arXiv subjects

Lei Du

Publications and source records attributed to Lei Du.

At least 55 records · Page 3Linked to original sources

arXiv2023

Perfect Transfer of enhanced entanglement and asymmetric steering in a cavity magnomechanical system

We propose a hybrid cavity magnomechanical system to realize and transfer the bipartite entanglements and Einstein-Podolsky-Rosen (EPR) steerings between magnons, photons, and phonons in the regime of stability of the system. As a parity-time-symmetric-like structure exhibiting the natural magnetostrictive magnon-phonon interaction, our passive-active cavity system can be explored to enhance the robust distant quantum entanglement and generate the relatively obvious asymmetric (even directional) EPR steering that is useful for the task with the highly asymmetric trusts of the bidirectional local measurements between two entangled states. It is of great interest that, based on such a tunable magnomechanical system, the perfect transfer between near and distant entanglements and steerings of different mode pairs is realized by adjusting the coupling parameters; in particular, we propose a perfect transfer scheme of steerings. These transferring processes suggest indeed an alternative method for quantum information storage and manipulation. In addition, the entanglements and steerings can also be exchanged between different mode pairs by adjusting the detunings between different modes. This work may provide a potential platform for distant and asymmetric quantum modulation.

quant-ph↗

arXiv2023

Complex decoherence-free interactions between giant atoms

Giant atoms provide a promising platform for engineering decoherence-free interactions which is a major task in modern quantum technologies. Here we study systematically how to implement complex decoherence-free interactions among giant atoms resorting to periodic coupling modulations and suitable arrangements of coupling points. We demonstrate that the phase of the modulation, which is tunable in experiments, can be encoded into the decoherence-free interactions, and thus enables phase-dependent dynamics when the giant atoms constitute an effective closed loop. Moreover, we consider the influence of non-Markovian retardation effect arising from large separations of the coupling points and study its dependence on the modulation parameters.

quant-ph↗

arXiv2023

Cohomologies and deformations of coassociative coderivations

The motivation of this paper is to construct a deformation theory of coderivations of coassociative coalgebras. We introduce a notion of a Coder pair, that is, a coassociative coalgebra with a coderivation. Then we define a proper corepresentation of a Coder pair and study the corresponding cohomology. Finally, we show that a Coder pair is rigid, provided that the second cohomology group is trivial and point out that a deformation of finite order is extensible to higher order deformations if the obstruction class, which is defined to be in the third cohomology group, is trivial.

math.RA↗

arXiv2022

A giant atom with modulated transition frequency

Giant atoms are known for the frequency-dependent spontaneous emission and associated interference effects. In this paper, we study the spontaneous emission dynamics of a two-level giant atom with dynamically modulated transition frequency. It is shown that the retarded feedback effect of the giant-atom system is greatly modified by a dynamical phase arising from the frequency modulation and the retardation effect itself. Interestingly, such a modification can in turn suppress the retarded feedback such that the giant atom behaves like a small one. By introducing an additional phase difference between the two atom-waveguide coupling paths, we also demonstrate the possibility of realizing chiral and tunable temporal profiles of the output fields. The results in this paper have potential applications in quantum information processing and quantum network engineering.

quant-ph↗

arXiv2022

Enhanced sensing of optomechanically induced nonlinearity by linewidth suppression and optical bistability in cavity-waveguide systems

We study enhanced sensing of optomechanically induced nonlinearity (OMIN) in a cavity-waveguide coupled system. The Hamiltonian of the system is anti-PT symmetric with the two involved cavities being dissipatively coupled via the waveguide. When a weak waveguide-mediated coherent coupling is introduced, the anti-PT symmetry may break down. However, we find a strong bistable response of the cavity intensity to the OMIN near the cavity resonance, benefiting from linewidth suppression caused by the vacuum induced coherence. The joint effect of optical bistability and the linewidth suppression is inaccessible by the anti-PT symmetric system involving only dissipative coupling. Due to that, the sensitivity is greatly enhanced by two orders of magnitude compared to that for the anti-PT symmetric model. Moreover, the sensitivity shows resistances to a reasonably large cavity decay and robustness to fluctuations in the cavity-waveguide detuning. Based on the integrated optomechanical cavity-waveguide systems, the scheme can be used for sensing different physical quantities related to the single-photon coupling strength, and has potential applications in high-precision measurements with physical systems involving Kerr-type nonlinearity.

quant-ph↗

arXiv2022

Giant atoms with time-dependent couplings

We study the decay dynamics of a two-level giant atom that is coupled to a waveguide with time-dependent coupling strengths. In the non-Markovian regime where the retardation effect cannot be ignored, we show that the dynamics of the atom depends on the atom-waveguide coupling strengths at an earlier time. This allows one to tailor the decay dynamics of the giant atom and even realize a stationary population revival with appropriate coupling modulations. Moreover, we demonstrate the possibility of simulating the quantum Zeno and quantum anti-Zeno effects in the giant-atom model with periodic coupling quenches. These results have potential applications in quantum information processing and quantum network engineering.

quant-ph↗

arXiv2022

Giant Atoms in a Synthetic Frequency Dimension

Giant atoms that interact with real-space waveguides at multiple spatial points have attracted extensive attention due to their unique interference effects. Here we propose a feasible scheme for constructing giant atoms in a synthetic frequency dimension with, e.g., a dynamically modulated superconducting resonator and a tailored three-level artificial atom. Both analytical and numerical calculations show good agreement between our scheme and real-space two-level giant atoms. In particular, the symmetry of the model in momentum space can be broken by tuning the phase of the external field applied on the atom, enabling chiral interactions between the atom and the frequency lattice. We further demonstrate the possibility of simulating cascaded interaction and directional excitation transfer in the frequency dimension by directly extending our model to involve more such effective giant atoms.

quant-ph↗

arXiv2022

Nonreciprocal frequency conversion with chiral $Λ$-type atoms

In this paper, we begin with a model of a $Λ$-type atom whose both transitions are chirally coupled to a waveguide and then extend the model to its giant-atom version. We investigate the single-photon scatterings of the giant-atom model in both the Markovian and non-Markovian regimes. It is shown that the chiral atom-waveguide couplings enable nonreciprocal, reflectionless, and efficient frequency conversion, while the giant-atom structure introduces intriguing interference effects to the scattering behaviors, such as ultra-narrow scattering windows. The chiral giant-atom model exhibits quite different scattering spectra in the two regimes and, in particular, demonstrates non-Markovicity induced nonreciprocity under specific conditions. These phenomena can be understood from the effective detuning and decay rate of the giant-atom model. Our results have potential applications in integrated photonics and quantum network engineering.

quant-ph↗

arXiv2021

Quantum transport in nonlinear Rudner-Levitov models

Quantum transport in a class of nonlinear extensions of the Rudner-Levitov model is numerically studied in this paper. We show that the quantization of the mean displacement, which embodies the quantum coherence and the topological characteristics of the model, is markedly modified by nonlinearities. Peculiar effects such as a "trivial-nontrivial" transition and unidirectional long-range quantum transport are observed. These phenomena can be understood on the basis of the dynamic behavior of the effective hopping terms, which are time and position dependent, containing contributions of both the linear and nonlinear couplings.d nonlinear couplings.

quant-ph↗

arXiv2021

Single-photon frequency conversion via a giant $Λ$-type atom

We study single-photon scattering via a giant $Λ$-type atom, where both atomic transitions are coupled with the modes of a single waveguide at two separated points. The giant-atom structure introduces phase-dependent interference effects to both elastic (frequency-preserving) and inelastic (frequency-converting) scattering processes, which modify the corresponding decay rates (as well as the transition frequencies) such that the giant atom is capable of accessing the various limits of a small one. The condition of the optimal frequency conversion is also identified and shown to be phase dependent. Moreover, we consider the combination of the giant-atom interference and the Sagnac quantum interference by further inserting a Sagnac interferometer at each of the coupling points. It is shown that the two kinds of interference effects are compatible and play independent roles, such that efficient frequency conversion with unit efficiency can be achieved in addition to the phase-dependent phenomena induced by the giant-atom structure.

quant-ph↗

arXiv2021

Single-photon nonreciprocal excitation transfer with non-Markovian retarded effects

We study at the single-photon level the nonreciprocal excitation transfer between emitters coupled with a common waveguide. Non-Markovian retarded effects are taken into account due to the large separation distance between different emitter-waveguide coupling ports. It is shown that the excitation transfer between the emitters of a small-atom dimer can be obviously nonreciprocal by introducing between them a coherent coupling channel with nontrivial coupling phase. We prove that for dimer models the nonreciprocity cannot coexist with the decoherence-free giant-atom structure although the latter markedly lengthens the lifetime of the emitters. In view of this, we further propose a giant-atom trimer which supports both nonreciprocal transfer (directional circulation) of the excitation and greatly lengthened lifetime. Such a trimer model also exhibits incommensurate emitter-waveguide entanglement for different initial states in which case the excitation transfer is however reciprocal. We believe that the proposals in this paper are of potential applications in large-scale quantum networks and quantum information processing.

quant-ph↗

arXiv2021

A Multi-task Deep Feature Selection Method for Brain Imaging Genetics

Using brain imaging quantitative traits (QTs) to identify the genetic risk factors is an important research topic in imaging genetics. Many efforts have been made via building linear models, e.g. linear regression (LR), to extract the association between imaging QTs and genetic factors such as single nucleotide polymorphisms (SNPs). However, to the best of our knowledge, these linear models could not fully uncover the complicated relationship due to the loci's elusive and diverse impacts on imaging QTs. Though deep learning models can extract the nonlinear relationship, they could not select relevant genetic factors. In this paper, we proposed a novel multi-task deep feature selection (MTDFS) method for brain imaging genetics. MTDFS first adds a multi-task one-to-one layer and imposes a hybrid sparsity-inducing penalty to select relevant SNPs making significant contributions to abnormal imaging QTs. It then builds a multi-task deep neural network to model the complicated associations between imaging QTs and SNPs. MTDFS can not only extract the nonlinear relationship but also arms the deep neural network with the feature selection capability. We compared MTDFS to both LR and single-task DFS (DFS) methods on the real neuroimaging genetic data. The experimental results showed that MTDFS performed better than both LR and DFS in terms of the QT-SNP relationship identification and feature selection. In a word, MTDFS is powerful for identifying risk loci and could be a great supplement to the method library for brain imaging genetics.

q-bio.GN↗

arXiv2021

Parasitic modes suppression in CW cold tests of 1.3 GHz 9-cell high Q cavities at IHEP

The CW RF test of 1.3 GHz 9-cell cavity in liquid helium bath at 2 K is a very important key point in the cavity procurement. Some problems can be found through the test, according which to optimized and improve the process of cavity. Recently, Medium temperature (mid-T) furnace bake of 1.3 GHz 9-cell cavities have been carried out at IHEP. Through the proceed of mid-T bake, the quality factor of cavity has been greatly improved. While the excitation of the parasitic modes in the high Q cavities CW cold test has been encountered, which implies an error source for the cavity gradient and quality factor determination. In order to ensure the testing accuracy of superconducting cavity, we have improved the testing system. Finally, the parasitic mode is completely suppressed and the CW RF cold test of high Q cavity is guaranteed.

physics.acc-ph↗

arXiv2020

Medium-temperature furnace bake of Superconducting Radio-Frequency cavities at IHEP

Recently, heat treatment between 250 C and 500 C has been attempted to improve quality factor of superconducting radio-frequency cavities at FNAL and KEK. Experiments of such medium temperature (mid-T) bake with furnaces have also been carried out at IHEP. Firstly, eleven 1.3 GHz 1-cell cavities were treated with different temperatures at a small furnace. The average quality factor has reached 3.6E10 when the gradient is 16 MV/m. Then, the recipe of mid-T furnace bake at 300 C for 3 hours has been applied to six 1.3 GHz 9-cell cavities at a new big furnace. The average quality factor has reached 3.8E10 when the gradient is 16 MV/m.

physics.acc-ph↗

arXiv2020

Controllable optical response and tunable sensing based on self interference in waveguide QED systems

We study the self interference effect of a resonator coupled with a bent waveguide at two separated ports. Such interference effects are shown to be similar for the cases of standing-wave and traveling-wave resonators, while in the system of two separated resonators indirectly coupled via a waveguide, the coupling forms and the related interference effects depend on which kind of resonators is chosen. Due to the self interference, controllable optical responses including tunable linewidth and frequency shift, and optical dark state can be achieved. Moreover, we consider a self-interference photon-magnon hybrid model and show phase-dependent Fano-like line shapes which have potential applications in frequency sensing. The photon-magnon hybridization can not only enhance the sensitivity and provide tunable working region, but also enables optical readout of the magnetic field strength in turn. The results in this paper provide a deeper insight into the self interference effect and its potential applications.

physics.optics↗

arXiv2020

Knowledge-aware Attention Network for Protein-Protein Interaction Extraction

Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. However, many of the current PPI extraction methods need extensive feature engineering and cannot make full use of the prior knowledge in knowledge bases (KB). KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in PPI extraction. This paper proposes a knowledge-aware attention network (KAN) to fuse prior knowledge about protein-protein pairs and context information for PPI extraction. The proposed model first adopts a diagonal-disabled multi-head attention mechanism to encode context sequence along with knowledge representations learned from KB. Then a novel multi-dimensional attention mechanism is used to select the features that can best describe the encoded context. Experiment results on the BioCreative VI PPI dataset show that the proposed approach could acquire knowledge-aware dependencies between different words in a sequence and lead to a new state-of-the-art performance.

cs.CL↗

arXiv2019

Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction

Background: Automatic extraction of chemical-disease relations (CDR) from unstructured text is of essential importance for disease treatment and drug development. Meanwhile, biomedical experts have built many highly-structured knowledge bases (KBs), which contain prior knowledge about chemicals and diseases. Prior knowledge provides strong support for CDR extraction. How to make full use of it is worth studying. Results: This paper proposes a novel model called "Knowledge-guided Convolutional Networks (KCN)" to leverage prior knowledge for CDR extraction. The proposed model first learns knowledge representations including entity embeddings and relation embeddings from KBs. Then, entity embeddings are used to control the propagation of context features towards a chemical-disease pair with gated convolutions. After that, relation embeddings are employed to further capture the weighted context features by a shared attention pooling. Finally, the weighted context features containing additional knowledge information are used for CDR extraction. Experiments on the BioCreative V CDR dataset show that the proposed KCN achieves 71.28% F1-score, which outperforms most of the state-of-the-art systems. Conclusions: This paper proposes a novel CDR extraction model KCN to make full use of prior knowledge. Experimental results demonstrate that KCN could effectively integrate prior knowledge and contexts for the performance improvement.

cs.CL↗

arXiv2019

Improving Neural Protein-Protein Interaction Extraction with Knowledge Selection

Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. Meanwhile, knowledge bases (KBs) contain huge amounts of structured information of protein entities and their relations, which can be encoded in entity and relation embeddings to help PPI extraction. However, the prior knowledge of protein-protein pairs must be selectively used so that it is suitable for different contexts. This paper proposes a Knowledge Selection Model (KSM) to fuse the selected prior knowledge and context information for PPI extraction. Firstly, two Transformers encode the context sequence of a protein pair according to each protein embedding, respectively. Then, the two outputs are fed to a mutual attention to capture the important context features towards the protein pair. Next, the context features are used to distill the relation embedding by a knowledge selector. Finally, the selected relation embedding and the context features are concatenated for PPI extraction. Experiments on the BioCreative VI PPI dataset show that KSM achieves a new state-of-the-art performance (38.08% F1-score) by adding knowledge selection.

cs.CL↗