arXiv subjects

Jin Wang

Publications and source records attributed to Jin Wang.

At least 253 records · Page 14Linked to original sources

arXiv2024

Mitigating noise of residual electric fields for single Rydberg atoms with electron photodesorption

Rydberg atoms as versatile tools for quantum applications are extremely sensitive to electric fields. When utilizing these atoms, it becomes imperative to comprehensively characterize and mitigate any residual electric fields present in the environment. Particularly for single Rydberg atoms trapped in optical tweezers in a compact quartz vacuum cell, we have identified that a significant source of background electric fields originates from electrons bound to the cell surface. These electrons are generated by the 297-nm light used for single-photon Rydberg excitation. Furthermore, once the electrons are desorbed from the surface through exposure to ultraviolet light, the incoherent ground-Rydberg transition undergoes a transformation into coherent excitation, since the noise of residual electric fields are effectively mitigated. Our studies promote enhanced control and reliable performance of Rydberg atom-based systems, thereby paving the way for advancements in quantum information processing, the realization of high-fidelity quantum gates, and the development of precise quantum sensors.

physics.atom-ph↗

arXiv2024

Language models as master equation solvers

Master equations are of fundamental importance in modeling stochastic dynamical systems.However, solving master equations is challenging due to the exponential increase in the number of possible states or trajectories with the dimension of the state space. In this study, we propose repurposing language models as a machine learning approach to solve master equations. We design a prompt-based neural network to map rate parameters, initial conditions, and time values directly to the state joint probability distribution that exactly matches the input contexts. In this way, we approximate the solution of the master equation in its most general form. We train the network using the policy gradient algorithm within the reinforcement learning framework, with feedback rewards provided by a set of variational autoregressive models. By applying this approach to representative examples, we observe high accuracy for both multi-module and high-dimensional systems. The trained network also exhibits extrapolating ability, extending its predictability to unseen data. Our findings establish the connection between language models and master equations, highlighting the possibility of using a single pretrained large model to solve any master equation.

cs.LG↗

arXiv2024

Three-dimensional Hard X-ray Ptychographic Reflectometry Imaging on Extended Mesoscopic Surface Structures

Many nano and quantum devices, with their sizes often spanning from millimeters down to sub-nanometer, have intricate low-dimensional, non-uniform, or hierarchical structures on surfaces and interfaces. Since their functionalities are dependent on these structures, high-resolution surface-sensitive characterization becomes imperative to gain a comprehensive understanding of the function-structure relationship. We thus developed hard X-ray ptychographic reflectometry imaging, a new technique that merges the high-resolution two-dimensional imaging capabilities of hard X-ray ptychography for extended objects, with the high-resolution depth profiling capabilities of X-ray reflectivity for layered structures. The synergy of these two methods fully leverages both amplitude and phase information from ptychography reconstruction to not only reveal surface topography and localized structures such as shapes and electron densities, but also yields statistical details such as interfacial roughness that is not readily accessible through coherent imaging solely. The hard X-ray ptychographic reflectometry imaging is well-suited for three-dimensional imaging of mesoscopic samples, particularly those comprising planar or layered nanostructures on opaque supports, and could also offer a high-resolution surface metrology and defect analysis on semiconductor devices such as integrated nanocircuits and lithographic photomasks for microchip fabrications.

physics.optics↗

arXiv2024

Quantum information recovery from black hole with projective measurement

We studied the Hayden-Preskill thought experiment with the local projective measurement. Compared to the original model, the measurement is applied on the Hawking radiation that was emitted after throwing the quantum diary into the black hole. Within this setup, we explored various aspects of this model, including the information recovery from the black hole, the relation to the black hole final state proposal, the relation between the Yoshida-Kitaev protocol and Petz recovery map, the effects of the decoherence, and the quantum simulations of the decoding protocols. These aspects may provide us new insights into the non-perturbative nature of quantum black holes.

gr-qc↗

arXiv2024

Effective stick-slip parameter for structurally lubric 2D interface friction

The wear-free sliding of layers or flakes of graphene-like 2D materials, important in many experimental systems, may occur either smoothly or through stick-slip, depending on driving conditions, corrugation, twist angles, as well as edges and defects. No single parameter has been so far identified to discriminate a priori between the two sliding regimes. Such a parameter, $η$, does exist in the ideal (Prandtl-Tomlinson) problem of a point particle sliding across a 1D periodic lattice potential. In that case $η>1$ implies mechanical instability, generally leading to stick-slip, with $η= \frac{2π^2 U_0}{K_\mathrm{p} a^2}$, where $U_0$ is the potential magnitude, $a$ the lattice spacing, and $K_\mathrm{p}$ the pulling spring constant. Here we show, supported by a repertoire of graphene flake/graphene sliding simulations, that a similar stick-slip predictor $η_\mathrm{eff}$ can be defined with the same form but suitably defined $U_\mathrm{eff}$, $a_\mathrm{eff}$ and $K_\mathrm{eff}$. Remarkably, simulations show that $a_\mathrm{eff} = a$ of the substrate remains an excellent approximation, while $K_\mathrm{eff}$ is an effective stiffness parameter, combining equipment and internal elasticity. Only the effective energy barrier $U_\mathrm{eff}$ needs to be estimated in order to predict whether stick-slip sliding of a 2D island or extended layer is expected or not. In a misaligned defect-free circular graphene sliding island of contact area $A$, we show that $U_\mathrm{eff}$, whose magnitude for a micrometer size diameter is of order 1 eV, scales as $A^{1/4}$, thus increasing very gently with size. The PT-like parameter $η_\mathrm{eff}$ is therefore proposed as a valuable tool in 2D layer sliding.

cond-mat.mtrl-sci↗

arXiv2024

Observation of GRB 221009A early afterglow in X/$γ$-ray energy band

The early afterglow of a Gamma-ray burst (GRB) can provide critical information on the jet and progenitor of the GRB. The extreme brightness of GRB 221009A allows us to probe its early afterglow in unprecedented detail. In this letter, we report comprehensive observation results of the early afterglow of GRB 221009A (from $T_0$+660 s to $T_0$+1860 s, where $T_0$ is the \textit{Insight}-HXMT/HE trigger time) in X/$γ$-ray energy band (from 20 keV to 20 MeV) by \textit{Insight}-HXMT/HE, GECAM-C and \textit{Fermi}/GBM. We find that the spectrum of the early afterglow in 20 keV-20 MeV could be well described by a cutoff power-law with an extra power-law which dominates the low and high energy bands respectively. The cutoff power-law $E_{\rm peak}$ is $\sim$ 30 keV and the power-law photon index is $\sim$ 1.8 throughout the early afterglow phase. By fitting the light curves in different energy bands, we find that a significant achromatic break (from keV to TeV) is required at $T_0$ + 1246$^{+27}_{-26}$ s (i.e. 1021 s since the afterglow starting time $T_{\rm AG}$=$T_0$+225 s), providing compelling evidence of a jet break. Interestingly, both the pre-break and post-break decay slopes vary with energy, and these two slopes become closer in the lower energy band, making the break less identifiable. Intriguingly, the spectrum of the early afterglow experienced a slight hardening before the break and a softening after the break. These results provide new insights into the understanding of this remarkable GRB.

astro-ph.HE↗

arXiv2024

Information retrieval from Hawking radiation in the non-isometric model of black hole interior: theory and quantum simulations

The non-isometric holographic model of the black hole interior stands out as a potential resolution of the long-standing black hole information puzzle since it remedies the friction between the effective calculation and the microscopic description. In this study, combining the final-state projection model, the non-isometric model of black hole interior and Hayden-Preskill thought experiment, we investigate the information recovery from decoding Hawking radiation and demonstrate the emergence of the Page time in this setup. We incorporate the effective modes into the scrambling inside the horizon, which are usually disregarded in Hayden-Preskill protocols, and show that the Page time can be identified as the transition of information transmission channels from the EPR projection to the local projections. This offers a new perspective on the Page time. We compute the decoupling condition under which retrieving information is feasible and show that this model computes the black hole entropy consistent with the quantum extremal surface calculation. Assuming the full knowledge of the dynamics of the black hole interior, we show how Yoshida-Kitaev decoding strategy can be employed in the modified Hayden-Preskill protocol. Furthermore, we perform experimental tests of both probabilistic and Grover's search decoding strategies on the 7-qubit IBM quantum processors to validate our analytical findings and confirm the feasibility of retrieving information in the non-isometric model. This study would stimulate more interests to explore black hole information problem on the quantum processors.

hep-th↗

arXiv2023

High precision atom interferometer-based dynamic gravimeter measurement by eliminating the cross-coupling effect

A dynamic gravimeter with an atomic interferometer (AI) can perform absolute gravity measurements with high precision. AI-based dynamic gravity measurement is a type of joint measurement that uses AI sensors and a classical accelerometer. The coupling of the two sensors may degrade the measurement precision. In this study, we analyzed the cross-coupling effect and introduced a recovery vector to suppress this effect. We improved the phase noise of the interference fringe by a factor of 1.9 by performing marine gravity measurements using an AI-based gravimeter and optimizing the recovery vector. Marine gravity measurements were performed, and high gravity measurement precision was achieved. The external and inner coincidence accuracies of the gravity measurement are 0.42 mGal and 0.46 mGal, which were improved by factors of 4.18 and 4.21 by optimizing the cross-coupling effect.

physics.app-ph↗

arXiv2023

Page Time as a Transition of Information Channels: High-fidelity Information Retrieval for Radiating Black Holes

The effective field theory description of a radiating black hole introduces redundant degrees of freedom that necessitate annihilation of those modes at late stages to conserve entropy. The prevailing view is that such effective process can result in information loss unless the redundant states are annihilated in maximally entangled pairs, resembling quantum teleportation. In this Letter, we demonstrate that this view can be relaxed in a new postselection model. We investigate information recoverability in a radiating black hole through the non-unitary dynamics that projects the randomly-selected modes from a scrambling unitary. We show that the model has the merit of producing the von Neumann entropy of black holes consistent with the island formula calculation and that information in the black hole interior can be decoded from the Hawking radiation without loss after the Page time. Moreover, in this model the Page time gains a new interpretation as the transition point between two channels of information transmission when sufficient amounts of effective modes are annihilated inside the horizon. We present two decoding strategies along with their quantum circuit realizations. The experimental verification of the strategies employs 7-qubit IBM quantum processors, demonstrating the viability of these strategies and the potential for quantum processors to probe the black hole interior.

hep-th↗

arXiv2023

Rheological softening of metal nanocontacts sheared under oscillatory strains

The way metal interfaces evolve during frictional sliding, and how that evolution can be externally influenced under external drivers are important questions, hard to investigate experimentally because the contacts themselves are generally difficult to access. Here we focus on an elementary constituent of a general metal-metal interface, namely an ultra-thin individual nanocontact, where recent rheological studies of crystalline gold nanocontacts [Nature 569, 393 (2019)] showed a dramatic and unexpected mechanical softening as a result of external oscillatory tensile stress. The question which we address through realistic nonequilibrium molecular dynamics simulations is to what extent such mechanical softening might influence the shearing habit of gold nanocontacts at room temperature. It is found that the shearing evolution, which occurs through a series of discrete slips, is indeed rheologically softened, even though not completely, by the oscillations. Differences also emerge for different types of external oscillation, tensile or rotational. The relevance of these results for future experiments will be discussed.

cond-mat.mtrl-sci↗

arXiv2023

Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications

The multiple-choice knapsack problem (MCKP) is a classic NP-hard combinatorial optimization problem. Motivated by several significant real-world applications, this work investigates a novel variant of MCKP called chance-constrained multiple-choice knapsack problem (CCMCKP), where the item weights are random variables. In particular, we focus on the practical scenario of CCMCKP, where the probability distributions of random weights are unknown but only sample data is available. We first present the problem formulation of CCMCKP and then establish two benchmark sets. The first set contains synthetic instances and the second set is devised to simulate a real-world application scenario of a certain telecommunication company. To solve CCMCKP, we propose a data-driven adaptive local search (DDALS) algorithm. The main novelty of DDALS lies in its data-driven solution evaluation approach that can effectively handle unknown probability distributions of item weights. Moreover, in cases with unknown distributions, high intensity of chance constraints, limited amount of sample data and large-scale problems, it still exhibits good performance. Experimental results demonstrate the superiority of DDALS over other baselines on the two benchmarks. Additionally, ablation studies confirm the effectiveness of each component of the algorithm. Finally, DDALS can serve as the baseline for future research, and the benchmark sets are open-sourced to further promote research on this challenging problem.

cs.NE↗

arXiv2023

Open quantum system in the indefinite environment

In this paper, we investigate the interference engineering of the open quantum system, where the environment is made indefinite either through the use of an interferometer or the introduction of auxiliary qubits. The environments are modeled by fully connected qubit baths with exact analytical dynamics. As the system passes through the interferometer or is controlled by auxiliary qubits, it is propagated along different paths or their superpositions, leading to distinct interactions with the environment in each path. This results in the superposition of the environments, which can be detected through specific measurements that retain certain coherent information about the paths. Our results demonstrate that the indefiniteness of the environment can significantly enhance the quantum correlations. However, only the statistical mixture of the influences from the environments preserves provided that the path coherence is destructed. We also examine the serviceability of the indefiniteness as a resource for teleportation and quantum parameter estimation. Additionally, we discuss how to quantify the indefiniteness and the ways in which it affects the system's dynamics from the perspective of wave-particle-entanglement-ignorance complementarity. Overall, our study highlights the potential benefits of an indefinite environment in quantum information processing and sheds light on the fundamental principles underlying its effects.

quant-ph↗

arXiv2023

Kinetic Friction of Structurally Superlubric 2D Material Interfaces

The ultra-low kinetic friction F_k of 2D structurally superlubric interfaces, connected with the fast motion of the incommensurate moiré pattern, is often invoked for its linear increase with velocity v_0 and area A, but never seriously addressed and calculated so far. Here we do that, exemplifying with a twisted graphene layer sliding on top of bulk graphite -- a demonstration case that could easily be generalized to other systems. Neglecting quantum effects and assuming a classical Langevin dynamics, we derive friction expressions valid in two temperature regimes. At low temperatures the nonzero sliding friction velocity derivative dF_k/dv_0 is shown by Adelman-Doll-Kantorovich type approximations to be equivalent to that of a bilayer whose substrate is affected by an analytically derived effective damping parameter, replacing the semi-infinite substrate. At high temperatures, friction grows proportional to temperature as analytically required by fluctuation-dissipation. The theory is validated by non-equilibrium molecular dynamics simulations with different contact areas, velocities, twist angles and temperatures. Using 6^{\circ}-twisted graphene on Bernal graphite as a prototype we find a shear stress of measurable magnitude, from 25 kPa at low temperature to 260 kPa at room temperature, yet only at high sliding velocities such as 100 m/s. However, it will linearly drop many orders of magnitude below measurable values at common experimental velocities such as 1 μm/s, a factor 10^{-8} lower. The low but not ultra-low "engineering superlubric" friction measured in existing experiments should therefore be attributed to defects and/or edges, whose contribution surpasses by far the negligible moiré contribution.

cond-mat.mtrl-sci↗

arXiv2023

Bending Stiffness Collapse, Buckling, Topological Bands of Freestanding Twisted Bilayer Graphene

The freestanding twisted bilayer graphene (TBG) is unstable, below a critical twist angle θ_c~3.7 degrees, against a moire (2 \times 1) buckling distortion at T=0. Realistic simulations reveal the concurrent unexpected collapse of the bending rigidity, an unrelated macroscopic mechanical parameter. An analytical model connects bending and buckling anomalies at T=0, but as temperature rises the former fades, while buckling persists further. The (2 \times 1) electronic properties are also surprising. The magic twist angle narrow bands, now eight in number, fail to show zone boundary splittings despite the new periodicity. Symmetry shows how this is dictated by an effective single valley physics. These structural, critical, and electronic predictions promise to make the freestanding state of TBG especially interesting.

cond-mat.mes-hall↗

arXiv2023

RecAGT: Shard Testable Codes with Adaptive Group Testing for Malicious Nodes Identification in Sharding Permissioned Blockchain

Recently, permissioned blockchain has been extensively explored in various fields, such as asset management, supply chain, healthcare, and many others. Many scholars are dedicated to improving its verifiability, scalability, and performance based on sharding techniques, including grouping nodes and handling cross-shard transactions. However, they ignore the node vulnerability problem, i.e., there is no guarantee that nodes will not be maliciously controlled throughout their life cycle. Facing this challenge, we propose RecAGT, a novel identification scheme aimed at reducing communication overhead and identifying potential malicious nodes. First, shard testable codes are designed to encode the original data in case of a leak of confidential data. Second, a new identity proof protocol is presented as evidence against malicious behavior. Finally, adaptive group testing is chosen to identify malicious nodes. Notably, our work focuses on the internal operation within the committee and can thus be applied to any sharding permissioned blockchains. Simulation results show that our proposed scheme can effectively identify malicious nodes with low communication and computational costs.

cs.CR↗

arXiv2023

UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving

Multi-camera setups find widespread use across various applications, such as autonomous driving, as they greatly expand sensing capabilities. Despite the fast development of Neural radiance field (NeRF) techniques and their wide applications in both indoor and outdoor scenes, applying NeRF to multi-camera systems remains very challenging. This is primarily due to the inherent under-calibration issues in multi-camera setup, including inconsistent imaging effects stemming from separately calibrated image signal processing units in diverse cameras, and system errors arising from mechanical vibrations during driving that affect relative camera poses. In this paper, we present UC-NeRF, a novel method tailored for novel view synthesis in under-calibrated multi-view camera systems. Firstly, we propose a layer-based color correction to rectify the color inconsistency in different image regions. Second, we propose virtual warping to generate more viewpoint-diverse but color-consistent virtual views for color correction and 3D recovery. Finally, a spatiotemporally constrained pose refinement is designed for more robust and accurate pose calibration in multi-camera systems. Our method not only achieves state-of-the-art performance of novel view synthesis in multi-camera setups, but also effectively facilitates depth estimation in large-scale outdoor scenes with the synthesized novel views.

cs.CV↗

arXiv2023

Generalized Volume Complexity in Gauss-Bonnet Gravity: Constraints and Phase Transitions

It has been proposed that quantum complexity is dual to the volume of the extremal surface, the action of the Wheeler-DeWitt patch, and the spacetime volume of the patch. Recently, a generalized volume-complexity observable was formulated as an equivalently good candidate for the dual holographic complexity. This proposal is abbreviated as ``complexity=anything." This proposal offers greater flexibility in selecting extremal surfaces and evaluating physical quantities, e.g., volume or action, on these surfaces. In this study, we explore the 'complexity=anything' proposal for Gauss-Bonnet black holes in asymptotic anti-de Sitter space in various dimensions. We demonstrate that this proposal guarantees the linear growth of the generalized volume at late times, regardless of the coupling parameters for four-dimensional Gauss-Bonnet gravity. However, this universality does not hold for higher dimensions. Moreover, discontinuous deformations of the extremal surfaces emerge when multiple peaks exist in the effective potential, which is reminiscent of a phase transition. Additionally, we present constraints on the coupling parameters of five-dimensional models in order for the generalized volume to be a viable candidate for holographic complexity.

hep-th↗

arXiv2023

Quantum learning and essential cognition under the traction of meta-characteristics in an open world

Artificial intelligence has made significant progress in the Close World problem, being able to accurately recognize old knowledge through training and classification. However, AI faces significant challenges in the Open World problem, as it involves a new and unknown exploration journey. AI is not inherently proactive in exploration, and its challenge lies in not knowing how to approach and adapt to the unknown world. How do humans acquire knowledge of the unknown world. Humans identify new knowledge through intrinsic cognition. In the process of recognizing new colors, the cognitive cues are different from known color features and involve hue, saturation, brightness, and other characteristics. When AI encounters objects with different features in the new world, it faces another challenge: where are the distinguishing features between influential features of new and old objects? AI often mistakes a new world's brown bear for a known dog because it has not learned the differences in feature distributions between knowledge systems. This is because things in the new and old worlds have different units and dimensions for their features. This paper proposes an open-world model and elemental feature system that focuses on fundamentally recognizing the distribution differences in objective features between the new and old worlds. The quantum tunneling effect of learning ability in the new and old worlds is realized through the tractive force of meta-characteristic. The outstanding performance of the model system in learning new knowledge (using pedestrian re-identification datasets as an example) demonstrates that AI has acquired the ability to recognize the new world with an accuracy of $96.71\%$ at most and has gained the capability to explore new knowledge, similar to humans.

cs.AI↗