Search

We investigate the enhancement of quantum phase synchronization in a two-level system (TLS) coupled to a squeezed reservoir. Our study reveals that the squeezed reservoir induces a stable limit cycle in the TLS, enhancing the quantum phase synchronization. We utilize the Husimi $Q$-function to describe the phase portrait of the driven TLS, and the $S$-function to quantitatively illustrate the effects of signal strength and detuning on phase synchronization. Remarkably, we demonstrate that the squeezed reservoir imparts its squeezing characteristics to the TLS, leading to a more localized and pronounced synchronization. Additionally, we observe typical features of the Arnold tongue in the synchronization regions. The experimental feasibility of our findings is discussed in the context of a circuit QED system, suggesting that squeezed-reservoir engineering is an effective approach for achieving quantum phase synchronization., Comment: 6 pages,4 figures, comments are welcome!

We present our optical observations and multi-wavelength analysis of the GRB\,200613A detected by \texttt{Fermi} satellite. Time-resolved spectral analysis of the prompt $\gamma$-ray emission was conducted utilizing the Bayesian block method to determine statistically optimal time bins. Based on the Bayesian Information Criterion (BIC), the data generally favor the Band+Blackbody (short as BB) model. We speculate that the main Band component comes from the Blandford-Znajek mechanism, while the additional BB component comes from the neutrino annihilation process. The BB component becomes significant for a low-spin, high-accretion rate black hole central engine, as evidenced by our model comparison with the data. The afterglow light curve exhibits typical power-law decay, and its behavior can be explained by the collision between the ejecta and constant interstellar medium (ISM). Model fitting yields the following parameters: $E_{K,iso} = (2.04^{+11.8}_{-1.50})\times 10^{53}$ erg, $\Gamma_0=354^{+578}_{-217}$, $p=2.09^{+0.02}_{-0.03}$, $n_{18}=(2.04^{+9.71}_{-1.87})\times 10^{2}$ cm$^{-3}$, $\theta_j=24.0^{+6.50}_{-5.54}$ degree, $\epsilon_e=1.66^{+4.09}_{-1.39})\times 10^{-1}$ and $\epsilon_B=(7.76^{+48.5}_{-5.9})\times 10^{-6}$. In addition, we employed the public Python package \texttt{Prospector} perform a spectral energy distribution (SED) modeling of the host galaxy. The results suggest that the host galaxy is a massive galaxy ($\log(M_\ast / M_\odot)=11.75^{+0.10}_{-0.09}$) with moderate star formation rate ($\mbox{SFR}=22.58^{+13.63}_{-7.22} M_{\odot}$/yr). This SFR is consistent with the SFR of $\sim 34.2 M_{\odot}$ yr$^{-1}$ derived from the [OII] emission line in the observed spectrum., Comment: 30 pages, 16 figures, accepted by ApJ

Quantum entanglement is a crucial resource in quantum information processing, and protecting it against noise poses a significant challenge. This paper introduces two strategies for preserving qutrit-qutrit entanglement in the presence of correlated amplitude damping (CAD) noise: weak measurement (WM) and environment-assisted measurement (EAM), both combined with quantum measurement reversal (QMR). Two prototypical classes of three-dimensional entangled states are examined. The findings demonstrate that while the WM+QMR method can partially retain entanglement, the EAM+QMR approach is more effective at protecting entanglement as well as enhancing success probabilities, particularly for specific qutrit-qutrit entangled states. Additionally, we thoroughly discuss the impact of correlation effects on entanglement protection and the enhancement of success probability. Our results provide valuable insights into defending high-dimensional entanglement from CAD noise, thus offering practical solutions for the advancement of quantum information technologies., Comment: 14 pages,, 6 figures, comments are welcome!

A circulator is one of the crucial devices in quantum networks and simulations. We propose a four-port circulator that regulate the flow of single photons at muti-frequency points by studying the coherent transmission of a single photon in a coupled system of two resonators and two waveguides. When both resonators are static or rotate at the same angular velocity, single-photon transport demonstrates reciprocity; however, when the angular velocities differ, four distinct frequency points emerge where photon circulation can occur. In particular, when the angular velocities of the two resonators are equal and opposite, there are two different frequency points where photon circulation can be achieved, and there is a frequency point where a single photon input from any waveguide can be completely routed to the other waveguide. Interestingly, by rotating the two resonators, the single-photon circulation suppressed by the internal defect-induced backscattering can be restored., Comment: 12 pages, 5 figures

Cavity magnomechanics, exhibiting remarkable experimental tunability, rich magnonic nonlinearities, and compatibility with various quantum systems, has witnessed considerable advances in recent years. However, the potential benefits of using cavity magnomechanical (CMM) systems in further improving the performance of quantum-enhanced sensing for weak forces remain largely unexplored. Here we show that the performance of a quantum CMM sensor can be significantly enhanced beyond the standard quantum limit (SQL), by squeezing the magnons. We find that, for comparable parameters, two orders of enhancement in force sensitivity can be achieved in comparison with the case without the magnon squeezing. Moreover, we show optimal parameter regimes of homodyne angle for minimizing added quantum noise. Our findings provide a promising approach for highly tunable and compatible quantum force sensing using hybrid CMM devices, with potential applications ranging from quantum precision measurements to quantum information processing.

Cavity optomechanical (COM) sensors, featuring efficient light-motion couplings, have been widely used for ultra sensitive measurements of various physical quantities ranging from displacements to accelerations or weak forces. Previous works, however, have mainly focused on reciprocal COM systems. Here, we propose how to further improve the performance of quantum COM sensors by breaking reciprocal symmetry in purely quantum regime. Specifically, we consider a spinning COM resonator and show that by selectively driving it in opposite directions, highly nonreciprocal optical squeezing can emerge, which in turn provides an efficient way to surpass the standard quantum limit that otherwise exists in conventional reciprocal devices. Our work confirms that breaking reciprocal symmetry, already achieved in diverse systems well beyond spinning systems, can serve as a new strategy to further enhance the abilities of advanced quantum sensors, for applications ranging from testing fundamental physical laws to practical quantum metrology., Comment: 7 pages,3 figures

We propose to achieve a multi-color nonreciprocal optical amplifier, a crucial device in optical communication and information processing, by spinning an active resonator. We show that in such a device, due to the interplay of the Sagnac effect and the optical gain, nonreciprocal signal {amplification} can be realized, accompanied by a giant enhancement of optical group delay from $0.3\;\mathrm{ms}$ to $35\;\mathrm{ms}$ in a chosen direction, which is otherwise unattainable in a passive device. Also, coherent amplification of higher-order optical sidebands and a slow-to-fast light switch can be achieved by tuning both the pump power and the spinning velocity. Our work provides a unique and accessible way, well-compatible with other existing techniques, to realize multi-color nonreciprocal optical amplifiers for more flexible control of optical fields., Comment: 8pages, 4 figures

Phonon lasers or coherent amplifications of mechanical oscillations have provided powerful tools for both fundamental studies of coherent acoustics and diverse applications ranging from ultrasensitive force sensing to phononic information processing. Here, we propose how to achieve directional phonon lasing with an optomechanical resonator coupled to a nonlinear optical resonator. We find that, by pumping the nonlinear resonator, directional optical squeezing can occur along the pump direction. As a result, we can achieve the directional mechanical gain by utilizing the directional optical squeezing, thus leading to nonreciprocal phonon lasing with a well-tunable directional power threshold. Our work shows a feasible way to build nonreciprocal phonon lasers with various nonlinear optical mediums, which are important for such a wide range of applications as directional acoustic amplifiers, invisible sound sensing or imaging, and one-way phononic networks., Comment: 11 pages, 4 figures

High-dimensional teleportation provides various benefits in quantum networks and repeaters, but all these advantages rely on the high-quality distribution of high-dimensional entanglement over a noisy channel. It is essential to consider correlation effects when two entangled qutrits travel consecutively through the same channel. In this paper, we present two strategies for enhancing qutrit teleportation in correlated amplitude damping (CAD) noise by weak measurement (WM) and environment-assisted measurement (EAM). The fidelity of both approaches has been dramatically improved due to the probabilistic nature of WM and EAM. We have observed that the correlation effects of CAD noise result in an increase in the probability of success. A comparison has demonstrated that the EAM scheme consistently outperforms the WM scheme in regard to fidelity. Our research expands the capabilities of WM and EAM as quantum techniques to combat CAD noise in qutrit teleportation, facilitating the development of advanced quantum technologies in high-dimensional systems., Comment: 18 pages, 5 figures. The figure 1 is replaced

FinTech lending (e.g., micro-lending) has played a significant role in facilitating financial inclusion. It has reduced processing times and costs, enhanced the user experience, and made it possible for people to obtain loans who may not have qualified for credit from traditional lenders. However, there are concerns about the potentially biased algorithmic decision-making during loan screening. Machine learning algorithms used to evaluate credit quality can be influenced by representation bias in the training data, as we only have access to the default outcome labels of approved loan applications, for which the borrowers' socioeconomic characteristics are better than those of rejected ones. In this case, the model trained on the labeled data performs well on the historically approved population, but does not generalize well to borrowers of low socioeconomic background. In this paper, we investigate the problem of representation bias in loan screening for a real-world FinTech lending platform. We propose a new Transformer-based sequential loan screening model with self-supervised contrastive learning and domain adaptation to tackle this challenging issue. We use contrastive learning to train our feature extractor on unapproved (unlabeled) loan applications and use domain adaptation to generalize the performance of our label predictor. We demonstrate the effectiveness of our model through extensive experimentation in the real-world micro-lending setting. Our results show that our model significantly promotes the inclusiveness of funding decisions, while also improving loan screening accuracy and profit by 7.10% and 8.95%, respectively. We also show that incorporating the test data into contrastive learning and domain adaptation and labeling a small ratio of test data can further boost model performance.

Two-dimensional material-based field effect transistors (2DM-FETs) are playing a revolutionary role in electronic devices. However, after years of development, no device model can match the Pao-Sah model for standard silicon-based transistors in terms of physical accuracy and computational efficiency to support large-scale integrated circuit design. One remaining critical obstacle is the contacts of 2DM-FETs. In order to self-consistently include the contact effect in the current model, it is necessary to perform self-consistent calculations, which is a fatal flaw for applications that prioritize efficiency. Here, we report that the Landauer-QFLPS model effectively overcomes the above contradiction, where QFLPS means quasi-Fermi-level phase space theory. By connecting the physical pictures of the contact and the intrinsic channel part, we have successfully derived a drain-source current formula including the contact effect. To verify the model, we prepared transistors based on two typical 2DMs, black phosphorus (BP) and molybdenum disulfide (MoS2), the former having ambipolar transport and the latter showing electron-dominant unipolar transport. The proposed new formula could describe both 2DM-FETs with Schottky or Ohmic contacts. Moreover, compared with traditional methods, the proposed model has the advantages of accuracy and efficiency, especially in describing non-monotonic drain conductance characteristics, because the contact effect is self-consistently and compactly packaged as an exponential term. More importantly, we also examined the model at the circuit level. Here, we fabricated a three-bit threshold inverter quantizer circuit based on ambipolar-BP process and experimentally demonstrated that the model can accurately predict the circuit performance. This industry-benign 2DM-FET model is supposed to be very useful for the development of 2DM-FET-based integrated circuits.

High-redshift gamma-ray bursts (GRBs) provide a powerful tool to probe the early universe, but still for relatively few do we have good observations of the afterglow. We here report the optical and near-infrared observations of the afterglow of a relatively high-redshift event, GRB\,220101A, triggered on New Year's Day of 2022. With the optical spectra obtained at XL2.16/BFOSC and NOT/ALFOSC, we determine the redshift of the burst at $z= 4.615$. Based on our optical and near-infrared data, combined with the X-ray data, we perform multiband fit with the python package \emph{afterglowpy}. A jet-break at $\sim$ 0.7 day post-burst is found to constrain the opening angle of the jet as $\sim$ 3.4 degree. We also determine circumburst density of $n_0 = 0.15\ {\rm cm}^{-3}$ as well as kinetic energy $E_{\rm K, iso} = 3.52\times 10^{54}$ erg. The optical afterglow is among the most luminous ever detected. We also find a ``mirror'' feature in the lightcurve during the prompt phase of the burst from 80 s to 120 s. The physical origin of such mirror feature is unclear., Comment: 15 pages, 10 figures, submitted to ApJ

Non-Hermitian systems with anti-parity-time ($\mathcal{APT}$) symmetry have revealed rich physics beyond conventional systems. Here, we study optomechanics in an $\mathcal{APT}$-symmetric spinning resonator and show that, by tuning the rotating speed to approach the exceptional point (EP) or the non-Hermitian spectral degeneracy, nonreciprocal light transmission with a high isolation ratio can be realized. Accompanying this process, nonreciprocal group delay or advance is also identified in the vicinity of EP. Our work sheds new light on manipulating laser propagation with optomechanical EP devices and, in a broader view, can be extended to explore a wide range of $\mathcal{APT}$-symmetric effects, such as $\mathcal{APT}$-symmetric phonon lasers, $\mathcal{APT}$-symmetric topological effects, and $\mathcal{APT}$-symmetric force sensing or accelerator., Comment: 9 pages, 4 figures. It has been accepted for publication as a Regular Article in Physical Review A

Early optical observations of gamma-ray bursts can significantly contribute to the study of the central engine and physical processes therein. However, of the thousands observed so far, still only a few have data at optical wavelengths in the first minutes after the onset of the prompt emission. Here we report on GRB\,190106A, whose afterglow was observed in optical bands just 36 s after the {\em Swift}/BAT trigger, i.e., during the prompt emission phase. The early optical afterglow exhibits a bimodal structure followed by a normal decay, with a faster decay after $\sim \rm T_{0}+$1 day. We present optical photometric and spectroscopic observations of GRB\,190106A. We derive the redshift via metal absorption lines from Xinglong 2.16-m/BFOSC spectroscopic observations. From the BFOSC spectrum, we measure $z= 1.861\pm0.002$. The double-peak optical light curve is a significant feature predicted by the reverse-forward external shock model. The shallow decay followed by a normal decay in both the X-ray and optical light curves is well explained with the standard forward-shock model with late-time energy injection. Therefore, GRB\,190106A offers a case study for GRBs emission from both reverse and forward shocks., Comment: 17 pages, 9 figures. Accepted for publication in ApJ

We study the quantum phase synchronization of a driven two-level system (TLS) coupled to a structured environment and demonstrate that quantum synchronization can be enhanced by the classical driving field. We use the Husimi $Q$-function to characterize the phase preference and find the in-phase and anti-phase locking phenomenon in the phase diagram. Remarkably, we show that the in-phase classical driving enables a TLS to reach stable anti-phase locking in the Markovian regime. However, we find that the synergistic action of classical driving and non-Markovian effects significantly enhances the initial in-phase locking. By introducing the $S$-function and its maximal value to quantify the strength of synchronization and sketch the synchronization regions, we observe the typical signatures of the hollowed Arnold tongue in the parameter regions of synchronization. In the hollowed Arnold tongue, the synchronization regions exist both inside and outside the tongue while unsynchronized regions only lie on the boundary line. We also provide an intuitive interpretation of the above results by using the quasimode theory., Comment: minor version, accepted by Physical Review A

Risk scoring systems have been widely deployed in many applications, which assign risk scores to users according to their behavior sequences. Though many deep learning methods with sophisticated designs have achieved promising results, the black-box nature hinders their applications due to fairness, explainability, and compliance consideration. Rule-based systems are considered reliable in these sensitive scenarios. However, building a rule system is labor-intensive. Experts need to find informative statistics from user behavior sequences, design rules based on statistics and assign weights to each rule. In this paper, we bridge the gap between effective but black-box models and transparent rule models. We propose a two-stage method, RuDi, that distills the knowledge of black-box teacher models into rule-based student models. We design a Monte Carlo tree search-based statistics generation method that can provide a set of informative statistics in the first stage. Then statistics are composed into logical rules with our proposed neural logical networks by mimicking the outputs of teacher models. We evaluate RuDi on three real-world public datasets and an industrial dataset to demonstrate its effectiveness., Comment: CIKM'2022. Codes: https://github.com/yzhang1918/cikm2022rudi

We develop a structural econometric model to capture the decision dynamics of human evaluators on an online micro-lending platform, and estimate the model parameters using a real-world dataset. We find two types of biases in gender, preference-based bias and belief-based bias, are present in human evaluators' decisions. Both types of biases are in favor of female applicants. Through counterfactual simulations, we quantify the effect of gender bias on loan granting outcomes and the welfare of the company and the borrowers. Our results imply that both the existence of the preference-based bias and that of the belief-based bias reduce the company's profits. When the preference-based bias is removed, the company earns more profits. When the belief-based bias is removed, the company's profits also increase. Both increases result from raising the approval probability for borrowers, especially male borrowers, who eventually pay back loans. For borrowers, the elimination of either bias decreases the gender gap of the true positive rates in the credit risk evaluation. We also train machine learning algorithms on both the real-world data and the data from the counterfactual simulations. We compare the decisions made by those algorithms to see how evaluators' biases are inherited by the algorithms and reflected in machine-based decisions. We find that machine learning algorithms can mitigate both the preference-based bias and the belief-based bias., Comment: accepted by KDD 2021, MLCM workshop

The prosperity of mobile and financial technologies has bred and expanded various kinds of financial products to a broader scope of people, which contributes to advocating financial inclusion. It has non-trivial social benefits of diminishing financial inequality. However, the technical challenges in individual financial risk evaluation caused by the distinct characteristic distribution and limited credit history of new users, as well as the inexperience of newly-entered companies in handling complex data and obtaining accurate labels, impede further promoting financial inclusion. To tackle these challenges, this paper develops a novel transfer learning algorithm (i.e., TransBoost) that combines the merits of tree-based models and kernel methods. The TransBoost is designed with a parallel tree structure and efficient weights updating mechanism with theoretical guarantee, which enables it to excel in tackling real-world data with high dimensional features and sparsity in $O(n)$ time complexity. We conduct extensive experiments on two public datasets and a unique large-scale dataset from Tencent Mobile Payment. The results show that the TransBoost outperforms other state-of-the-art benchmark transfer learning algorithms in terms of prediction accuracy with superior efficiency, shows stronger robustness to data sparsity, and provides meaningful model interpretation. Besides, given a financial risk level, the TransBoost enables financial service providers to serve the largest number of users including those who would otherwise be excluded by other algorithms. That is, the TransBoost improves financial inclusion., Comment: Accepted at AAAI-22