arXiv subjects

Zhihan Li

Publications and source records attributed to Zhihan Li.

23 records · Page 2Linked to original sources

arXiv2020

Simulation of nodal-line semimetal in amplitude-shaken optical lattices

With topologcial semimetal developing, semimetal with nodal-line ring comes into people's vision as a powerful candidate for practical application of topological devices. We propose a method using ultracold atoms in two-dimensional amplitude-shaken bipartite hexagonal optical lattice to simulate nodal-line semimetal, which can be achieved in experiment by attaching one triangular optical lattice to a hexangonal optical lattice and periodically modulating the intensity and position of the triangular lattice. By amplitude shaking, a time-reversal-symmetry-unstable mode is introduced into the bipartite optical lattice, and then the nodal-line semimetal is gotten by adjusting the proportion of such mode and the trivial mode of hexagonal lattice. Through calculating the energy spectrum of effective Hamiltonian, the transformation from Dirac semimetal to nodal-line semimetal in pace with changing shaking parameters is observed. We also study the change of Berry curvature and Berry phase in the transformation, which provides guidance on measuring the transformation in experiment. By analyzing the symmetry of the system, the emergence of the time-reversal-symmetry-unstable mode is researched. This proposal provides a way to research the pure nodal-line semimetal without the influence of other bands, which may contribute to the study of those unique features of surface states and bulk states of nodal-line semimetal.

cond-mat.quant-gas↗

arXiv2019

Asymmetric population of momentum distribution by quasi-periodically driving a triangular optical lattice

Ultracold atoms in periodical driven optical lattices enable us to investigate novel band structures and explore the topology of the bands. In this work, we investigate the impact of the ramping process of the driving signal and propose a simple but effective method to realize desired asymmetric population in momentum distribution by controlling the initial phase of the driving signal. A quasi-momentum oscillation along the shaking direction in the frame of reference co-moving with the lattice is formed, causing the formation of the mix of ground energy band and first excited band in laboratory frame, within the regime that the driving frequency is far less than the coupling frequency between ground band and higher energy bands. This method avoids the construction of intricate lattices or complex control sequence. With a triangular lattice, we experimentally investigate the influence of the initial phase, frequency, amplitude of the driving signal on the population difference, and observe good agreement with our theoretical model. This provides guidance on how to load a driving signal in driven optical lattice experiment and also potentially supplies a useful tool to form a qubit that can be used in quantum computation.

cond-mat.quant-gas↗

arXiv2019

Parallel multicomponent interferometer with a spinor Bose-Einstein condensate

Atom interferometry with high visibility is of high demand for precision measurements. Here, a parallel multicomponent interferometer is achieved by preparing a spin-$2$ Bose-Einstein condensate of $^{87}$Rb atoms confined in a hybrid magneto-optical trap. After the preparation of a spinor Bose-Einstein condensate with spin degrees of freedom entangled, we observe four spatial interference patterns in each run of measurements corresponding to four hyperfine states we mainly populate in the experiment. The atomic populations in different Zeeman sublevels are made controllably using magnetic-field-pulse induced Majorana transitions. The spatial separation of atom cloud in different hyperfine states is reached by Stern-Gerlach momentum splitting. The high visibility of the interference fringes is reached by designing a proper overlap of the interfering wave packets. Due to uncontrollable phase accumulation in Majorana transitions, the phase of each individual spin is found to be subjected to unreproducible shift in multiple experimental runs. However, the relative phase across different spins is stable, paving a way towards noise-resilient multicomponent parallel interferometers.

cond-mat.quant-gas↗

arXiv2019

On the Necessity and Effectiveness of Learning the Prior of Variational Auto-Encoder

Using powerful posterior distributions is a popular approach to achieving better variational inference. However, recent works showed that the aggregated posterior may fail to match unit Gaussian prior, thus learning the prior becomes an alternative way to improve the lower-bound. In this paper, for the first time in the literature, we prove the necessity and effectiveness of learning the prior when aggregated posterior does not match unit Gaussian prior, analyze why this situation may happen, and propose a hypothesis that learning the prior may improve reconstruction loss, all of which are supported by our extensive experiment results. We show that using learned Real NVP prior and just one latent variable in VAE, we can achieve test NLL comparable to very deep state-of-the-art hierarchical VAE, outperforming many previous works with complex hierarchical VAE architectures.

cs.LG↗

arXiv2018

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with various patterns and data quality has been a great challenge, especially without labels. In this paper, we proposed Donut, an unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our key techniques, Donut greatly outperforms a state-of-arts supervised ensemble approach and a baseline VAE approach, and its best F-scores range from 0.75 to 0.9 for the studied KPIs from a top global Internet company. We come up with a novel KDE interpretation of reconstruction for Donut, making it the first VAE-based anomaly detection algorithm with solid theoretical explanation.

cs.LG↗