Tootfinder

Quantifying mixed-state entanglement via partial transpose and realignment moments
Poetri Sonya Tarabunga, Tobias Haug
https://arxiv.org/abs/2507.13840 htt…

Quantifying mixed-state entanglement via partial transpose and realignment moments
Entanglement plays a crucial role in quantum information science and many-body physics, yet quantifying it in mixed quantum many-body systems has remained a notoriously difficult problem. Here, we introduce families of quantitative entanglement witnesses, constructed from partial transpose and realignment moments, which provide rigorous bounds on entanglement monotones. Our witnesses can be efficiently measured using SWAP tests or variants of Bell measurements, thus making them directly impleme…

Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
G\'abor Lugosi, Marcos Matabuena
https://arxiv.org/abs/2507.15741 h…

Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
This paper introduces a framework for uncertainty quantification in regression models defined in metric spaces. Leveraging a newly defined notion of homoscedasticity, we develop a conformal prediction algorithm that offers finite-sample coverage guarantees and fast convergence rates of the oracle estimator. In heteroscedastic settings, we forgo these non-asymptotic guarantees to gain statistical efficiency, proposing a local $k$--nearest--neighbor method without conformal calibration that is ad…

https://fanf2.user.srcf.net/hermes/doc/qsmtp/draft-fanf-wtf8.html
Encoding a character to WTF-8 proceeds as follows:
* Determine the number of octets required from the character number and the first column of the table above. It is important to note that th…

Adaptive Parameter Optimization in Gaussian Processes: A Comprehensive Study of Uncertainty Quantification and Dimensional Scaling
Nishant Gadde
https://arxiv.org/abs/2507.15138

Adaptive Parameter Optimization in Gaussian Processes: A Comprehensive Study of Uncertainty Quantification and Dimensional Scaling
Gaussian Process (GP) models have also become extremely useful for optimization under uncertainty algorithms, especially where the objective functions are costly to compute. Yet, the more classical methods usually adopt strategies that, in certain circumstances, might be effective but not flexible to be applied to a wide range of problem terrains. This study aims to adapt parameter optimization in GP models and especially how uncertainty quantification can assist in the learning process. We inv…

Quantifying How Much Has Been Learned from a Research Study
Jonas M. Mikhaeil, Donald P. Green
https://arxiv.org/abs/2508.14789 https://arxiv.org/pdf/2508.…

Quantifying How Much Has Been Learned from a Research Study
How much does a research study contribute to a scientific literature? We propose a learning metric to quantify how much a research community learns from a given study. To do so, we adopt a Bayesian perspective and assess changes in the community's beliefs once updated with a new study's evidence. We recommend the Wasserstein-2 distance as a way to describe how the research community's prior beliefs change to incorporate a study's findings. We illustrate this approach through stylized examples a…

LoUQAL: Low-fidelity informed Uncertainty Quantification for Active Learning in the chemical configuration space
Vivin Vinod, Peter Zaspel
https://arxiv.org/abs/2508.15577 https…

LoUQAL: Low-fidelity informed Uncertainty Quantification for Active Learning in the chemical configuration space
Uncertainty quantification is an important scheme in active learning techniques, including applications in predicting quantum chemical properties. In quantum chemical calculations, there exists the notion of a fidelity, a less accurate computation is accessible at a cheaper computational cost. This work proposes a novel low-fidelity informed uncertainty quantification for active learning with applications in predicting diverse quantum chemical properties such as excitation energies and \textit{…

Identity and Quantify Various Dissipation Mechanisms of Josephson Junction in Superconducting Circuits
Hao Deng, Huijuan Zhan, Lijuan Hu, Hui-Hai Zhao, Ran Gao, Kannan Lu, Xizheng Ma, Zhijun Song, Fei Wang, Tenghui Wang, Feng Wu, Tian Xia, Gengyan Zhang, Xiaohang Zhang, Chunqing Deng
https://arxiv.org/abs/2508.15174

Identity and Quantify Various Dissipation Mechanisms of Josephson Junction in Superconducting Circuits
Pinpointing the dissipation mechanisms and evaluating their impacts to the performance of Josephson junction (JJ) are crucial for its application in superconducting circuits. In this work, we demonstrate the junction-embedded resonator (JER) as a platform which enables us to identify and quantify various dissipation mechanisms of JJ. JER is constructed by embedding JJ in the middle of an open-circuit, 1/2 λ transmission-line resonator. When the 1st and 2nd harmonics of JER are excited, JJ expe…

Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning
Yijin Ni, Xiaoming Huo
https://arxiv.org/abs/2508.15084 https://

Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning
This paper introduces a novel kernel-based formulation of the Equalized Odds (EO) criterion, denoted as $EO_k$, for fair representation learning (FRL) in supervised settings. The central goal of FRL is to mitigate discrimination regarding a sensitive attribute $S$ while preserving prediction accuracy for the target variable $Y$. Our proposed criterion enables a rigorous and interpretable quantification of three core fairness objectives: independence (prediction $\hat{Y}$ is independent of $S$),…

Conformal Contraction for Robust Nonlinear Control with Distribution-Free Uncertainty Quantification
Sihang Wei, Melkior Ornik, Hiroyasu Tsukamoto
https://arxiv.org/abs/2507.13613

Conformal Contraction for Robust Nonlinear Control with Distribution-Free Uncertainty Quantification
We present a novel robust control framework for continuous-time, perturbed nonlinear dynamical systems with uncertainty that depends nonlinearly on both the state and control inputs. Unlike conventional approaches that impose structural assumptions on the uncertainty, our framework enhances contraction-based robust control with data-driven uncertainty prediction, remaining agnostic to the models of the uncertainty and predictor. We statistically quantify how reliably the contraction conditions …