arXiv subjects

Liping Guo

Publications and source records attributed to Liping Guo.

13 recordsLinked to original sources

arXiv2026

Antibunching Enhancement via Non-Markovianity in a Hybrid Optical-Microwave Cross-Cavity

Unconventional photon blockade (UPB) provides an attractive route for generating antibunched light through quantum interference without requiring strong nonlinear coupling. In conventional Markovian systems, irreversible dissipation progressively destroys the phase coherence, even limiting the achievable single-photon purity. Here we propose a scheme for imposing non-Markovian effects on a microwave cavity where a Er$^{3+}$:Y$_2$SiO$_5$ crystal loaded into a hybrid optical-microwave system. Based on the time-convolutionless non-Markovian framework, we derive the time-dependent non-Markovian decay rate and the renormalized microwave drive in the weak-coupling regime. We show that the second-order correlation function is significantly suppressed, even to the order of $10^{-7}$, since the backflow of environmental information enhances photon blocking. In addition, this structured reservoir engineering not only provides an additional degree of freedom, but also further optimizes the blockade via optical detuning. Importantly, the delayed second-order correlation exhibits a broadened antibunching profile, thereby relaxing the timing requirements for single-photon synchronization. Our results demonstrate that environmental non-Markovianity can be transformed from a source of decoherence into a controllable quantum resource, opening new opportunities for high-purity single-photon generation.

quant-ph↗

arXiv2025

Distributed Fusion Estimation with Protecting Exogenous Inputs

In the context of distributed fusion estimation, directly transmitting local estimates to the fusion center may cause a privacy leakage concerning exogenous inputs. Thus, it is crucial to protect exogenous inputs against full eavesdropping while achieving distributed fusion estimation. To address this issue, a noise injection strategy is provided by injecting mutually independent noises into the local estimates transmitted to the fusion center. To determine the covariance matrices of the injected noises, a constrained minimization problem is constructed by minimizing the sum of mean square errors of the local estimates while ensuring ({\epsilon}, {\delta})-differential privacy. Suffering from the non-convexity of the minimization problem, an approach of relaxation is proposed, which efficiently solves the minimization problem without sacrificing differential privacy level. Then, a differentially private distributed fusion estimation algorithm based on the covariance intersection approach is developed. Further, by introducing a feedback mechanism, the fusion estimation accuracy is enhanced on the premise of the same ({\epsilon}, {\delta})-differential privacy. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed algorithms, and the trade-off between differential privacy level and fusion estimation accuracy.

eess.SY↗

arXiv2025

Decoding Polyphenol-Protein Interactions with Deep Learning: From Molecular Mechanisms to Food Applications

Polyphenols and proteins are essential biomolecules that influence food functionality and, by extension, human health. Their interactions -- hereafter referred to as PhPIs (polyphenol-protein interactions) -- affect key processes such as nutrient bioavailability, antioxidant activity, and therapeutic efficacy. However, these interactions remain challenging due to the structural diversity of polyphenols and the dynamic nature of protein binding. Traditional experimental techniques like nuclear magnetic resonance (NMR) and mass spectrometry (MS), along with computational tools such as molecular docking and molecular dynamics (MD), have offered important insights but face constraints in scalability, throughput, and reproducibility. This review explores how deep learning (DL) is reshaping the study of PhPIs by enabling efficient prediction of binding sites, interaction affinities, and MD using high-dimensional bio- and chem-informatics data. While DL enhances prediction accuracy and reduces experimental redundancy, its effectiveness remains limited by data availability, quality, and representativeness, particularly in the context of natural products. We critically assess current DL frameworks for PhPIs analysis and outline future directions, including multimodal data integration, improved model generalizability, and development of domain-specific benchmark datasets. This synthesis offers guidance for researchers aiming to apply DL in unraveling structure-function relationships of polyphenols, accelerating discovery in nutritional science and therapeutic development.

q-bio.BM↗

arXiv2024

State Estimation with Protecting Exogenous Inputs via Cram\'er-Rao Lower Bound Approach

This paper addresses the real-time state estimation problem for dynamic systems while protecting exogenous inputs against adversaries, who may be honest-but-curious third parties or external eavesdroppers. The Cram\'er-Rao lower bound (CRLB) is employed to constrain the mean square error (MSE) of the adversary's estimate for the exogenous inputs above a specified threshold. By minimizing the MSE of the state estimate while ensuring a certain privacy level measured by CRLB, the problem is formulated as a constrained optimization. To solve the optimization problem, an explicit expression for CRLB is first provided. As the computational complexity of the CRLB increases with the time step, a low-complexity approach is proposed to make the complexity independent of time. Then, a relaxation approach is proposed to efficiently solve the optimization problem. Finally, a privacy-preserving state estimation algorithm with low complexity is developed, which also ensures $(\epsilon, \delta)$-differential privacy. Two illustrative examples, including a practical scenario for protecting building occupancy, demonstrate the effectiveness of the proposed algorithm.

eess.SY↗

arXiv2024

AACP: Aesthetics assessment of children's paintings based on self-supervised learning

The Aesthetics Assessment of Children's Paintings (AACP) is an important branch of the image aesthetics assessment (IAA), playing a significant role in children's education. This task presents unique challenges, such as limited available data and the requirement for evaluation metrics from multiple perspectives. However, previous approaches have relied on training large datasets and subsequently providing an aesthetics score to the image, which is not applicable to AACP. To solve this problem, we construct an aesthetics assessment dataset of children's paintings and a model based on self-supervised learning. 1) We build a novel dataset composed of two parts: the first part contains more than 20k unlabeled images of children's paintings; the second part contains 1.2k images of children's paintings, and each image contains eight attributes labeled by multiple design experts. 2) We design a pipeline that includes a feature extraction module, perception modules and a disentangled evaluation module. 3) We conduct both qualitative and quantitative experiments to compare our model's performance with five other methods using the AACP dataset. Our experiments reveal that our method can accurately capture aesthetic features and achieve state-of-the-art performance.

cs.CV↗

arXiv2023

Consensus-Based Distributed Nonlinear Filtering with Kernel Mean Embedding

This paper proposes a consensus-based distributed nonlinear filter with kernel mean embedding (KME). This fills with gap of posterior density approximation with KME for distributed nonlinear dynamic systems. To approximate the posterior density, the system state is embedded into a higher-dimensional reproducing kernel Hilbert space (RKHS), and then the nonlinear measurement function is linearly converted. As a result, an update rule of KME of posterior distribution is established in the RKHS. To show the proposed distributed filter being capable of achieving the centralized estimation accuracy, a centralized filter, serving as an extension of the standard Kalman filter in the state space to the RKHS, is developed first. Benefited from the KME, the proposed distributed filter converges to the centralized one while maintaining the distributed pattern. Two examples are introduced to demonstrate the effectiveness of the developed filters in target tracking scenarios including nearly constantly moving target and turning target, respectively, with bearing-only, range and bearing measurements.

eess.SY↗

arXiv2023

Plasmonic-enhanced bright single spin defects in silicon carbide membranes

Optically addressable spin defects in silicon carbide (SiC) have emerged as attractable platforms for various quantum technologies. However, the low photon count rate significantly limits their applications. We strongly enhanced the brightness by 7 times and spin-control strength by 14 times of single divacancy defects in 4H-SiC membranes using surface plasmon generated by gold film coplanar waveguides. The mechanism of the plasmonic-enhanced effect is further studied by tuning the distance between single defects and the surface of the gold film. A three-energy-level model is used to determine the corresponding transition rates consistent with the enhanced brightness of single defects. Lifetime measurements also verified the coupling between defects and surface plasmons. Our scheme is low-cost, without complicated microfabrication and delicate structures, which is applicable for other spin defects in different materials. This work would promote developing spin defect-based quantum applications in mature SiC materials.

physics.optics↗

arXiv2018

Dynamical evolution of an effective two-level system with PT symmetry

We investigate the dynamics of parity- and time-reversal (PT ) symmetric two-energy-level atoms in the presence of two optical and a radio-frequency (rf) fields. The strength and relative phase of fields can drive the system from unbroken to broken PT symmetric regions. Compared with the Hermitian model, Rabi-type oscillation is still observed, and the oscillation characteristics are also adjusted by the strength and relative phase in the region of unbroken PT symmetry. At exception point (EP), the oscillation breaks down. To better understand the underlying properties we study the effective Bloch dynamics and find the emergence of the z components of the fixed points is the feature of the PT symmetry breaking and the projections in x-y plane can be controlled with high flexibility compared with the standard two-level system with PT symmetry. It helps to study the dynamic behavior of the complex PT symmetric model.

cond-mat.quant-gas↗

arXiv2018

Dynamical evolutions in non-Hermitian triple-well system with complex potential

We investigate the dynamical properties for non-Hermitian triple-well system with a loss in the middle well. When chemical potentials in two end wells are uniform and nonlinear interactions are neglected, there always exists a dark state, whose eigenenergy becomes zero, and the projections onto which do not change over time and the loss factor. The increasing of loss factor only makes the damping form from the oscillating decay to over-damping decay. However, when the nonlinear interaction is introduced, even interactions in the two end wells are also uniform, the projection of the dark state will be obviously diminished. Simultaneously the increasing of loss factor will also aggravate the loss. In this process the interaction in the middle well plays no role. When two chemical potentials or interactions in two end wells are not uniform all disappear with time. In addition, when we extend the triple-well system to a general (2n + 1)-well, the loss is reduced greatly by the factor 1=2n in the absence of the nonlinear interaction.

cond-mat.quant-gas↗

arXiv2015

Geometric Curvature and Phase of the Rabi model

We study the geometric curvature and phase of the Rabi model. Under the rotating-wave approximation (RWA), we apply the gauge independent Berry curvature over a surface integral to calculate the Berry phase of the eigenstates for both single and two-qubit systems, which is found to be identical with the system of spin-1/2 particle in a magnetic field. We extend the idea to define a vacuum-induced geometric curvature when the system starts from an initial state with pure vacuum bosonic field. The induced geometric phase is related to the average photon number in a period which is possible to measure in the qubit-cavity system. We also calculate the geometric phase beyond the RWA and find an anomalous sudden change, which implies the breakdown of the adiabatic theorem and the Berry phases in an adiabatic cyclic evolution are ill-defined near the anti-crossing point in the spectrum.

quant-ph↗

arXiv2014

High-Sensitivity Temperature Sensing Using an Implanted Single Nitrogen-Vacancy Center Array in Diamond

We presented a high-sensitivity temperature detection using an implanted single Nitrogen-Vacancy center array in diamond. The high-order Thermal Carr-Purcell-Meiboom-Gill (TCPMG) method was performed on the implanted single nitrogen vacancy (NV) center in diamond in a static magnetic field. We demonstrated that under small detunings for the two driving microwave frequencies, the oscillation frequency of the induced fluorescence of the NV center equals approximately to the average of the detunings of the two driving fields. On basis of the conclusion, the zero-field splitting D for the NV center and the corresponding temperature could be determined. The experiment showed that the coherence time for the high-order TCPMG was effectively extended, particularly up to 108 μs for TCPMG-8, about 14 times of the value 7.7 μs for thermal Ramsey method. This coherence time corresponded to a thermal sensitivity of 10.1 mK/Hz1/2. We also detected the temperature distribution on the surface of a diamond chip in three different circumstances by using the implanted NV center array with the TCPMG-3 method. The experiment implies the feasibility for using implanted NV centers in high-quality diamonds to detect temperatures in biology, chemistry, material science and microelectronic system with high-sensitivity and nanoscale resolution.

quant-ph↗

arXiv2009

Cavity-enhanced detection of magnetic orders in lattice spin models

We develop a general scheme for detecting spin correlations inside a two-component lattice gas of bosonic atoms, stimulated by the recent theoretical and experimental advances on analogous systems for a single component quantum gas. Within a linearized theory for the transmission spectra of the cavity mode field, different magnetic phases of a two-component (spin 1/2) lattice bosons become clearly distinguishable. In the Mott-insulating (MI) state with unit filling for the two-component lattice bosons, three different phases: antiferromagnetic, ferromagnetic, and the XY phases are found to be associated with drastically different cavity photon numbers. Our suggested study can be straightforwardly implemented with current cold atom experiments.

cond-mat.str-el↗

arXiv2007

Ground-state Competition of Two-Component Bosons in Optical Lattice near a Feshbach Resonance

We investigate the ground state properties of an equal mixture of two species of bosons in its Mott-insulator phase at a filling factor two per site. We identify one type of spin triplet-singlet transition through the competition of ground state. When the on-site interaction is weak ($U U_c$), higher band will be populated. Due to the orbital coupling term $S^+S^-$ in the Hamiltonian, the two atoms in different orbits on a site would form an on-site singlet. For a non-SU(2)-symmetric model, easy-axis or easy-plane ferromagnetic spin exchange models may be realized corresponding to phase separation or counter-flow superfluidity, respectively.

cond-mat.other↗