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We propose a protocol for the preparation of high-fidelity entangled cat states with composite pulses. The physical model contains two Kerr-nonlinear resonators and a cavity. By properly designing the parameters, each Kerr-nonlinear resonator is confined in the cat-state subspace and the entangled cat states can be generated efficiently. We introduce composite two-photon drives with multiple amplitudes and frequencies to improve the fidelity of the entangled cat states in the presence of parameter errors. The performance of the protocol is estimated by taking into account the parametric errors and decoherence. Numerical simulation results show that the protocol is insensitive to timing error and detuning error, and has strong robustness to decoherence. We hope the protocol may provide a method for preparing stable entangled cat states., Comment: 9 pages, 7 figures, comments are welcome

It is well known that in the quantum Rabi model, a three-photon resonance occurs when the cavity field bare frequency is about 1/3 of the atomic transition frequency. In this manuscript, we show that the resonance can also be generated in the absence of the 1/3 condition by employing an artificial atom with tunable transition frequency. To realize the protocol, the modulation frequency should be comparable to the cavity frequency in order to induce a counter-rotating interaction in the effective Hamiltonian. In this way, three-photon Rabi oscillations can be observed in a small-detuning regime, thus avoiding the excitation of high-energy states. We derive an effective Hamiltonian (equivalent to the anisotropic Rabi model Hamiltonian) to determine the magnitude of the energy splitting and the resonance position. Numerical simulations results show that the protocol not only generates a three-photon resonance, but also has a detectable output photon flux. We hope the protocol can be exploited for the realization of Fock-state sources and the generation of multiparticle entanglement., Comment: 10 pages, 10 figures, comments are welcome

Cat-state qubits formed by photonic coherent states are a promising candidate for realizing fault-tolerant quantum computing. Such logic qubits have a biased noise channel that the bit-flip error dominates over all the other errors. In this manuscript, we propose an optimally robust protocol using the control method of shortcuts to adiabaticity to realize a nearly perfect population inversion in a cat-state qubit. We construct a shortcut based on the Lewis-Riesenfeld invariant and examine the stability versus different types of perturbations for the fast and robust population inversion. Numerical simulations demonstrate that the population inversion can be mostly insensitive to systematic errors in our protocol. Even when the parameter imperfection rate for bit-flip control is $20\%$, the final population of the target state can still reach $\geq 99\%$. The optimally robust control provides a feasible method for fault-tolerant and scalable quantum computation., Comment: 9 pages, 8 figures, comments are welcome

Vortex lasers have attracted substantial attention in recent years owing to their wide array of applications such as micromanipulation, optical multiplexing, and quantum cryptography. In this work, we propose and demonstrate chiral emission of vortex microlaser leveraging the collective modes from omnidirectionally hybridizing the guided mode resonances (GMRs) within photonic crystal (PhC) slabs. Specifically, we encircle a central uniform PhC with a heterogeneous PhC that features a circular lateral boundary. Consequently, the bulk GMRs hybridize into a series of collective modes due to boundary scatterings, resulting in a vortex pattern in real space with a spiral phase front in its radiation. Benefiting from the long lifetime of GMRs as quasi-bound state in the continuum and using asymmetric pumping to lift the chiral symmetry, we demonstrate stable single-mode lasing oscillation with a low optical pumping threshold of $18~\mathrm{kW/cm^2}$ at room temperature. We identify the real-space vortex through polarization-resolved imaging and self-interference patterns, showing a vivid example of applying collective modes to realize compact and energy-efficient vortex microlasers.

Classifying videos into distinct categories, such as Sport and Music Video, is crucial for multimedia understanding and retrieval, especially when an immense volume of video content is being constantly generated. Traditional methods require video decompression to extract pixel-level features like color, texture, and motion, thereby increasing computational and storage demands. Moreover, these methods often suffer from performance degradation in low-quality videos. We present a novel approach that examines only the post-compression bitstream of a video to perform classification, eliminating the need for bitstream decoding. To validate our approach, we built a comprehensive data set comprising over 29,000 YouTube video clips, totaling 6,000 hours and spanning 11 distinct categories. Our evaluations indicate precision, accuracy, and recall rates consistently above 80%, many exceeding 90%, and some reaching 99%. The algorithm operates approximately 15,000 times faster than real-time for 30fps videos, outperforming traditional Dynamic Time Warping (DTW) algorithm by seven orders of magnitude., Comment: 5 pages, 5 figures, 1 table. arXiv admin note: substantial text overlap with arXiv:2309.07361

We introduce SoftTiger, a clinical large language model (CLaM) designed as a foundation model for healthcare workflows. The narrative and unstructured nature of clinical notes is a major obstacle for healthcare intelligentization. We address a critical problem of structuring clinical notes into clinical data, according to international interoperability standards. We collect and annotate data for three subtasks, namely, international patient summary, clinical impression and medical encounter. We then supervised fine-tuned a state-of-the-art LLM using public and credentialed clinical data. The training is orchestrated in a way that the target model can first support basic clinical tasks such as abbreviation expansion and temporal information extraction, and then learn to perform more complex downstream clinical tasks. Moreover, we address several modeling challenges in the healthcare context, e.g., extra long context window. Our blind pairwise evaluation shows that SoftTiger outperforms other popular open-source models and GPT-3.5, comparable to Gemini-pro, with a mild gap from GPT-4. We believe that LLMs may become a step-stone towards healthcare digitalization and democratization. Therefore, we publicly release SoftTiger models at scales of 13 billion and 70 billion parameters, as well as datasets and code for our innovative scalable evaluation, hopefully, making a significant contribution to the healthcare industry., Comment: Accepted to AAAI 2024 Spring Symposium on Clinical Foundation Models, Stanford University, Stanford, California

Berry curvature that describes local geometrical properties of energy bands can elucidate many fascinating phenomena in solid-state, photonic, and phononic systems, given its connection to global topological invariants such as the Chern number. Despite its significance, the observation of Berry curvature poses a substantial challenging since wavefunctions are deeply embedded within the system. Here, we theoretically propose a correspondence between the geometry of far-field radiation and the underneath band topology of non-Hermitian systems, thus providing a general method to fully capture the Berry curvature without strongly disturbing the eigenstates. We further experimentally observe the Berry curvature in a honeycomb photonic crystal slab from polarimetry measurements and quantitatively obtain the non-trivial valley Chern number. Our work reveals the feasibility of retrieving the bulk band topology from escaping photons and paves the way to exploring intriguing topological landscapes in non-Hermitian systems.

Cat-state qubits formed by photonic cat states have a biased noise channel, i.e., one type of error dominates over all the others. We demonstrate that such biased-noise qubits are also promising for error-tolerant simulations of the quantum Rabi model (and its varieties) by coupling a cat-state qubit to an optical cavity. Using the cat-state qubit can effectively enhance the counter-rotating coupling, allowing us to explore several fascinating quantum phenomena relying on the counter-rotating interaction. Moreover, another benefit from biased-noise cat qubits is that the two main error channels (frequency and amplitude mismatches) are both exponentially suppressed. Therefore, the simulation protocols are robust against parameter errors of the parametric drive which determines the projection subspace. We analyze three examples: (i) collapse and revivals of quantum states; (ii) hidden symmetry and tunneling dynamics; and (iii) pair-cat-code computation., Comment: 7 pages, 7 figures

We release and introduce the TigerBot family of large language models (LLMs), consisting of base and chat models, sized from 7, 13, 70 and 180 billion parameters. We develop our models embarking from Llama-2 and BLOOM, and push the boundary further in data, training algorithm, infrastructure, and application tools. Our models yield meaningful performance gain over SOTA open-source models, e.g., Llama-2, specifically 6% gain in English and 20% gain in Chinese. TigerBot model family also achieves leading performance in major academic and industrial benchmarks and leaderboards. We believe that TigerBot represents just a snapshot of lightning-fast progression in LLM open-source community. Therefore, we are thrilled to give back by publicly releasing our models and reporting our approach behind, with additional emphases on building SOTA LLMs in a democratized way and making LLMs of use in real-world applications.

Classifying videos into distinct categories, such as Sport and Music Video, is crucial for multimedia understanding and retrieval, especially in an age where an immense volume of video content is constantly being generated. Traditional methods require video decompression to extract pixel-level features like color, texture, and motion, thereby increasing computational and storage demands. Moreover, these methods often suffer from performance degradation in low-quality videos. We present a novel approach that examines only the post-compression bitstream of a video to perform classification, eliminating the need for bitstream. We validate our approach using a custom-built data set comprising over 29,000 YouTube video clips, totaling 6,000 hours and spanning 11 distinct categories. Our preliminary evaluations indicate precision, accuracy, and recall rates well over 80%. The algorithm operates approximately 15,000 times faster than real-time for 30fps videos, outperforming traditional Dynamic Time Warping (DTW) algorithm by six orders of magnitude.

Quantum Key Distribution (QKD) is a physical layer encryption technique that enables two distant parties to exchange secure keys with information-theoretic security. In the last two decades, QKD has transitioned from laboratory research to real-world applications, including multi-user quantum access networks (QANs). This network structure allows users to share a single-photon detector at a network node through time-division multiplexing, thereby significantly reducing the network cost. However, current QAN implementations require additional hardware for auxiliary tasks such as time synchronization. To address this issue, we propose a cost-efficient QAN that uses qubit-based synchronization. In this approach, the transmitted qubits facilitate time synchronization, eliminating the need for additional synchronization hardware. We tested our scheme by implementing a network for two users and successfully achieved average secure key rates of $53.84$ kbps and $71.90$ kbps for each user over a 50-km commercial fiber spool. In addition, we investigated the capacity of the access network under cross-talk and loss conditions. The simulation results demonstrate that this scheme can support a QAN with 64 users with key rates up to 1070~bps. Our work provides a feasible and cost-effective way to implement a multi-user QKD network, further promoting the widespread application of QKD., Comment: Comments are welcomed. Accepted by Science China-Physics Mechanics & Astronomy

Quantum key distribution (QKD) is a method that enables two remote parties to share a secure key string. Clock synchronization between two parties is a crucial step in the normal operation of QKD. Qubit-based synchronization can achieve clock synchronization by transmitting quantum states between two remote parties, eliminating the necessity for hardware synchronization and thereby greatly reducing the hardware requirements of a QKD system. Nonetheless, classical qubit-based synchronization exhibits poor performance in continuous and high-loss systems, hindering its wide applicability in various scenarios. We propose a qubit-based distributed frame synchronization method that can achieve time recovery in a continuously running system and resist higher losses. Experimental results show that the proposed method outperforms the advanced qubit-based synchronization method Qubit4Sync in a continuously running system. We believe our method is applicable to a broad range of QKD scenarios, including drone-based QKD and quantum network construction., Comment: Any comments are welcomed

In this work, we develop a supervised learning model for implementing robust quantum control in composite-pulse systems, where the training parameters can be either phases, detunings, or Rabi frequencies. This model exhibits great resistance to all kinds of systematic errors, including single, multiple, and time-varying errors. We propose a modified gradient descent algorithm for adapting the training of phase parameters, and show that different sampling methods result in different robust performances. In particular, there is a trade-off between high fidelity and robustness for a given number of training parameters, and both can be simultaneously enhanced by increasing the number of training parameters (pulses). For its applications, we demonstrate that the current model can be used for achieving high-fidelity arbitrary superposition states and universal quantum gates in a robust manner. This work provides a highly efficient learning model for fault-tolerant quantum computation by training various physical parameters., Comment: 19 pages, comments welcome!

Humans have a strong intuitive understanding of physical processes such as fluid falling by just a glimpse of such a scene picture, i.e., quickly derived from our immersive visual experiences in memory. This work achieves such a photo-to-fluid-dynamics reconstruction functionality learned from unannotated videos, without any supervision of ground-truth fluid dynamics. In a nutshell, a differentiable Euler simulator modeled with a ConvNet-based pressure projection solver, is integrated with a volumetric renderer, supporting end-to-end/coherent differentiable dynamic simulation and rendering. By endowing each sampled point with a fluid volume value, we derive a NeRF-like differentiable renderer dedicated from fluid data; and thanks to this volume-augmented representation, fluid dynamics could be inversely inferred from the error signal between the rendered result and ground-truth video frame (i.e., inverse rendering). Experiments on our generated Fluid Fall datasets and DPI Dam Break dataset are conducted to demonstrate both effectiveness and generalization ability of our method.