arXiv subjects

Dongmei Liu

Publications and source records attributed to Dongmei Liu.

11 recordsLinked to original sources

arXiv2026

Quantum Sensing Beyond Exceptional Points via Hidden symmetry-protected vacuum-noise fixed point

Exceptional-point (EP) sensing has attracted considerable interest because of its anomalous response scaling. However, recent studies have shown that the enhanced response near an EP is inevitably accompanied by amplified quantum noise, fundamentally limiting the achievable signalto-noise ratio (SNR). Here, we propose a fundamentally different route toward non-Hermitian quantum sensing based on symmetry-protected noise suppression rather than response amplification. We develop a fully quantum continuous-variable model that unifies parity-time (PT) and anti-paritytime(APT) symmetries within a single framework. Exploiting the incompatibility between these two symmetries, we uncover a non-Hermitian Dirac eigenspectrum and reveal a hidden symmetryprotected phase transition embedded in the Hamiltonian spectrum. Remarkably, this hidden phasetransition simultaneously constitutes a symmetry-protected vacuum-noise fixed point, where collective three-mode quadratures exhibit suppressed quantum fluctuations despite the absence of any anomalous spectral response. As a consequence, quantum sensing is enhanced through the suppression of excess quantum noise while maintaining a finite response sensitivity, establishing a sensing mechanism fundamentally different from conventional EP-based approaches. These results reveal an unexpected connection between hidden symmetry, quantum fluctuations, and non-Hermitian quantum metrology, and establish noise suppression as an alternative paradigm for non-Hermitian quantum sensing.

quant-ph↗

arXiv2026

Adaptive Enhanced Quantum-inspired Simulated Bifurcation Algorithm for Population State Perception

Existing quantum-inspired simulated bifurcation algorithms rely on dynamic scheduling methods but lack the ability to adapt effectively to different problem instances. Additionally, during the evolutionary stage, balancing exploration and exploitation remains challenging. The fundamental issue stems from the widespread use of static preset parameters and globally uniform strategies, which can diminish algorithm effectiveness and lead to result homogenization. This article proposes an Adaptive Enhanced Quantum-inspired Simulated Bifurcation (AE-QSB) framework driven by population states. By leveraging perception indicators of four distinct population states, the QSB algorithm establishes a closed-loop strategy encompassing perception, decision-making, and execution. Within this framework, we introduce three complementary algorithms spanning a spectrum from efficient extremum seeking (ME-BSB), through population-level uniform refinement (SE-DSB), to density-aware adaptive scheduling (SG-DSB). On the medium-sized graph G22, both SE-DSB and SG-DSB achieve a mean gap below 0.05\%, while ME-BSB attains the optimal trade-off between runtime and solution quality with a gap of 0.26\% and the shortest single-run time. We compared AE-QSB variants with other algorithms across all benchmark graphs from G1 to G81. The results demonstrate that AE-QSB achieved the lowest mean gap on 74.6\% of the graphs and the highest average approximation rate on 84.5\% of the graphs. Ablation experiments further revealed that subgroup exploration and rescue mechanisms play crucial roles in both multifactor and single-factor components. This study demonstrates that population statistical information during dynamic evolution provides a computable and effective foundation for adaptive control, enabling quantum-inspired optimization methods to transition from fixed scheduling to data-driven closed-loop control.

cs.NE↗

arXiv2025

Efficient hybrid variational quantum algorithm for solving graph coloring problem

In the era of Noisy Intermediate Scale Quantum (NISQ) computing, available quantum resources are limited. Many NP-hard problems can be efficiently addressed using hybrid classical and quantum computational methods. This paper proposes a hybrid variational quantum algorithm designed to solve the $k$-coloring problem of graph vertices. The hybrid classical and quantum algorithms primarily partition the graph into multiple subgraphs through hierarchical techniques. The Quantum Approximate Optimization Algorithm (QAOA) is employed to determine the coloring within the subgraphs, while a classical greedy algorithm is utilized to find the coloring of the interaction graph. Fixed coloring is applied to the interaction graph, and feedback is provided to correct any conflicting colorings within the subgraphs. The merging process into the original graph is iteratively optimized to resolve any arising conflicts. We employ a hierarchical framework that integrates feedback correction and conflict resolution to achieve $k$-coloring of arbitrary graph vertices. Through experimental analysis, we demonstrate the effectiveness of the algorithm, highlighting the rapid convergence of conflict evolution and the fact that iterative optimization allows the classical algorithm to approximate the number of colorings. Finally, we apply the proposed algorithm to optimize the scheduling of a subway transportation network, demonstrating a high degree of fairness.

quant-ph↗

arXiv2024

Squeezing and Entanglement Dynamics in Phase-Sensitive Non-Hermitian Systems

Over the past decade, parity-time (PT) symmetry and anti-PT (APT) symmetry in various physical systems have been extensively studied, leading to significant experimental and theoretical advancements. However, physical systems that simultaneously exhibit both PT and APT symmetry have not yet been explored. In this study, we construct a phase-sensitive non-Hermitian wave mixing model that inherently possesses single-mode APT symmetry. By tuning the phase of the pump field, this model simultaneously exhibits two-mode quadrature-PT symmetry. Moreover, the APT phase transition is accompanied by the emergence of quantum entanglement from absence to presence. This remarkable quantum effect, quantum entanglement, which distinguishes itself from classical physics, is surprisingly linked to APT symmetry phase transition. Our work further explores the relationship between two-mode quantum entanglement and the phase of the pump field, offering deeper insight into the generation and evolution of entanglement in the corresponding nonlinear system. This provides a new perspective on quantum information processing.

quant-ph↗

arXiv2024

Quadrature-PT symmetry: Classical-to-quantum transition in noise fluctuations

While gain-loss-coupled photonic platforms have achieved significant success in studying classical parity-time (PT) symmetry, they encounter challenges in demonstrating pure quantum effects due to incompatible operator transformations and Langevin noise. Here, we present compelling evidence that a non-Hermitian (NH) twin-beam system, undergoing phase-sensitive amplification (PSA) and balanced loss, not only enables observing the usual eigenvalue-associated PT phase transition but also exhibits distinctive features absent in classical NH or Hermitian quantum scenarios, encompassing quadrature PT symmetry, anomalous loss-induced quadrature squeezing, and dynamical and stationary classical-to-quantum transitions in noise fluctuations. Furthermore, our proposed bipartite open system promises optimal sensing, showcasing an improved signal-to-noise ratio and sensitivity, constrained by quantum Cramér-Rao bound or Fisher information. These findings deepen the comprehension of authentic quantum optical PT symmetry involving both gain and loss, addressing contentious issues and illuminating new facets of the subject.

quant-ph↗

arXiv2024

Dual opposing quadrature-PT symmetry

Our recent research on type-I quadrature parity-time (PT) symmetry, utilizing an open twin-beam system, not only enables observing genuine quantum photonic PT symmetry amid phase-sensitive amplification (PSA) and loss in the presence of Langevin noise but also reveals additional classical-to-quantum (C2Q) transitions in quadrature and relative-intensity noise fluctuations. In contrast to the previous setup, our exploration of an alternative system assuming no loss involves a type-II PSA-only scheme. This scheme facilitates dual opposing quadrature PT symmetry, offering a comprehensive and complementary comprehension of C2Q transitions and anti-Hermiticity-enhanced quantum sensing. Furthermore, our investigation into the correlation with the Einstein-Podolsky-Rosen criteria uncovers previously unexplored connections between PT symmetry and nonclassicality, as well as quantum entanglement within the continuous-variable framework.

quant-ph↗

arXiv2019

Morphy: A Datamorphic Software Test Automation Tool

This paper presents an automated tool called Morphy for datamorphic testing. It classifies software test artefacts into test entities and test morphisms, which are mappings on testing entities. In addition to datamorphisms, metamorphisms and seed test case makers, Morphy also employs a set of other test morphisms including test case metrics and filters, test set metrics and filters, test result analysers and test executers to realise test automation. In particular, basic testing activities can be automated by invoking test morphisms. Test strategies can be realised as complex combinations of test morphisms. Test processes can be automated by recording, editing and playing test scripts that invoke test morphisms and strategies. Three types of test strategies have been implemented in Morphy: datamorphism combination strategies, cluster border exploration strategies and strategies for test set optimisation via genetic algorithms. This paper focuses on the datamorphism combination strategies by giving their definitions and implementation algorithms. The paper also illustrates their uses for testing both traditional software and AI applications with three case studies.

cs.SE↗

arXiv2019

Datamorphic Testing: A Methodology for Testing AI Applications

With the rapid growth of the applications of machine learning (ML) and other artificial intelligence (AI) techniques, adequate testing has become a necessity to ensure their quality. This paper identifies the characteristics of AI applications that distinguish them from traditional software, and analyses the main difficulties in applying existing testing methods. Based on this analysis, we propose a new method called datamorphic testing and illustrate the method with an example of testing face recognition applications. We also report an experiment with four real industrial application systems of face recognition to validate the proposed approach.

cs.SE↗

arXiv2017

Structured condition numbers and small sample condition estimation of symmetric algebraic Riccati equations

This paper is devoted to a structured perturbation analysis of the symmetric algebraic Riccati equations by exploiting the symmetry structure. Based on the analysis, the upper bounds for the structured normwise, mixed and componentwise condition numbers are derived. Due to the exploitation of the symmetry structure, our results are improvements of the previous work on the perturbation analysis and condition numbers of the symmetric algebraic Riccati equations. Our preliminary numerical experiments demonstrate that our condition numbers provide accurate estimates for the change in the solution caused by the perturbations on the data. Moreover, by applying the small sample condition estimation method, we propose a statistical algorithm for practically estimating the condition numbers of the symmetric algebraic Riccati equations.

math.NA↗

arXiv2016

Nondestructive on-chip detection of optical orbital angular momentum through a single plasmonic nanohole

Optical orbital angular momentum (OAM) provides an additional dimension for photons to carry information in high-capacity optical communication. Although the practical needs have intrigued the generations of miniaturized devices to manipulate the OAM modes in various integrated platforms, the on-chip OAM detection is still challenging to match the newly-developed compact OAM emitter and OAM transmission fiber. Here, we demonstrate an ultra-compact device, i.e., a single plasmonic nanohole, to efficiently measure an optical beam's OAM state in a nondestructive way. The device size is reduced down to a few hundreds of nanometers, which can be easily fabricated and installed in the current OAM devices. It is a flexible and robust way for in-situ OAM monitoring and detection in optical fiber networks and long-distance optical communication systems. With proper optimization of the nanohole parameters, this approach could be further extended to discriminate the OAM information multiplexed in multiple wavelengths and polarizations.

physics.optics↗

arXiv2013

Diffraction Interference Induced Superfocusing in Nonlinear Talbot Effect

We report a simple, novel sub-diffraction method, i.e. diffraction interference induced super-focusing in second-harmonic (SH) Talbot effect, to achieve focusing size of less than λ_pump/8 without involving evanescent waves or sub-wavelength apertures. By tailoring point spread functions with Fresnel diffraction interference, we observe periodic SH sub-diffracted spots over a hundred of micrometers away from the sample. Our demonstration is the first experimental realization of the proposal by Toraldo Di Francia pioneered 60 years ago for super-resolution imaging.

physics.optics↗