arXiv subjects

Bo Lu

Publications and source records attributed to Bo Lu.

At least 37 records · Page 2Linked to original sources

arXiv2025

A machine learning model for skillful climate system prediction

Climate system models (CSMs), through integrating cross-sphere interactions among the atmosphere, ocean, land, and cryosphere, have emerged as pivotal tools for deciphering climate dynamics and improving forecasting capabilities. Recent breakthroughs in artificial intelligence (AI)-driven meteorological modeling have demonstrated remarkable success in single-sphere systems and partially spheres coupled systems. However, the development of a fully coupled AI-based climate system model encompassing atmosphere-ocean-land-sea ice interactions has remained an unresolved challenge. This paper introduces FengShun-CSM, an AI-based CSM model that provides 60-day global daily forecasts for 29 critical variables across atmospheric, oceanic, terrestrial, and cryospheric domains. The model significantly outperforms the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal-to-seasonal (S2S) model in predicting most variables, particularly precipitation, land surface, and oceanic components. This enhanced capability is primarily attributed to its improved representation of intra-seasonal variability modes, most notably the Madden-Julian Oscillation (MJO). Remarkably, FengShun-CSM exhibits substantial potential in predicting subseasonal extreme events. Such breakthroughs will advance its applications in meteorological disaster mitigation, marine ecosystem conservation, and agricultural productivity enhancement. Furthermore, it validates the feasibility of developing AI-powered CSMs through machine learning technologies, establishing a transformative paradigm for next-generation Earth system modeling.

cs.LG↗

arXiv2025

Classification of pair symmetries in superconductors with unconventional magnetism

We consider unconventional magnets with proximity-induced superconductivity and investigate the emergence of superconducting correlations by carrying out a full classification of allowed Cooper pair symmetries. In particular, we focus on $d$-wave altermagnets and $p$-wave magnets under the influence of spin-singlet and spin-triplet superconductivity induced by proximity effect. Under generic conditions, we find that unconventional magnets not only drive a spin-singlet to spin-triplet conversion but also they transfer their parity symmetry that induces superconducting correlations with higher angular momentum. For instance, a conventional spin-singlet $s$-wave superconductor with $d$-wave altermagnetism is able to host odd-frequency mixed spin-triplet $d$-wave superconducting pair amplitudes, while when combining with $p$-wave magnetism the emerging superconducting pairing acquires an even-frequency mixed spin-triplet $p$-wave symmetry. We further demonstrate that unconventional magnetism produces even more exotic superconducting correlations in spin-singlet $d$-wave superconductors, where odd-frequency mixed spin-triplet $g$-wave and even-frequency mixed spin-triplet $f$-wave pair symmetries are possible in altermagnets and $p$-wave magnets, respectively. We also discuss how these ideas generalize to spin-triplet $p$-wave superconductors and also show how our results can be applied to unconventional magnets with higher angular momentum, such as with $f$-, $g$-, and $i$-wave symmetries. Our results can help understand the emergent superconducting correlations due to the interplay of unconventional magnetism and superconductivity.

cond-mat.supr-con↗

arXiv2025

Sim4EndoR: A Reinforcement Learning Centered Simulation Platform for Task Automation of Endovascular Robotics

Robotic-assisted percutaneous coronary intervention (PCI) holds considerable promise for elevating precision and safety in cardiovascular procedures. Nevertheless, current systems heavily depend on human operators, resulting in variability and the potential for human error. To tackle these challenges, Sim4EndoR, an innovative reinforcement learning (RL) based simulation environment, is first introduced to bolster task-level autonomy in PCI. This platform offers a comprehensive and risk-free environment for the development, evaluation, and refinement of potential autonomous systems, enhancing data collection efficiency and minimizing the need for costly hardware trials. A notable aspect of the groundbreaking Sim4EndoR is its reward function, which takes into account the anatomical constraints of the vascular environment, utilizing the geometric characteristics of vessels to steer the learning process. By seamlessly integrating advanced physical simulations with neural network-driven policy learning, Sim4EndoR fosters efficient sim-to-real translation, paving the way for safer, more consistent robotic interventions in clinical practice, ultimately improving patient outcomes.

cs.RO↗

arXiv2025

Generative assimilation and prediction for weather and climate

Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks. However, existing approaches mostly focus on the prediction task, and do not incorporate the necessary data assimilation. Moreover, these models suffer from error accumulation in long roll-outs, limiting their applicability to seasonal predictions or climate projections. Here, we introduce Generative Assimilation and Prediction (GAP), a unified deep generative framework for assimilation and prediction of both weather and climate. By learning to quantify the probabilistic distribution of atmospheric states under observational, predictive, and external forcing constraints, GAP excels in a broad range of weather-climate related tasks, including data assimilation, seamless prediction, and climate simulation. In particular, GAP is competitive with state-of-the-art ensemble assimilation, probabilistic weather forecast and seasonal prediction, yields stable millennial simulations, and reproduces climate variability from daily to decadal time scales.

cs.LG↗

arXiv2025

Adaptive cold-atom magnetometry mitigating the trade-off between sensitivity and dynamic range

Cold-atom magnetometers can achieve an exceptional combination of superior sensitivity and high spatial resolution. One key challenge these quantum sensors face is improving the sensitivity within a given timeframe while preserving a high dynamic range. Here, we experimentally demonstrate an adaptive entanglement-free cold-atom magnetometry with both superior sensitivity and high dynamic range. Employing a tailored adaptive Bayesian quantum estimation algorithm designed for Ramsey interferometry using coherent population trapping (CPT), cold-atom magnetometry facilitates adaptive high-precision detection of a direct-current (d.c.) magnetic field with high dynamic range. Through implementing a sequence of correlated CPT-Ramsey interferometry, the sensitivity significantly surpasses the standard quantum limit with respect to total interrogation time. We yield a sensitivity of 6.8$\pm$0.1 picotesla per square root of hertz over a range of 145.6 nanotesla, exceeding the conventional frequentist protocol by 3.3$\pm$0.1 decibels. Our study opens avenues for the next generation of adaptive cold-atom quantum sensors, wherein real-time measurement history is leveraged to improve their performance.

quant-ph↗

arXiv2025

Josephson effect and odd-frequency pairing in superconducting junctions with unconventional magnets

We consider Josephson junctions formed by coupling two conventional superconductors via an unconventional magnet and investigate the formation of Andreev bound states, their impact on the Josephson effect, and the emergent superconducting correlations. We focus on unconventional magnets known as $d$-wave altermagnets and $p$-wave magnets. We find that the Andreev bound states in $d$-wave altermagnet and $p_y$-wave magnet Josephson junctions strongly depend on the transverse momentum, with a spin splitting and low-energy minima as a function of the superconducting phase difference $φ$. In contrast, the Andreev bound states for $p_{x}$-wave magnets are insensitive to the transverse momentum. We show that the Andreev bound states can be probed by the local density of states in the middle of the junction, which also reveals that $d_{x^{2}-y^{2}}$- and $p$-wave magnet junctions are prone to host zero energy peaks. While the zero-energy peak in $d_{x^{2}-y^{2}}$-wave altermagnet junctions tends to oscillate with the magnetic order, it remains robust in $p$-wave magnet junctions. We also demonstrate that the critical currents in $d$-wave altermagnet Josephson junctions exhibit an oscillatory decay with the increase of the magnetic order, while the oscillations are absent in $p$-wave magnet junctions albeit the currents exhibit a slow decay. Furthermore, we also demonstrate that the interplay of the Josephson effect and unconventional magnetic order of $d$-wave altermagnets and $p$-wave magnets originates from odd-frequency spin-triplet $s$-wave superconducting correlations that are otherwise absent. Our results can serve as a guide to pursue the new functionality of Josephson junctions based on unconventional magnets.

cond-mat.supr-con↗

arXiv2024

Theory of Tunneling Spectroscopy in Unconventional $p$-wave Magnet-Superconductor Hybrid Structures

We theoretically study the tunneling conductance of a junction consisting of a two-dimensional unconventional $p$-wave magnet (UPM) and a superconductor (SC) for various pairing symmetries. The zero bias conductance peaks arising from the dispersionless surface Andreev bound states (SABSs) in $d_{xy}$-wave and $p_{x}$-wave superconductor junctions are insensitive against varying the magnetic spin-splitting strength $α_{y}$. Moreover, for chiral $p$- or chiral $d$-wave SCs, zero bias conductance shows a non-monotonic change as a function of $α_{y}$ indicating the existence of the dispersive SABSs. Our obtained results of tunneling spectroscopy based on a UPM serve as an effective way for the identification of the pairing symmetries of unconventional superconductors. It is noted that our used Hamiltonian of UPM is also available for persistent spin helix systems.

cond-mat.supr-con↗

arXiv2024

Atomic clock locking with Bayesian quantum parameter estimation: scheme and experiment

Atomic clocks are crucial for science and technology, but their sensitivity is often restricted by the standard quantum limit. To surpass this limit, correlations between particles or interrogation times must be leveraged. Although the sensitivity can be enhanced to the Heisenberg limit using quantum entanglement, it remains unclear whether the scaling of sensitivity with total interrogation time can achieve the Heisenberg scaling. Here, we design an adaptive Bayesian quantum frequency estimation protocol that approaches the Heisenberg scaling and experimentally demonstrate its validity with a cold-atom coherent-population-trapping (CPT) clock. In further, we achieve robust and high-precision closed-loop locking of the cold-atom CPT clock by utilizing our Bayesian quantum frequency estimation protocol. In comparison to the conventional proportional-integral-differential locking, our Bayesian locking scheme not only yields an improvement of 5.1(4) dB in fractional frequency stability, but also exhibits better robustness against technical noises. Our findings not only provide a robust and high-precision approach to lock atomic clocks, but also hold promising applications in various interferometry-based quantum sensors, such as quantum magnetometers and atomic interferometers.

quant-ph↗

arXiv2024

$φ$ Josephson junction induced by altermagnetism

We study the Josephson effect in a superconductor/altermagnet/superconductor (S/AM/S) junction. We find anomalous phenomena including $0$-$π$ transition as well as multi-nodal current-phase relations. Similar to $d$-wave superconductor, $d$-wave altermagnet can support $φ$ junction where free energy minima locate neither $φ=0$ nor $\pm π$ with double degeneracy. These properties can be tunable by parameters, e.g., the exchange energy, the orientation of crystal axis, the length, and the chemical doping of altermagnet. These rich features lead to accessible functionality of S/AM/S junction.

cond-mat.supr-con↗

arXiv2024

Optical conductivity of the Majorana mode at the s- and d-wave topological superconductor edge

The Majorana fermion offers fascinating possibilities such as non-Abelian statistics and non-local robust qubits, and hunting it is one of the most important topics in current condensed matter physics. Most of the efforts have been focused on the Majorana bound state at zero energy in terms of scanning tunneling spectroscopy searching for the quantized conductance. On the other hand, a chiral Majorana edge channel appears at the surface of a three-dimensional topological insulator when engineering an interface between proximity-induced superconductivity and ferromagnetism. Recent advances in microwave spectroscopy of topological edge states open a new avenue for observing signatures of such Majorana edge states through the local optical conductivity. As a guide to future experiments, we show how the local optical conductivity and density of states present distinct qualitative features depending on the symmetry of the superconductivity, that can be tuned via the magnetization and temperature. In particular, the presence of the Majorana edge state leads to a characteristic non-monotonic temperature dependence achieved by tuning the magnetization.

cond-mat.mes-hall↗

arXiv2024

Generation of squeezed vacuum state in the millihertz frequency band

The detection of gravitational waves has ushered in a new era of observing the universe. Quantum resource advantages offer significant enhancements to the sensitivity of gravitational wave observatories. While squeezed states for ground-based gravitational wave detection have received marked attention, the generation of squeezed states suitable for mid-to-low-frequency detection has remained unexplored. To address the gap in squeezed state optical fields at ultra-low frequencies, we report on the first direct observation of a squeezed vacuum field until Fourier frequency of 4 millihertz with the quantum noise reduction of up to 8 dB, by the employment of a multiple noise suppression scheme. Our work provides quantum resources for future gravitational wave observatories, facilitating the development of quantum precision measurement.

physics.optics↗

arXiv2024

FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting

Ensemble forecasting is crucial for improving weather predictions, especially for forecasts of extreme events. Constructing an ensemble prediction system (EPS) based on conventional NWP models is highly computationally expensive. ML models have emerged as valuable tools for deterministic weather forecasts, providing forecasts with significantly reduced computational requirements and even surpassing the forecast performance of traditional NWP models. However, challenges arise when applying ML models to ensemble forecasting. Recent ML models, such as GenCast and SEEDS model, rely on the ERA5 EDA or operational NWP ensemble members for forecast generation. Their spatial resolution is also considered too coarse for many applications. To overcome these limitations, we introduce FuXi-ENS, an advanced ML model designed to deliver 6-hourly global ensemble weather forecasts up to 15 days. This model runs at a significantly increased spatial resolution of 0.25\textdegree, incorporating 5 atmospheric variables at 13 pressure levels, along with 13 surface variables. By leveraging the inherent probabilistic nature of Variational AutoEncoder (VAE), FuXi-ENS optimizes a loss function that combines the CRPS and the KL divergence between the predicted and target distribution, facilitating the incorporation of flow-dependent perturbations in both initial conditions and forecast. This innovative approach makes FuXi-ENS an advancement over the traditional ones that use L1 loss combined with the KL loss in standard VAE models for ensemble weather forecasting. Results demonstrate that FuXi-ENS outperforms ensemble forecasts from the ECMWF, a world leading NWP model, in the CRPS of 98.1% of 360 variable and forecast lead time combinations. This achievement underscores the potential of the FuXi-ENS model to enhance ensemble weather forecasts, offering a promising direction for further development in this field.

cs.LG↗

arXiv2024

FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models

Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF's state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO, but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research.

physics.ao-ph↗

arXiv2024

Image enhancement algorithm for absorption imaging

The noise in absorption imaging of cold atoms significantly impacts measurement accuracy across a range of applications with ultracold atoms. It is crucial to adopt an approach that offers effective denoising capabilities without compromising the unique structure of the atoms. Here we introduce a novel image enhancement algorithm for cold atomic absorption imaging. The algorithm successfully suppresses background noise, enhancing image contrast significantly. Experimental results showcase that this approach can enhance the accuracy of cold atom particle number measurements by approximately tenfold, all while preserving essential information. Moreover, the method exhibits exceptional performance and robustness when confronted with fringe noise and multi-component imaging scenarios, offering high stability. Importantly, the optimization process is entirely automated, eliminating the need for manual parameter selection. The method is both compatible and practical, making it applicable across various absorption imaging fields.

quant-ph↗

arXiv2024

Performance studies of a SiPM-readout system with a pico-second timing chip

A pico-second timing (PIST) front-end electronic chip has been developed using $55~\mathrm{nm}$ CMOS technology for future electron-positron collider experiments (namely Higgs factories). Extensive tests have been performed to evaluate the timing performance of a dedicated SiPM-readout system equipped with a PIST chip. The results show that the system timing resolution can achieve $45~\mathrm{ps}$ for SiPM signals at the minimum-ionizing particles (MIP) level ($200~\mathrm{p.e.}$) and better than $ 10~\mathrm{ps}$ for signals larger than $1200~\mathrm{p.e.}$, while the PIST intrinsic timing resolution is $4.76 \pm 0.60~\mathrm{ps}$. The PIST dynamic range has been further extended using the time-over-threshold (ToT) technique, which can cover the SiPM response spanning from $\mathrm{\sim 900~p.e.}$ to $~\mathrm{\sim 40000~p.e.}$.

physics.ins-det↗

arXiv2023

Domain formation and universally critical dynamics through phase separation in two-component Bose-Einstein condensates

We explore the defect formation and universally critical dynamics in two-dimensional (2D) two-component Bose-Einstein condensates(BECs) subjected to two types of potential traps: a homogeneous trap and a harmonic trap.We focus on the non-equilibrium universal dynamics of the miscible-immiscible phase transition with both linear and nonlinear quenching types.Although there exists spatial independence of the critical point, we find that the inhomogeneity of trap doesn't affect the phase transition of system and the critical exponents can still be explained by the homogeneous Kibble-Zurek mechanism. By analyzing the Bogoliubov excitations, we establish a power-law relationship between the domain correlation length, the phase transition delay, and the quench time.Furthermore, through real-time simulations of phase transition dynamics, the formation of domain defects and the delay of phase transition in non-equilibrium dynamics are demonstrated, along with the corresponding universal scaling of correlation length and phase transition delay for various quench time and quench coefficients, which align well with our analytical predictions.Our study confirms that the universality class of two-component BECs remains unaffected by dimensionality, while the larger nonlinear coefficients effectively suppress non-adiabatic excitations, offering a novel perspective for addressing adiabatic evolution.

cond-mat.quant-gas↗

arXiv2023

Combinatorial optimization solving by coherent Ising machines based on spiking neural networks

Spiking neural network is a kind of neuromorphic computing that is believed to improve the level of intelligence and provide advantages for quantum computing. In this work, we address this issue by designing an optical spiking neural network and find that it can be used to accelerate the speed of computation, especially on combinatorial optimization problems. Here the spiking neural network is constructed by the antisymmetrically coupled degenerate optical parametric oscillator pulses and dissipative pulses. A nonlinear transfer function is chosen to mitigate amplitude inhomogeneities and destabilize the resulting local minima according to the dynamical behavior of spiking neurons. It is numerically shown that the spiking neural network-coherent Ising machines have excellent performance on combinatorial optimization problems, which is expected to offer new applications for neural computing and optical computing.

quant-ph↗

arXiv2023

Quantum Double Lock-in Amplifier

Quantum lock-in amplifier aims to extract an alternating signal within strong noise background by using quantum strategy. However, as the target signal usually has an unknown initial phase, we can't obtain the complete information of its amplitude, frequency and phase in a single lock-in measurement. Here, to overcome this challenge, we give a general protocol for achieving a quantum double lock-in amplifier and illustrate its realization. In analog to a classical double lock-in amplifier, our protocol is accomplished via two quantum mixers under orthogonal pulse sequences. The two orthogonal pulse sequences act the roles of two orthogonal reference signals in a classical double lock-in amplifier. Combining the output signals, the complete characteristics of the target signal can be obtained. As an example, we illustrate the realization of our quantum double lock-in amplifier via a five-level double-$Λ$ coherent population trapping system with $^{87}$Rb atoms, in which each $Λ$ structure acts as a quantum mixer and the two applied dynamical decoupling sequences take the roles of two orthogonal reference signals. Our numerical calculations show that the quantum double lock-in amplifier is robust against experimental imperfections, such as finite pulse length and stochastic noise. Our study opens an avenue for extracting complete characteristics of an alternating signal within strong noise background, which is beneficial for developing practical quantum sensing technologies.

quant-ph↗