arXiv subjects

Xiaopeng Li

Publications and source records attributed to Xiaopeng Li.

At least 181 records · Page 10Linked to original sources

arXiv2020

Chiral Induced Spin Selectivity as a Spontaneous Intertwined Order

Chiral induced spin selectivity (CISS) describes efficient spin filtering by chiral molecules. This phenomenon has led to nanoscale manipulation of quantum spins with promising applications to spintronics and quantum computing, since its discovery nearly two decades ago. However, its underlying mechanism still remains mysterious for the required spin-orbit interaction (SOI) strength is unexpectedly large. Here we report a multi-orbital theory for CISS, where an effective SOI emerges from spontaneous formation of electron-hole pairing caused by many-body correlation. This mechanism produces a strong SOI to the order of tens of milielectronvolts which could support the large spin polarization observed in CISS at room temperature. One central ingredient of our theory is the Wannier functions of the valence and conduction bands correspond respectively to one- and two-dimensional representation of the spatial rotation symmetry around the molecule elongation direction. The induced SOI strength is found to decrease when the band gap increases. Our theory may provide important guidance for searching other molecules with CISS effects.

quant-ph↗

arXiv2020

Programmable Quantum Annealing Architectures with Ising Quantum Wires

Quantum annealing aims at solving optimization problems efficiently by preparing the ground state of an Ising spin-Hamiltonian quantum mechanically. A prerequisite of building a quantum annealer is the implementation of programmable long-range two-, three- or multi-spin Ising interactions. We discuss an architecture, where the required spin interactions are implemented via two-port, or in general multi-port quantum Ising wires connecting the spins of interest. This quantum annealing architecture of spins connected by Ising quantum wires can be realized by exploiting the three dimensional (3D) character of atomic platforms, including atoms in optical lattices and Rydberg tweezer arrays. The realization only requires engineering on-site terms and two-body interactions between nearest neighboring qubits. The locally coupled spin model on a 3D cubic lattice is sufficient to effectively produce arbitrary all-to-all coupled Ising Hamiltonians. We illustrate the approach for few spin devices solving Max-Cut and prime factorization problems, and discuss the potential scaling to large atom based systems.

cond-mat.quant-gas↗

arXiv2020

Modeling and Analysis of Excess Commuting with Trip Chains

Commuting, like other types of human travel, is complex in nature, such as trip-chaining behavior involving making stops of multiple purposes between two anchors. According to the 2001 National Household Travel Survey, about one half of weekday U.S. workers made a stop during their commute. In excess commuting studies that examine a region's overall commuting efficiency, commuting is, however, simplified as nonstop travel from homes to jobs. This research fills this gap by proposing a trip-chaining-based model to integrate trip-chaining behavior into excess commuting. Based on a case study of the Tampa Bay region of Florida, this research finds that traditional excess commuting studies underestimate both actual and optimal commute, while overestimate excess commuting. For chained commuting trips alone, for example, the mean minimum commute time is increased by 70 percent from 5.48 minutes to 9.32 minutes after trip-chaining is accounted for. The gaps are found to vary across trip-chaining types by a disaggregate analysis by types of chain activities. Hence, policymakers and planners are cautioned of omitting trip-chaining behavior in making urban transportation and land use policies. In addition, the proposed model can be adopted to study the efficiency of non-work travel.

physics.soc-ph↗

arXiv2020

Precise Programmable Quantum Simulations with Optical Lattices

We present an efficient approach to precisely simulate tight binding models with optical lattices, based on programmable digital-micromirror-device (DMD) techniques. Our approach consists of a subroutine of Wegner-flow enabled precise extraction of a tight-binding model for a given optical potential, and a reverse engineering step of adjusting the potential for a targeting model, for both of which we develop classical algorithms to achieve high precision and high efficiency. With renormalization of Wannier functions and high band effects systematically calibrated in our protocol, we show the tight-binding models with programmable onsite energies and tunnelings can be precisely simulated with optical lattices integrated with the DMD techniques. With numerical simulation, we demonstrate that our approach would facilitate quantum simulation of localization physics with unprecedented programmability and atom-based boson sampling for illustration of quantum computational advantage. We expect this approach would pave a way towards large-scale and precise programmable quantum simulations based on optical lattices.

cond-mat.quant-gas↗

arXiv2020

A comparison study on the growth pattern of traffic oscillations in car-following experiments

The evolution of oscillations is a very important issue in traffic flow studies. A recent car-following experiment (Experiment-I) showed that the speed standard deviation grows in a concave way along a platoon of vehicles following one another. This finding indicates that the traditional traffic instability mechanism is debatable, in which the speed standard deviation initially grows in a convex way. This paper has investigated the growth pattern of traffic oscillations in another car-following experiment (Experiment-II) and compared it with that in Experiment-I. It is shown that the speed standard deviation also exhibits concave growth characteristics in Experiment-II. The paired-sample t-test and the Mann-Kendall (MK) trend test showed that there is no significant difference between the two datasets. However, the acceleration standard deviation was remarkably larger in Experiment-II since drivers were asked to follow closely. Furthermore, a comparison experiment has been performed which indicates that the set of experiments on a circular track can be considered equivalent to that on a straight track. Our study is expected to shed light not only on traffic flow dynamics itself but also on the future design of the experiment scheme.

physics.soc-ph↗

arXiv2020

Quantum Adiabatic Algorithm Design using Reinforcement Learning

Quantum algorithm design plays a crucial role in exploiting the computational advantage of quantum devices. Here we develop a deep-reinforcement-learning based approach for quantum adiabatic algorithm design. Our approach is generically applicable to a class of problems with solution hard-to-find but easy-to-verify, e.g., searching and NP-complete problems. We benchmark this approach in Grover-search and 3-SAT problems, and find that the adiabatic-algorithm obtained by our RL approach leads to significant improvement in the resultant success probability. In application to Grover search, our RL-design automatically produces an adiabatic quantum algorithm that has the quadratic speedup. We find for all our studied cases that quantitatively the RL-designed algorithm has a better performance compared to the analytically constructed non-linear Hamiltonian path when the encoding Hamiltonian is solvable, and that this RL-design approach remains applicable even when the non-linear Hamiltonian path is not analytically available. In 3-SAT, we find RL-design has fascinating transferability---the adiabatic algorithm obtained by training on a specific choice of clause number leads to better performance consistently over the linear algorithm on different clause numbers. These findings suggest the applicability of reinforcement learning for automated quantum adiabatic algorithm design. Further considering the established complexity-equivalence of circuit and adiabatic quantum algorithms, we expect the RL-designed adiabatic algorithm to inspire novel circuit algorithms as well. Our approach is potentially applicable to different quantum hardwares from trapped-ions and optical-lattices to superconducting-qubit devices.

quant-ph↗

arXiv2020

$f$-wave superfluidity from repulsive interaction in Rydberg-dressed Fermi gas

Interacting Fermi gas provides an ideal model system to understand unconventional pairing and intertwined orders relevant to a large class of quantum materials. Rydberg-dressed Fermi gas is a recent experimental system where the sign, strength, and range of the interaction can be controlled. The interaction in momentum space has a negative minimum at $q_c$ inversely proportional to the characteristic length-scale in real space, the soft-core radius $r_c$. We show theoretically that single-component (spinless) Rydberg-dressed Fermi gas in two dimensions has a rich phase diagram with novel superfluid and density wave orders due to the interplay of the Fermi momentum $p_F$, interaction range $r_c$, and interaction strength $u_0$. For repulsive bare interactions $u_0>0$, the dominant instability is $f$-wave superfluid for $p_Fr_c\lesssim 2$, and density wave for $p_Fr_c\gtrsim 4$. The $f$-wave pairing in this repulsive Fermi gas is reminiscent of the conventional Kohn-Luttinger mechanism, but has a much higher $T_c$. For attractive bare interactions $u_0<0$, the leading instability is $p$-wave pairing. The phase diagram is obtained from functional renormalization group that treats all competing many-body instabilities in the particle-particle and particle-hole channels on equal footing.

cond-mat.quant-gas↗

arXiv2020

Dimension Crossing Turbulent Cascade in an Excited Lattice Bose Gas

Turbulence is an intriguing non-equilibrium state, which originates from fluid mechanics and has far-reaching consequences in the description of climate physics, the characterization of quantum hydrodynamics, and the understanding of cosmic evolution. The concept of turbulent cascade describing the energy redistribution across different length scales offers one profound route to reconcile fundamental conservative forces with observational energy non-conservation of accelerating expansion of the universe bypassing the cosmological constant. Here, we observe a dimension crossing turbulent energy cascade in an atomic Bose-Einstein condensate confined in a two-dimensional (2d) optical lattice forming a 2d array of tubes, which exhibits universal behaviors in the dynamical energy-redistribution across different dimensions. By exciting atoms into the optical-lattice high bands, the excessive energy of this quantum many-body system is found to cascade from the transverse two-dimensional lattice directions to the continuous dimension, giving rise to a one-dimensional turbulent energy cascade, which is in general challenging to reach due to integrability. We expect this observed novel phenomenon of dimension-crossing energy cascade may inspire microscopic theories for modeling positive cosmological constant of our inflationary universe.

cond-mat.quant-gas↗

arXiv2019

Quantum Adiabatic Doping with Incommensurate Optical Lattices

Quantum simulations of Fermi-Hubbard models have been attracting considerable efforts in the optical lattice research, with the ultracold anti-ferromagnetic atomic phase reached at half filling in recent years. An unresolved issue is to dope the system while maintaining the low thermal entropy. Here we propose to achieve the low temperature phase of the doped Fermi-Hubbard model using incommensurate optical lattices through adiabatic quantum evolution. In this theoretical proposal, we find that one major problem about the adiabatic doping that shows up is atomic localization in the incommensurate lattice, potentially causing exponential slowing down of the adiabatic procedure. We study both one- and two-dimensional incommensurate optical lattices, and find that the localization prevents efficient adiabatic doping in the strong lattice regime for both cases. With density matrix renormalization group calculation, we further show that the slowing down problem in one dimension can be circumvented by considering interaction induced many-body delocalization, which is experimentally feasible using Feshbach resonance techniques. This protocol is expected to be efficient as well in two dimensions where the localization phenomenon is less stable.

cond-mat.quant-gas↗

arXiv2019

Not All Attention Is Needed: Gated Attention Network for Sequence Data

Although deep neural networks generally have fixed network structures, the concept of dynamic mechanism has drawn more and more attention in recent years. Attention mechanisms compute input-dependent dynamic attention weights for aggregating a sequence of hidden states. Dynamic network configuration in convolutional neural networks (CNNs) selectively activates only part of the network at a time for different inputs. In this paper, we combine the two dynamic mechanisms for text classification tasks. Traditional attention mechanisms attend to the whole sequence of hidden states for an input sentence, while in most cases not all attention is needed especially for long sequences. We propose a novel method called Gated Attention Network (GA-Net) to dynamically select a subset of elements to attend to using an auxiliary network, and compute attention weights to aggregate the selected elements. It avoids a significant amount of unnecessary computation on unattended elements, and allows the model to pay attention to important parts of the sequence. Experiments in various datasets show that the proposed method achieves better performance compared with all baseline models with global or local attention while requiring less computation and achieving better interpretability. It is also promising to extend the idea to more complex attention-based models, such as transformers and seq-to-seq models.

cs.LG↗

arXiv2019

Quantum Information Scrambling Through a High-Complexity Operator Mapping

Quantum information scrambling has attracted much attention amid the effort to reconcile the conflict between quantum-mechanical unitarity and the thermalizaiton-irreversibility in many-body systems. Here we propose an unconventional mechanism to generate quantum information scrambling through a high-complexity mapping from logical to physical degrees-of-freedom that hides the logical information into non-separable many-body-correlations. Corresponding to this mapping, we develop an algorithm to efficiently sample a Slater-determinant wavefunction and compute all physical observables in dynamics with a polynomial cost in system-size. The system shows information scrambling in the quantum many-body Hilbert space characterized by the spreading of Hamming-distance. At late time, we find emergence of classical diffusion dynamics in this quantum many-body system. We establish that the operator-mapping enabled growth in out-of-time-order-correlator exhibits exponential-scrambling behavior. The quantum information-hiding mapping approach may shed light on the understanding of fundamental connections among computational complexity, information scrambling and quantum thermalization.

quant-ph↗

arXiv2019

Exploiting the Inherent Limitation of L0 Adversarial Examples

Despite the great achievements made by neural networks on tasks such as image classification, they are brittle and vulnerable to adversarial example (AE) attacks, which are crafted by adding human-imperceptible perturbations to inputs in order that a neural-network-based classifier incorrectly labels them. In particular, L0 AEs are a category of widely discussed threats where adversaries are restricted in the number of pixels that they can corrupt. However, our observation is that, while L0 attacks modify as few pixels as possible, they tend to cause large-amplitude perturbations to the modified pixels. We consider this as an inherent limitation of L0 AEs, and thwart such attacks by both detecting and rectifying them. The main novelty of the proposed detector is that we convert the AE detection problem into a comparison problem by exploiting the inherent limitation of L0 attacks. More concretely, given an image I, it is pre-processed to obtain another image I' . A Siamese network, which is known to be effective in comparison, takes I and I' as the input pair to determine whether I is an AE. A trained Siamese network automatically and precisely captures the discrepancies between I and I' to detect L0 perturbations. In addition, we show that the pre-processing technique, inpainting, used for detection can also work as an effective defense, which has a high probability of removing the adversarial influence of L0 perturbations. Thus, our system, called AEPECKER, demonstrates not only high AE detection accuracies, but also a notable capability to correct the classification results.

cs.CR↗

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

Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering

We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features. In general, our superstructure is a tree structure of multiple super latent variables and it is automatically learned from data. When there is only one latent variable in the superstructure, our model reduces to one that assumes the latent features to be generated from a Gaussian mixture model. We call our model the latent tree variational autoencoder (LTVAE). Whereas previous deep learning methods for clustering produce only one partition of data, LTVAE produces multiple partitions of data, each being given by one super latent variable. This is desirable because high dimensional data usually have many different natural facets and can be meaningfully partitioned in multiple ways.

cs.LG↗

arXiv2018

Observation of a dynamical sliding phase superfluid with P-band bosons

Sliding phases have been long sought after in the context of coupled XY-models, as they are of relevance to various many-body systems such as layered superconductors, freestanding liquid-crystal films, and cationic lipid-DNA complexes. Here we report an observation of a dynamical sliding phase superfluid that emerges in a nonequilibrium setting from the quantum dynamics of a three-dimensional ultracold atomic gas loaded into the P-band of a one-dimensional optical lattice. A shortcut loading method is used to transfer atoms into the P-band at zero quasimomentum within a very short time duration. The system can be viewed as a series of "pancake"-shaped atomic samples. For this far-out-of-equilibrium system, we find an intermediate time window with a lifetime around tens of milliseconds, where the atomic ensemble exhibits robust superfluid phase coherence in the pancake directions, but no coherence in the lattice direction, which implies a dynamical sliding phase superfluid. The emergence of the sliding phase is attributed to a mechanism of cross-dimensional energy transfer in our proposed phenomenological theory, which is consistent with experimental measurements. This experiment potentially opens up a novel venue to search for exotic dynamical phases by creating high-band excitations in optical lattices.

cond-mat.quant-gas↗

arXiv2018

Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs

Binary code analysis allows analyzing binary code without having access to the corresponding source code. A binary, after disassembly, is expressed in an assembly language. This inspires us to approach binary analysis by leveraging ideas and techniques from Natural Language Processing (NLP), a rich area focused on processing text of various natural languages. We notice that binary code analysis and NLP share a lot of analogical topics, such as semantics extraction, summarization, and classification. This work utilizes these ideas to address two important code similarity comparison problems. (I) Given a pair of basic blocks for different instruction set architectures (ISAs), determining whether their semantics is similar or not; and (II) given a piece of code of interest, determining if it is contained in another piece of assembly code for a different ISA. The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection. We implement a prototype system INNEREYE and perform a comprehensive evaluation. A comparison between our approach and existing approaches to Problem I shows that our system outperforms them in terms of accuracy, efficiency and scalability. And the case studies utilizing the system demonstrate that our solution to Problem II is effective. Moreover, this research showcases how to apply ideas and techniques from NLP to large-scale binary code analysis.

cs.SE↗

arXiv2018

Which One to Go: Security and Usability Evaluation of Mid-Air Gestures

With the emerging of touch-less human-computer interaction techniques and gadgets, mid-air hand gestures have been widely used for authentication. Much literature examined either the usability or security of a handful of gestures. This paper aims at quantifying usability and security of gestures as well as understanding their relationship across multiple gestures. To study gesture-based authentication, we design an authentication method that combines Dynamic Time Warping (DTW) and Support Vector Machine (SVM), and conducted a user study with 42 participants over a period of 6 weeks. We objectively quantify the usability of a gesture by the number of corners and the frame length of all gesture samples, quantify the security using the equal error rate (EER), and the consistency by EER over a period of time. Meanwhile, we obtain subjective evaluation of usability and security by conducting a survey. By examining the responses, we found that the subjective evaluation confirms with the objective ones, and usability is in inverse relationship with security. We studied the consistency of gestures and found that most participants forgot gestures to some degree and reinforcing the memorization of gestures is necessary to improve the authentication performance. Finally, we performed a study with another 17 participants on shoulder surfing attacks, where attackers can observe the victims multiple times. The results show that shoulder surfing does not help to boost the attacks.

cs.CR↗

arXiv2018

Validating the Contextual Information of Outdoor Images for Photo Misuse Detection

The contextual information (i.e., the time and location) in which a photo is taken can be easily tampered with or falsely claimed by forgers to achieve malicious purposes, e.g., creating fear among the general public. A rich body of work has focused on detecting photo tampering and manipulation by verifying the integrity of image content. Instead, we aim to detect photo misuse by verifying the capture time and location of photos. This paper is motivated by the law of nature that sun position varies with the time and location, which can be used to determine whether the claimed contextual information corresponds with the sun position that the image content actually indicates. Prior approaches to inferring sun position from images mainly rely on vanishing points associated with at least two shadows, while we propose novel algorithms which utilize only one shadow in the image to infer the sun position. Meanwhile, we compute the sun position by applying astronomical algorithms which take as input the claimed capture time and location. Only when the two estimated sun positions are consistent can the claimed contextual information be genuine. We have developed a prototype called IMAGEGUARD. The experimental results show that our method can successfully estimate sun position and detect the time-location inconsistency with high accuracy. By setting the thresholds to be 9.4 degrees and 5 degrees for the sun position distance and the altitude angle distance, respectively, our system can correctly identify 91.5% of falsified photos with fake contextual information.

cs.CR↗