arXiv subjects

Haowei Xu

Publications and source records attributed to Haowei Xu.

At least 37 records · Page 2Linked to original sources

arXiv2024

Stochastic Parrots or ICU Experts? Large Language Models in Critical Care Medicine: A Scoping Review

With the rapid development of artificial intelligence (AI), large language models (LLMs) have shown strong capabilities in natural language understanding, reasoning, and generation, attracting amounts of research interest in applying LLMs to health and medicine. Critical care medicine (CCM) provides diagnosis and treatment for critically ill patients who often require intensive monitoring and interventions in intensive care units (ICUs). Can LLMs be applied to CCM? Are LLMs just like stochastic parrots or ICU experts in assisting clinical decision-making? This scoping review aims to provide a panoramic portrait of the application of LLMs in CCM. Literature in seven databases, including PubMed, Embase, Scopus, Web of Science, CINAHL, IEEE Xplore, and ACM Digital Library, were searched from January 1, 2019, to June 10, 2024. Peer-reviewed journal and conference articles that discussed the application of LLMs in critical care settings were included. From an initial 619 articles, 24 were selected for final review. This review grouped applications of LLMs in CCM into three categories: clinical decision support, medical documentation and reporting, and medical education and doctor-patient communication. LLMs have advantages in handling unstructured data and do not require manual feature engineering. Meanwhile, applying LLMs to CCM faces challenges, including hallucinations, poor interpretability, bias and alignment challenges, and privacy and ethics issues. Future research should enhance model reliability and interpretability, integrate up-to-date medical knowledge, and strengthen privacy and ethical guidelines. As LLMs evolve, they could become key tools in CCM to help improve patient outcomes and optimize healthcare delivery. This study is the first review of LLMs in CCM, aiding researchers, clinicians, and policymakers to understand the current status and future potentials of LLMs in CCM.

cs.AI↗

arXiv2024

Multi-task learning for molecular electronic structure approaching coupled-cluster accuracy

Machine learning (ML) plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules. However, most existing ML models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work, we developed a unified ML method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with the widely-used hybrid and double hybrid functionals in computational costs and prediction accuracy of various quantum chemical properties. As case studies, we apply the model to aromatic compounds and semiconducting polymers on both ground state and excited state properties, demonstrating its accuracy and generalization capability to complex systems that are hard to calculate using CCSD(T)-level methods.

physics.chem-ph↗

arXiv2024

Exponentially Enhanced non-Hermitian Cooling

Certain non-Hermitian systems exhibit the skin effect, whereby the wavefunctions become exponentially localized at one edge of the system. Such exponential amplification of wavefunction has received significant attention due to its potential applications in e.g., classical and quantum sensing. However, the opposite edge of the system, featured by the exponentially suppressed wavefunctions, remains largely unexplored. Leveraging this phenomenon, we introduce a non-Hermitian cooling mechanism, which is fundamentally distinct from traditional refrigeration or laser cooling techniques. Notably, non-Hermiticity will not amplify thermal excitations, but rather redistribute them. Hence, thermal excitations can be cooled down at one edge of the system, and the cooling effect can be exponentially enhanced by the number of auxiliary modes, albeit with a lower bound that depends on the dissipative interaction with the environment. Non-Hermitian cooling does not rely on intricate properties such as exceptional points or non-trivial topology, and it can apply to a wide range of Bosonic modes such as photons, phonons, magnons, etc.

quant-ph↗

arXiv2023

Blind quantum machine learning with quantum bipartite correlator

Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes. In this work, we introduce novel blind quantum machine learning protocols based on the quantum bipartite correlator algorithm. Our protocols have reduced communication overhead while preserving the privacy of data from untrusted parties. We introduce robust algorithm-specific privacy-preserving mechanisms with low computational overhead that do not require complex cryptographic techniques. We then validate the effectiveness of the proposed protocols through complexity and privacy analysis. Our findings pave the way for advancements in distributed quantum computing, opening up new possibilities for privacy-aware machine learning applications in the era of quantum technologies.

quant-ph↗

arXiv2023

Ferroelastic twin wall mediated ferro-flexoelectricity and bulk photovoltaic effect in SrTiO$_3$

Ferroelastic twin walls in nonpolar materials can give rise to a spontaneous polarization due to symmetry breaking. Nevertheless, the bi-stable polarity of twin walls and its reversal have not yet been demonstrated. Here, we report that the polarity of SrTiO$_3$ twin walls can be switched by ultra-low strain gradient. Using first-principles-based machine-learning potential, we demonstrate that the twin walls can be deterministically rotated and realigned in specific directions under strain gradient, which breaks the inversion symmetry of a sequence of walls and leads to a macroscopic polarization. The system can maintain polarity even after the strain gradient is removed. As a result, the polarization of twin walls can exhibit ferroelectric-like hysteresis loop upon cyclic bending, namely ferro-flexoelectricity. Finally, we propose a scheme to experimentally detect the polarity of twin wall by measuring the bulk photovoltaic responses. Our findings suggest a twin-wall-mediated ferro-flexoelectricity in SrTiO$_3$, which could be potentially exploited as functional elements in nano-electronic devices design.

cond-mat.mtrl-sci↗

arXiv2023

Precise Fermi-level engineering in a topological Weyl semimetal via fast ion implantation

The precise controllability of the Fermi level is a critical aspect of quantum materials. For topological Weyl semimetals, there is a pressing need to fine-tune the Fermi level to the Weyl nodes and unlock exotic electronic and optoelectronic effects associated with the divergent Berry curvature. However, in contrast to 2D materials, where the Fermi level can be controlled through various techniques, the situation for bulk crystals beyond laborious chemical doping poses significant challenges. Here, we report the meV-level ultra-fine-tuning of the Fermi level of bulk topological Weyl semimetal TaP using accelerator-based high-energy hydrogen implantation and theory-driven planning. By calculating the desired carrier density and controlling the accelerator profiles, the Fermi level can be fine-tuned from 5 meV to only $\sim$0.5 meV (DFT calculations) away from the Weyl nodes. The Weyl nodes are preserved, while the carrier mobility is largely retained. Our work demonstrates the viability of this generic approach to tune the Fermi level in semimetal systems and could serve to achieve property fine-tuning for other bulk quantum materials with ultrahigh precision.

cond-mat.mtrl-sci↗

arXiv2023

Solid-State $\rm ^{229}Th$ Nuclear Laser with Two-Photon Pumping

The radiative excitation of the 8.3 eV isomeric state of thorium-229 is an outstanding challenge due to the lack of tunable far-ultraviolet (F-UV) sources. In this work, we propose an efficient two-photon pumping scheme for thorium-229 using the optonuclear quadrupolar effect, which only requires a 300 nm UV-B pumping laser. We further demonstrate that population inversion between the nuclear isomeric and ground states can be achieved at room temperature using a two-step pumping process. The nuclear laser, which has been pursued for decades, may be realized using a Watt-level UV-B pumping laser and ultrawide bandgap thorium compounds (e.g., $\rm ThF_4$, $\rm Na_2ThF_6$, or $\rm K_2ThF_6$) as the gain medium.

nucl-th↗

arXiv2023

Reinforcement learning-guided long-timescale simulation of hydrogen transport in metals

Atomic diffusion in solids is an important process in various phenomena. However, atomistic simulations of diffusion processes are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interests. In this work, we developed a long-timescale method using reinforcement learning that simulates diffusion processes. As a testbed, we simulate hydrogen diffusion in pure metals and a medium entropy alloy, CrCoNi, getting hydrogen diffusivity reasonably consistent with previous experiments. We also demonstrate that our method can accelerate the sampling of low-energy configurations compared to the Metropolis-Hastings algorithm using hydrogen migration to copper (111) surface sites as an example.

cond-mat.mtrl-sci↗

arXiv2023

Giant room-temperature nonlinearities from a monolayer Janus topological semiconductor

Nonlinear optical materials possess wide applications, ranging from terahertz and mid-infrared detection to energy harvesting. Recently, the correlations between nonlinear optical responses and topological properties, such as Berry curvature and the quantum metric tensor, have stimulated great interest. Here, we report giant room-temperature nonlinearities in an emergent non-centrosymmetric two-dimensional topological material, the Janus transition metal dichalcogenides in the 1T' phase, which are synthesized by an advanced atomic-layer substitution method. High harmonic generation, terahertz emission spectroscopy, and second harmonic generation measurements consistently reveal orders-of-the-magnitude enhancement in terahertz-frequency nonlinearities of 1T' MoSSe (e.g., > 50 times higher than 2H MoS$_2$ for 18th order harmonic generation; > 20 times higher than 2H MoS$_2$ for terahertz emission). It is elucidated that such colossal nonlinear optical responses come from topological band mixing and strong inversion symmetry breaking due to the Janus structure. Our work defines general protocols for designing materials with large nonlinearities and preludes the applications of topological materials in optoelectronics down to the monolayer limit. This two-dimensional form of topological materials also constitute a unique platform for examining origin of the anomalous high-harmonic generation, with potential applications as building blocks for scalable attosecond sources.

cond-mat.mes-hall↗

arXiv2023

Can ChatGPT be used to generate scientific hypotheses?

We investigate whether large language models can perform the creative hypothesis generation that human researchers regularly do. While the error rate is high, generative AI seems to be able to effectively structure vast amounts of scientific knowledge and provide interesting and testable hypotheses. The future scientific enterprise may include synergistic efforts with a swarm of "hypothesis machines", challenged by automated experimentation and adversarial peer reviews.

cs.CL↗

arXiv2023

Laser Cooling of Nuclear Magnons

The initialization of nuclear spin to its ground state is challenging due to its small energy scale compared with thermal energy, even at cryogenic temperature. In this Letter, we propose an opto-nuclear quadrupolar effect, whereby two-color optical photons can efficiently interact with nuclear spins. Leveraging such an optical interface, we demonstrate that nuclear magnons, the collective excitations of nuclear spin ensemble, can be cooled down optically. Under feasible experimental conditions, laser cooling can suppress the population and entropy of nuclear magnons by more than two orders of magnitude, which could facilitate the application of nuclear spins in quantum information science.

quant-ph↗

arXiv2023

Two-photon Interface of Nuclear Spins Based on the Opto-Nuclear Quadrupolar Effect

Photons and nuclear spins are two well-known building blocks in quantum information science and technology. Establishing an efficient interface between optical photons and nuclear spins, while highly desirable for hybridizing these two quantum systems, is challenging because the interactions between nuclear spins and the environment are usually weak in magnitude, and there is also a formidable gap between nuclear spin frequencies and optical frequencies. In this work, we propose an opto-nuclear quadrupolar (ONQ) effect, whereby optical photons can be efficiently coupled to nuclear spins, similar to Raman scattering. Compared to previous works, ancilla electron spins are not required for the ONQ effect. This leads to advantages such as applicability in defect-free nonmagnetic crystals and longer nuclear spin coherence time. In addition, the frequency of the optical photons can be arbitrary, so they can be fine-tuned to minimize the material heating and to match telecom wavelengths for long-distance communications. Using perturbation theory and first-principles calculations, we demonstrate that the ONQ effect is stronger by several orders of magnitude than other nonlinear optical effects that could couple to nuclear spins. Based on this rationale, we propose promising applications of the ONQ effect, including quantum memory, quantum transduction, and materials isotope spectroscopy. We also discuss issues relevant to the experimental demonstration of the ONQ effect.

quant-ph↗

arXiv2022

Communication-efficient Quantum Algorithm for Distributed Machine Learning

The growing demands of remote detection and increasing amount of training data make distributed machine learning under communication constraints a critical issue. This work provides a communication-efficient quantum algorithm that tackles two traditional machine learning problems, the least-square fitting and softmax regression problem, in the scenario where the data set is distributed across two parties. Our quantum algorithm finds the model parameters with a communication complexity of $O(\frac{\log_2(N)}ε)$, where $N$ is the number of data points and $ε$ is the bound on parameter errors. Compared to classical algorithms and other quantum algorithms that achieve the same output task, our algorithm provides a communication advantage in the scaling with the data volume. The building block of our algorithm, the quantum-accelerated estimation of distributed inner product and Hamming distance, could be further applied to various tasks in distributed machine learning to accelerate communication.

quant-ph↗

arXiv2022

Nonlinear Nonreciprocal Photocurrents under Phonon Dressing

Nonlinear optical (NLO) effects have attracted great interest recently. However, by far the computational studies on NLO use the independent particle approximation and ignore many-body effects. Here we develop a generic Green's function framework to calculate the NLO response functions, which can incorporate various many-body interactions. We focus on the electron-phonon coupling and reveal that phonon dressing can make significant impacts on nonlinear photocurrent, such as the bulk photovoltaic (BPV) and bulk spin photovoltaic (BSPV) currents. BPV/BSPV should be zero for centrosymmetric crystals, but when phonons are driven out-of-equilibrium, by for example, a temperature gradient $\nabla T$, the optical selections rules are altered and phonon-pumped BPV/BSPV currents can be non-zero in nominally centrosymmetric crystal. Moreover, we elucidate that such NLO responses under non-equilibrium phonon dressing can be nonreciprocal, as the direction of the current does not necessarily get reversed when the direction of the temperature gradient is reversed.

cond-mat.mtrl-sci↗

arXiv2022

Generalized Wilson Loop Method for Nonlinear Light-Matter Interaction

Nonlinear light-matter interaction, as the core of ultrafast optics, bulk photovoltaics, nonlinear optical sensing and imaging, and efficient generation of entangled photons, has been traditionally studied by first-principles theoretical methods with the sum-over-states approach. However, this indirect method often suffers from the divergence at band degeneracy and optical zeros as well as convergence issues and high computation costs when summing over the states. Here, using shift vector and shift current conductivity tensor as an example, we present a gauge-invariant generalized approach for efficient and direct calculations of nonlinear optical responses by representing interband Berry curvature, quantum metric, and shift vector in a generalized Wilson loop. This generalized Wilson loop method avoids the above cumbersome challenges and allows for easy implementation and efficient calculations. More importantly, the Wilson loop representation provides a succinct geometric interpretation of nonlinear optical processes and responses based on quantum geometric tensors and quantum geometric potentials and can be readily applied to studying other excited-state responses.

cond-mat.mtrl-sci↗

arXiv2022

Characterizing temperature and strain variations with qubit ensembles for their robust coherence protection

Solid-state spin defects, especially nuclear spins with potentially achievable long coherence times, are compelling candidates for quantum memories and sensors. However, their current performances are still limited by the decoherence due to the variation of their intrinsic quadrupole and hyperfine interactions. We propose an \textit{unbalanced echo} to overcome this challenge by using a second spin to refocus the variation of these interactions, which preserves the quantum information stored in the free evolution. The unbalanced echo can be used to probe the temperature and strain distribution in materials. Experimentally, we demonstrate a 20-fold $T_2^*$ coherence time increase in an ensemble of $\sim10^{10}$ nuclear spins in diamond. Theoretically, we develop first-principles methods to predict these interaction variations and reveal their correlation in large temperature and strain ranges. We numerically show that our method can refocus stronger noise variations than our current experiments and achieves a 400-fold coherence improvement for a 25~K temperature inhomogeneity.

quant-ph↗

arXiv2021

Abnormal Surface Nonlinear Optical Responses in Topological Materials

Nonlinear optical (NLO) responses of topological materials are under active research in recent years. Yet by far, most studies focused on the bulk properties, whereas the surface effects and the difference between surface and bulk responses have not been systematically studied. Here we develop a generic Green's function framework to investigate the surface NLO properties of topological materials. The Green's function framework can naturally incorporate many-body effects and can be easily extended to high-order NLO responses. Using Td-WTe2 as an example, we reveal that the surface can behave disparately from the bulk under light illumination. Remarkably, the shift and circular currents on the surface can flow in opposite directions to those in the bulk interior. Moreover, the light-induced spin current on the surface can be orders of magnitude stronger than its bulk counterpart. We also study the responses under inhomogeneous field and higher-order NLO effect, which are all distinct on the surface. These anomalous surface NLO responses suggest that light can be a valuable tool for probing the surface states of topological materials. On the other hand, the surface effects shall be prudently considered when investigating the optical properties of topological materials, especially if the material is of nanoscale and/or the light penetration depth is small.

physics.optics↗

arXiv2021

Pure Spin Photocurrent in Non-centrosymmetric Crystals: Bulk Spin Photovoltaic Effect

Spin current generators are critical components for spintronics-based information processing. In this work, we theoretically and computationally investigate the bulk spin photovoltaic (BSPV) effect for creating DC spin current under light illumination. The only requirement for BPSV is inversion symmetry breaking, thus it applies to a broad range of materials and can be readily integrated with existing semiconductor technologies. The BSPV effect is a cousin of the bulk photovoltaic (BPV) effect, whereby a DC charge current is generated under light. Thanks to the different selection rules on spin and charge currents, a pure spin current can be realized if the system possesses mirror symmetry or inversion-mirror symmetry. The mechanism of BPSV and the role of the electronic relaxation time $τ$ are also elucidated. We apply our theory to several distinct material systems, including transition metal dichalcogenides, anti-ferromagnetic $\rm MnBi_2Te_4$, and the surface of topological crystalline insulator cubic $\rm SnTe$.

cond-mat.mtrl-sci↗