous work highlighted that deep networks mainly focus on metadata, none investigated the phenomenon further, without quantifying their impact on the decision. In this work, we deepen our understanding of how spurious correlation affects d…

Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
Wei Chen, Giacomo Dimarco, Lorenzo Pareschi
https://arxiv.org/abs/2506.10636

Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
The high dimensionality of kinetic equations with stochastic parameters poses major computational challenges for uncertainty quantification (UQ). Traditional Monte Carlo (MC) sampling methods, while widely used, suffer from slow convergence and high variance, which become increasingly severe as the dimensionality of the parameter space grows. To accelerate MC sampling, we adopt a multiscale control variates strategy that leverages low-fidelity solutions from simplified kinetic models to reduce …

Quantifying coherence of quantum channels based on the generalized $\alpha$-$z$-relative R\'{e}nyi entropy
Jiaorui Fan, Zhaoqi Wu, Shao-Ming Fei
https://arxiv.org/abs/2506.10796

Quantifying coherence of quantum channels based on the generalized $α$-$z$-relative Rényi entropy
By using the Choi-Jamiołkowski isomorphism, we propose a well-defined coherence measure of quantum channels based on the generalized $α$-$z$-relative Rényi entropy. In addition, we present an alternative coherence measure of quantum channels by quantifying the commutativity between the channels and the completely dephasing channels with the generalized $α$-$z$-relative Rényi entropy. Some elegant properties of the measures are illustrated in detail. Explicit formulas of these coherence mea…

Quantifying Data Requirements for EEG Independent Component Analysis Using AMICA
Gwenevere Frank, Seyed Yahya Shirazi, Jason Palmer, Gert Cauwenberghs, Scott Makeig, Arnaud Delorme
https://arxiv.org/abs/2506.10156

Quantifying Data Requirements for EEG Independent Component Analysis Using AMICA
Independent Component Analysis (ICA) is an important step in EEG processing for a wide-ranging set of applications. However, ICA requires well-designed studies and data collection practices to yield optimal results. Past studies have focused on quantitative evaluation of the differences in quality produced by different ICA algorithms as well as different configurations of parameters for AMICA, a multimodal ICA algorithm that is considered the benchmark against which other algorithms are measure…

A Probabilistic Approach to Uncertainty Quantification Leveraging 3D Geometry
Rushil Desai, Frederik Warburg, Trevor Darrell, Marissa Ramirez de Chanlatte
https://arxiv.org/abs/2507.06269

A Probabilistic Approach to Uncertainty Quantification Leveraging 3D Geometry
Quantifying uncertainty in neural implicit 3D representations, particularly those utilizing Signed Distance Functions (SDFs), remains a substantial challenge due to computational inefficiencies, scalability issues, and geometric inconsistencies. Existing methods typically neglect direct geometric integration, leading to poorly calibrated uncertainty maps. We introduce BayesSDF, a novel probabilistic framework for uncertainty quantification in neural implicit SDF models, motivated by scientific …

Quantifying Azure RBAC Wildcard Overreach
Christophe Parisel
https://arxiv.org/abs/2506.10755 https://arxiv.org/pdf/2506.10755…

Quantifying Azure RBAC Wildcard Overreach
Azure RBAC leverages wildcard permissions to simplify policy authoring, but this abstraction often obscures the actual set of allowed operations and undermines least-privilege guarantees. We introduce Belshazaar, a two-stage framework that targets both the effective permission set problem and the evaluation of wildcards permissions spread. First, we formalize Azure action syntax via a context free grammar and implement a compiler that expands any wildcard into its explicit action set. Second, w…

Comparing probability distributions: application to quantum states of light
Soumyabrata Paul, V. Balakrishnan, S. Ramanan, S. Lakshmibala
https://arxiv.org/abs/2506.10760

Comparing probability distributions: application to quantum states of light
Probability distributions play a central role in quantum mechanics, and even more so in quantum optics with its rich diversity of theoretically conceivable and experimentally accessible quantum states of light. Quantifiers that compare two different states or density matrices in terms of `distances' between the respective probability distributions include the Kullback-Leibler divergence $D_{\rm KL}$, the Bhattacharyya distance $D_{\rm B}$, and the $p$-Wasserstein distance $W_{p}$. We present a …

Bipartite entanglement extracted from multimode squeezed light generated in lossy waveguides
Denis A. Kopylov, Torsten Meier, Polina R. Sharapova
https://arxiv.org/abs/2506.09587 …

Bipartite entanglement extracted from multimode squeezed light generated in lossy waveguides
Entangled two-mode Gaussian states constitute an important building block for continuous variable quantum computing and communication protocols and are thus of high demand for many experiments. In this work, we study such kind of states which are extracted from multimode light generated via type-II parametric down-conversion (PDC) in lossy waveguides. For such states, we demonstrate that the squeezing quantifies entanglement and we construct a measurement basis which results in the maximal bipa…

Certification of quantum networks using the generalised Choi isomorphism
Sophie Egelhaaf, Roope Uola
https://arxiv.org/abs/2506.10725 https://

Certification of quantum networks using the generalised Choi isomorphism
We present a framework for certifying entanglement properties of quantum states and measurements in line networks. The framework is based on the generalised Choi isomorphism, which can be used to map bipartite states and measurements into corresponding quantum operations. We apply the method to networks with trusted end points to demonstrate the power of the approach. We derive bounds for common convex geometric entanglement quantifiers of individual source states, as well as for the network as…