Neutral atom platform has become an attractive choice to study the science of quantum information and quantum simulation, where intense efforts have been devoted to the entangling processes between individual atoms. For the development of this area,
two-qubit controlled-PHASE gate via Rydberg blockade is one of the most essential elements. Recent theoretical studies have suggested the advantages of introducing non-trivial waveform modulation into the gate protocol, which is anticipated to improve its performance towards the next stage. We report our recent experimental results in realizing a two-qubit controlled-PHASE($C_Z$) gate via off-resonant modulated driving(ORMD) embedded in two-photon transition for Rb atoms. It relies upon a single modulated driving pulse with a carefully calculated smooth waveform to gain the appropriate phase accumulations required by the two-qubit gate. Combining this $C_Z$ gate with global microwave pulses, two-atom entanglement is generated with the raw fidelity of 0.945(6). Accounting for state preparation and measurement (SPAM) errors, we extract the entanglement operation fidelity to be 0.980(7). Our work features completing the $C_Z$ gate operation within a single pulse to avoid shelved Rydberg population, thus demonstrate another promising route for realizing high-fidelity two-qubit gate for neutral atom platform.
We focus on three scalar-field dark energy models (i.e., $phi$CDM models), which behave like cosmological trackers with potentials $V(phi)propto phi^{-alpha}$ (inverse power-law (IPL) model), $V(phi)propto coth^{alpha}{phi}$ (L-model) and $V(phi)prop
to cosh(alphaphi)$ (Oscillatory tracker model). The three $phi$CDM models, which reduce to the $Lambda$CDM model with the parameter $alpha to 0$, are investigated and compared with the recent observations of type Ia supernovae (SNe Ia), baryon acoustic oscillations (BAO) and cosmic microwave background radiation (CMB). The observational constraints from the combining sample (SNe Ia + BAO + CMB) indicate that none of the three $phi$CDM models exclude the $Lambda$CDM model at $68.3%$ confidence level, and a closed universe is strongly supported in the scenarios of the three $phi$CDM models (at 68.3% confidence level). Furthermore, we apply the Bayesian evidence to compare the $phi$CDM models and the $Lambda$CDM model with the analysis of the combining sample. The concordance $Lambda$CDM model is still the most supported one. In addition, among the three $phi$CDM models, the IPL model is the most competitive one, while the L-model/Oscillatory tacker model is moderately/strongly disfavored.
Considering the importance of building a good Visual Dialog (VD) Questioner, many researchers study the topic under a Q-Bot-A-Bot image-guessing game setting, where the Questioner needs to raise a series of questions to collect information of an undi
sclosed image. Despite progress has been made in Supervised Learning (SL) and Reinforcement Learning (RL), issues still exist. Firstly, previous methods do not provide explicit and effective guidance for Questioner to generate visually related and informative questions. Secondly, the effect of RL is hampered by an incompetent component, i.e., the Guesser, who makes image predictions based on the generated dialogs and assigns rewards accordingly. To enhance VD Questioner: 1) we propose a Related entity enhanced Questioner (ReeQ) that generates questions under the guidance of related entities and learns entity-based questioning strategy from human dialogs; 2) we propose an Augmented Guesser (AugG) that is strong and is optimized for the VD setting especially. Experimental results on the VisDial v1.0 dataset show that our approach achieves state-of-theart performance on both image-guessing task and question diversity. Human study further proves that our model generates more visually related, informative and coherent questions.
We propose a generalized Mathieu equation (GME) which describes well the dynamics for two different models in spin-1 Bose-Einstein condensates. The stability chart of this GME differs significantly from that of Mathieus equation and the unstable dyna
mics under this GME is called generalized parametric resonance. A typical region of $epsilon gtrsim 1$ and $delta approx 0.25$ can be used to distinguish these two equations. The GME we propose not only explains the experimental results of Hoang et al. [Nat. Commun. 7, 11233 (2016)] in nematic space with a small driving strength, but predicts the behavior in the regime of large driving strength. In addition, the model in spin space we propose, whose dynamics also obeys this GME, can be well-tuned such that it is easily implemented in experiments.
We present for the first time the timing and spectral analyses for a narrow-line Seyfert 1 galaxy, SBS 1353+564, using it{XMM-Newton} and it{Swift} multi-band observations from 2007 to 2019. Our main results are as follows: 1) The temporal variabilit
y of SBS 1353+564 is random, while the hardness ratio is relatively constant over a time span of 13 years; 2) We find a prominent soft X-ray excess feature below 2 keV, which cannot be well described by a simple blackbody component; 3) After comparing the two most prevailing models for interpreting the origin of the soft X-ray excess, we find that the relativistically smeared reflection model is unable to fit the data above 5 keV well and the X-ray spectra do not show any reflection features, such as the Fe Kalpha emission line. However, the warm corona model can obtain a good fitting result. For the warm corona model, we try to use three different sets of spin values to fit the data and derive different best-fitting parameter sets; 4) We compare the UV/optical spectral data with the extrapolated values of the warm corona model to determine which spin value is more appropriate for this source, and we find that the warm corona model with non-spin can sufficiently account for the soft X-ray excess in SBS 1353+564.
The current state-of-the-art generative models for open-domain question answering (ODQA) have focused on generating direct answers from unstructured textual information. However, a large amount of worlds knowledge is stored in structured databases, a
nd need to be accessed using query languages such as SQL. Furthermore, query languages can answer questions that require complex reasoning, as well as offering full explainability. In this paper, we propose a hybrid framework that takes both textual and tabular evidence as input and generates either direct answers or SQL queries depending on which form could better answer the question. The generated SQL queries can then be executed on the associated databases to obtain the final answers. To the best of our knowledge, this is the first paper that applies Text2SQL to ODQA tasks. Empirically, we demonstrate that on several ODQA datasets, the hybrid methods consistently outperforms the baseline models that only take homogeneous input by a large margin. Specifically we achieve state-of-the-art performance on OpenSQuAD dataset using a T5-base model. In a detailed analysis, we demonstrate that the being able to generate structural SQL queries can always bring gains, especially for those questions that requires complex reasoning.
Quantum networks are promising tools for the implementation of long-range quantum communication. The characterization of quantum correlations in networks and their usefulness for information processing is therefore central for the progress of the fie
ld, but so far only results for small basic network structures or pure quantum states are known. Here we show that symmetries provide a versatile tool for the analysis of correlations in quantum networks. We provide an analytical approach to characterize correlations in large network structures with arbitrary topologies. As examples, we show that entangled quantum states with a bosonic or fermionic symmetry can not be generated in networks; moreover, cluster and graph states are not accessible. Our methods can be used to design certification methods for the functionality of specific links in a network and have implications for the design of future network structures.
To encourage AI agents to conduct meaningful Visual Dialogue (VD), the use of Reinforcement Learning has been proven potential. In Reinforcement Learning, it is crucial to represent states and assign rewards based on the action-caused transitions of
states. However, the state representation in previous Visual Dialogue works uses the textual information only and its transitions are implicit. In this paper, we propose Explicit Concerning States (ECS) to represent what visual contents are concerned at each round and what have been concerned throughout the Visual Dialogue. ECS is modeled from multimodal information and is represented explicitly. Based on ECS, we formulate two intuitive and interpretable rewards to encourage the Visual Dialogue agents to converse on diverse and informative visual information. Experimental results on the VisDial v1.0 dataset show our method enables the Visual Dialogue agents to generate more visual coherent, less repetitive and more visual informative dialogues compared with previous methods, according to multiple automatic metrics, human study and qualitative analysis.
We propose an efficient method for non-rigid surface tracking from monocular RGB videos. Given a video and a template mesh, our algorithm sequentially registers the template non-rigidly to each frame. We formulate the per-frame registration as an opt
imization problem that includes a novel texture term specifically tailored towards tracking objects with uniform texture but fine-scale structure, such as the regular micro-structural patterns of fabric. Our texture term exploits the orientation information in the micro-structures of the objects, e.g., the yarn patterns of fabrics. This enables us to accurately track uniformly colored materials that have these high frequency micro-structures, for which traditional photometric terms are usually less effective. The results demonstrate the effectiveness of our method on both general textured non-rigid objects and monochromatic fabrics.
The Floquet Hamiltonian has often been used to describe a time-periodic system. Nevertheless, because the Floquet Hamiltonian depends on a micro-motion parameter, the Floquet Hamiltonian with a fixed micro-motion parameter cannot faithfully represent
a driven system, which manifests as the anomalous edge states. Here we show that an accurate description of a Floquet system requires a set of Hamiltonian exhausting all values of the micro-motion parameter, and this micro-motion parameter can be viewed as an extra synthetic dimension of the system. Therefore, we show that a $d$-dimensional Floquet system can be described by a $d+1$-dimensional static Hamiltonian, and the advantage of this representation is that the periodic boundary condition is automatically imposed along the extra-dimension, which enables a straightforward definition of topological invariants. The topological invariant in the $d+1$-dimensional system can ensure a $d-1$-dimensional edge state of the $d$-dimensional Floquet system. Here we show two examples where the topological invariant is a three-dimensional Hopf invariant. We highlight that our scheme of classifying Floquet topology on the micro-motion space is different from the previous classification of Floquet topology on the time space.
