arXiv subjects

Qing Yan

Publications and source records attributed to Qing Yan.

29 records · Page 2Linked to original sources

arXiv2023

Transport theory in non-Hermitian systems

Non-Hermitian systems have garnered significant attention due to the emergence of novel topology of complex spectra and skin modes. However, investigating transport phenomena in such systems faces obstacles stemming from the non-unitary nature of time evolution. Here, we establish the continuity equation for a general non-Hermitian Hamiltonian in the Schrödinger picture. It attributes the universal non-conservativity to the anti-commutation relationship between particle number and non-Hermitian terms. Our work derives a comprehensive current formula for non-Hermitian systems using Green's function, applicable to both time-dependent and steady-state responses. To demonstrate the validity of our approach, we calculate the local current in models with one-dimensional and two-dimensional settings, incorporating scattering potentials. The spatial distribution of local current highlights the widespread non-Hermitian phenomena, including skin modes, non-reciprocal quantum dots, and corner states. Our findings offer valuable insights for advancing theoretical and experimental research in the transport of non-Hermitian systems.

cond-mat.mes-hall↗

arXiv2023

Classical-Assisted Quantum Ground State Preparation with Tensor Network States and Monte Carlo Sampling

Quantum computing offers potential solutions for finding ground states in condensed-matter physics and chemistry. However, achieving effective ground state preparation is also computationally hard for arbitrary Hamiltonians. It is necessary to propose certain assumptions to make this problem efficiently solvable, including preparing a trial state of a non-trivial overlap with the genuine ground state. Here, we propose a classical-assisted quantum ground state preparation method for quantum many-body systems, combining Tensor Network States (TNS) and Monte Carlo (MC) sampling as a heuristic method to prepare a trial state with a non-trivial overlap with the genuine ground state. We extract a sparse trial state by sampling from TNS, which can be efficiently prepared by a quantum algorithm on early fault-tolerant quantum computers. Our method demonstrates a polynomial improvement in scaling of overlap between the trial state and genuine ground state compared to random trial states, as evidenced by numerical tests on the spin-$1/2$ $J_1$-$J_2$ Heisenberg model. Furthermore, our method is a novel approach to hybridize a classical numerical method and a quantum algorithm and brings inspiration to ground state preparation in other fields.

quant-ph↗

arXiv2023

Universal spin superconducting diode effect from spin-orbit coupling

We propose a universal spin superconducting diode effect (SDE) induced by spin-orbit coupling (SOC), where the critical spin supercurrents in opposite directions are unequal. By analysis from both the Ginzburg-Landau theory and energy band analysis, we show that the spin-$\uparrow \uparrow$ and spin-$\downarrow \downarrow$ Cooper pairs possess opposite phase gradients and opposite momenta from the SOC, which leads to the spin SDE. Two superconductors with SOC, a $p$-wave superconductor as a toy model and a practical superconducting nanowire, are numerically studied and they both exhibit spin SDE. In addition, our theory also provides a unified picture for both spin and charge SDEs. Besides, we propose spin-polarized detection and nonlocal spin transport, as mature experimental technologies, to confirm the spin SDE in superconducting nanowires.

cond-mat.supr-con↗

arXiv2021

Spin-triplet superconductor$-$quantum anomalous Hall insulator$-$spin-triplet superconductor Josephson junctions: $0$-$π$ transition, $ϕ_{0}$ phase and switch effects

We study the Josephson effect in spin-triplet superconductor$-$quantum anomalous Hall insulator$-$spin-triplet superconductor junctions using the nonequilibrium Green function method. The current-phase difference relations show strong dependence on the orientations of the $\bf{d}$-vectors in superconductors. We focus on two $\bf{d}$-vector configurations, the parallel one with the left and right ${\bf{d}}$-vectors being in the same direction, and the nonparallel one with the left ${\bf{d}}$-vector fixed at the $z$-axis. For the parallel configuration, the $0$-$π$ transition can be realized when one rotates the ${\bf{d}}$-vectors from the parallel to the junction plane to the perpendicular direction. The $ϕ_{0}$ phase with nonzero Josephson current at zero phase difference can be obtained as long as ${d_{x}}{d_{z}}\ne0$. For the nonparallel configuration, the $0$-$π$ transition and the $ϕ_{0}$ phase still exist. The condition for the formation of the $ϕ_{0}$ phase becomes $d_{Rx}\ne0$. The switch effects of the Josephson current are found in both configurations when the ${\bf{d}}$-vectors are rotated in the $xy$ plane. Furthermore, the symmetries satisfied by the current-phase difference relations are analysed in details by the operations of the time-reversal, mirror-reflections, the spin-rotation and the gauge transformation, which can well explain the above selection rules for the $ϕ_{0}$ phase. Our results reveal the peculiar Josephson effect between spin-triplet superconductors and the quantum anomalous Hall insulator, which provide helpful phases and effects for the device designs. The distinct current-phase difference relations for different orientations may be used to determine the direction of the ${\bf{d}}$-vector in the spin-triplet superconductor.

cond-mat.supr-con↗

arXiv2021

Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?

Unsupervised outlier detection, which predicts if a test sample is an outlier or not using only the information from unlabelled inlier data, is an important but challenging task. Recently, methods based on the two-stage framework achieve state-of-the-art performance on this task. The framework leverages self-supervised representation learning algorithms to train a feature extractor on inlier data, and applies a simple outlier detector in the feature space. In this paper, we explore the possibility of avoiding the high cost of training a distinct representation for each outlier detection task, and instead using a single pre-trained network as the universal feature extractor regardless of the source of in-domain data. In particular, we replace the task-specific feature extractor by one network pre-trained on ImageNet with a self-supervised loss. In experiments, we demonstrate competitive or better performance on a variety of outlier detection benchmarks compared with previous two-stage methods, suggesting that learning representations from in-domain data may be unnecessary for outlier detection.

cs.CV↗

arXiv2021

EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss

Maximum likelihood estimation is widely used in training Energy-based models (EBMs). Training requires samples from an unnormalized distribution, which is usually intractable, and in practice, these are obtained by MCMC algorithms such as Langevin dynamics. However, since MCMC in high-dimensional space converges extremely slowly, the current understanding of maximum likelihood training, which assumes approximate samples from the model can be drawn, is problematic. In this paper, we try to understand this training procedure by replacing Langevin dynamics with deterministic solutions of the associated gradient descent ODE. Doing so allows us to study the density induced by the dynamics (if the dynamics are invertible), and connect with GANs by treating the dynamics as generator models, the initial values as latent variables and the loss as optimizing a critic defined by the very same energy that determines the generator through its gradient. Hence the term - self-adversarial loss. We show that reintroducing the noise in the dynamics does not lead to a qualitative change in the behavior, and merely reduces the quality of the generator. We thus show that EBM training is effectively a self-adversarial procedure rather than maximum likelihood estimation.

cs.LG↗

arXiv2020

Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder

Deep probabilistic generative models enable modeling the likelihoods of very high dimensional data. An important application of generative modeling should be the ability to detect out-of-distribution (OOD) samples by setting a threshold on the likelihood. However, some recent studies show that probabilistic generative models can, in some cases, assign higher likelihoods on certain types of OOD samples, making the OOD detection rules based on likelihood threshold problematic. To address this issue, several OOD detection methods have been proposed for deep generative models. In this paper, we make the observation that many of these methods fail when applied to generative models based on Variational Auto-encoders (VAE). As an alternative, we propose Likelihood Regret, an efficient OOD score for VAEs. We benchmark our proposed method over existing approaches, and empirical results suggest that our method obtains the best overall OOD detection performances when applied to VAEs.

cs.LG↗

arXiv2020

Exponential Tilting of Generative Models: Improving Sample Quality by Training and Sampling from Latent Energy

In this paper, we present a general method that can improve the sample quality of pre-trained likelihood based generative models. Our method constructs an energy function on the latent variable space that yields an energy function on samples produced by the pre-trained generative model. The energy based model is efficiently trained by maximizing the data likelihood, and after training, new samples in the latent space are generated from the energy based model and passed through the generator to producing samples in observation space. We show that using our proposed method, we can greatly improve the sample quality of popular likelihood based generative models, such as normalizing flows and VAEs, with very little computational overhead.

cs.LG↗

arXiv2019

Electrically tunable chiral Majorana edge modes in quantum anomalous Hall insulator-topological superconductor systems

Chiral Majorana edge modes are theoretically proposed to perform braiding operations for the potential quantum computation. Here, we suggest a scheme to regulate trajectories of the chiral Majorana fermion based on a quantum anomalous Hall insulator (QAHI)-topological superconductor heterostructure. An applied external gate voltage to the QAHI region introduces a dynamical phase so that the outgoing Majorana fermions can be prominently tuned to different leads. The trajectory is mechanically analyzed and the electrical manipulation is represented by the oscillating transmission coefficients versus the gate voltage. Through the optimization of devices, the conductance is likewise detectable to be periodically oscillating, which means an experimental control of chiral Majorana edge modes. Besides, this oscillating period which is robust against disorder also provides an attainable method of observing the energy dispersion relation of the edge mode of the QAHI. Furthermore, the oscillating behavior of conductance serves as smoking-gun evidence of the existence of the chiral Majorana fermion, which could be experimentally confirmed.

cond-mat.mes-hall↗

arXiv2019

A Method to Model Conditional Distributions with Normalizing Flows

In this work, we investigate the use of normalizing flows to model conditional distributions. In particular, we use our proposed method to analyze inverse problems with invertible neural networks by maximizing the posterior likelihood. Our method uses only a single loss and is easy to train. This is an improvement on the previous method that solves similar inverse problems with invertible neural networks but which involves a combination of several loss terms with ad-hoc weighting. In addition, our method provides a natural framework to incorporate conditioning in normalizing flows, and therefore, we can train an invertible network to perform conditional generation. We analyze our method and perform a careful comparison with previous approaches. Simple experiments show the effectiveness of our method, and more comprehensive experimental evaluations are undergoing.

cs.LG↗

arXiv2019

Generative Latent Flow

In this work, we propose the Generative Latent Flow (GLF), an algorithm for generative modeling of the data distribution. GLF uses an Auto-encoder (AE) to learn latent representations of the data, and a normalizing flow to map the distribution of the latent variables to that of simple i.i.d noise. In contrast to some other Auto-encoder based generative models, which use various regularizers that encourage the encoded latent distribution to match the prior distribution, our model explicitly constructs a mapping between these two distributions, leading to better density matching while avoiding over regularizing the latent variables. We compare our model with several related techniques, and show that it has many relative advantages including fast convergence, single stage training and minimal reconstruction trade-off. We also study the relationship between our model and its stochastic counterpart, and show that our model can be viewed as a vanishing noise limit of VAEs with flow prior. Quantitatively, under standardized evaluations, our method achieves state-of-the-art sample quality among AE based models on commonly used datasets, and is competitive with GANs' benchmarks.

cs.CV↗