arXiv subjects

Huixian Meng

Publications and source records attributed to Huixian Meng.

3 recordsLinked to original sources

arXiv2026

Correlation-Enhanced Quantum Reservoir Processing for Two-Qutrit Steering: From Generalization to Discovery

Quantum reservoir processing (QRP) has been applied to entanglement quantification, yet its potential for generalizable steering detection and the discovery of high-dimensional quantum correlations remains largely unexplored. Here, we introduce correlation-enhanced QRP (CEQRP), extending QRP to two-qutrit systems with two-body spin-correlation readouts. Some CEQRP-based classifiers substantially improve cross-family generalization, with logistic regression trained on full-information CEQRP features achieving approximately $96\%$ accuracy on unseen Werner states and over $97\%$ accuracy on unseen isotropic states at both $m=4$ and $m=5$. Independently, we establish two interconnected steering criteria for mixed two-qutrit states: a necessary-and-sufficient all-versus-nothing criterion for states admitting three linearly independent pure conditional states, and an asymptotic sufficient criterion, with the two criteria linked through invertible local filtering. These criteria provide rigorously certified steerable samples for training-set augmentation. Combining CEQRP-based classification with these theory-certified samples enables the discovery of new Alice-to-Bob steerable partially entangled states, including states at $p=0.45$ whose steerability is independently confirmed by semidefinite programming (SDP) using seven projective measurement settings. The trained classifiers further guide finite-setting searches for steering boundaries. Our results establish a pathway from generalizable classification to the discovery of high-dimensional quantum steering.

quant-ph↗

arXiv2026

Robust Steerability Classification via Key Feature Extraction and Matrix Structure Preservation

Generalization ability is essential for assessing the robustness of quantum steerability classifiers. In this work, we investigate robust steerability classification from the perspective of key feature extraction and matrix structure preservation. The dataset introduced in Phys. Rev. A 100, 022314 (2019) provides the training basis for the present work. With strictly unsteerable random states, T-diagonal states, and All-Versus-Nothing (AVN) states, we evaluate the generalization performance of support vector machines (SVMs), multilayer perceptrons (MLPs), and deep perceptron control classifiers(DPs) trained on full-information features. None of these classifiers perform consistently on T-diagonal or AVN states. Given that stochastic local operations and classical communication and local unitary transformations preserve steerability, we introduce a key feature that determines steerability. SVMs trained on this feature overcome the instability on T-diagonal states but still fail on AVN states. Moreover, this feature alone is insufficient for training robust neural-network-based steerability classifiers. Recognizing that flattening quantum states into one-dimensional vectors may destroy their intrinsic matrix structure, we introduce matrix versions of both features and train convolutional neural networks on them. The most robust overall performance among the tested classifiers is achieved only when the matrix structure is preserved and key features are extracted simultaneously. Finally, as an application, we employ the most robust classifiers to predict the number of projective measurements required to detect the steerability of axially symmetric states

quant-ph↗

arXiv2025

Machine Learning for Detecting Steering in Qutrit-Pair States

Only a few states in high-dimensional systems can be identified as (un)steerable using existing theoretical or experimental methods. We utilize semidefinite programming (SDP) to construct a dataset for steerability detection in qutrit-qutrit systems. For the full-information feature $F_1$, artificial neural networks achieve high classification accuracy and generalization, and preform better than the support vector machine. As feature engineering playing a pivotal role, we introduce a steering ellipsoid-like feature $F_2$, which significantly enhances the performance of each of our models. Given the SDP method provides only a sufficient condition for steerability detection, we establish the first rigorously constructed, accurately labeled dataset based on theoretical foundations. This dataset enables models to exhibit outstanding accuracy and generalization capabilities, independent of the choice of features. As applications, we investigate the steerability boundaries of isotropic states and partially entangled states, and find new steerable states. This work not only advances the application of machine learning for probing quantum steerability in high-dimensional systems but also deepens the theoretical understanding of quantum steerability itself.

quant-ph↗