Xuan Leng | Searcharxiv

arXiv2025

Genuine multipartite entanglement verification with convolutional neural networks

In recent years, the detection of genuine multipartite entanglement (GME) via machine learning has received scant attention. Here, we employ convolutional neural networks (CNNs), as well as CNNs enhanced with squeeze-and-excitation (SE) to detect GME. We randomly generated GME states with 4 to 6 qubits and GHZ-diagonal states ranging from 4 to 20 qubits using the semidefinite programming approach. Subsequently, we assessed their classification accuracy. Our results demonstrate that the integration of the SE module significantly improved training performance. Additionally, we conducted an analysis of false positive and false negative occurrences. Utilizing our training data, we have substantially reduced the likelihood of incorrectly classifying non-entangled states as entangled.

quant-ph↗

arXiv2023

Debiased Inference for Dynamic Nonlinear Panels with Multi-dimensional Heterogeneities

We introduce a generic class of dynamic nonlinear heterogeneous parameter models that incorporate individual and time fixed effects in both the intercept and slope. These models are subject to the incidental parameter problem, in that the limiting distribution of the point estimator is not centered at zero, and that test statistics do not follow their standard asymptotic distributions as in the absence of the fixed effects. To address the problem, we develop an analytical bias correction procedure to construct a bias-corrected likelihood. The resulting estimator follows an asymptotic normal distribution with mean zero. Moreover, likelihood-based test statistics -- including likelihood-ratio, Lagrange-multiplier, and Wald tests -- follow the limiting chi-squared distribution under the null hypothesis. Simulations demonstrate the effectiveness of the proposed correction method, and an empirical application on the labor force participation of single mothers underscores its practical importance.

econ.EM↗