arXiv subjects

Zhenda Xie

Publications and source records attributed to Zhenda Xie.

At least 19 recordsLinked to original sources

arXiv2026

High Success Probability, Fidelity, and Purity Nonlinear Optical Two-Qubit Gates on Chip

Optical two-qubit gate with high success probability, fault-tolerant fidelity, and high-purity outputs is a fundamental yet unsolved challenge, essential for large-scale optical quantum computing toward quantum advantage. Here, we propose a feasible scheme for such gate using thin-film lithium niobate platform, enabling \c{hi}(2) nonlinear photon-photon interaction with 100% efficiency. By decoupling photon interaction and qubit flip operations, fidelity ceiling is removed, and output state purity is recovered by spectral-phase pre-compensation based on a full-spectral photon interaction model, yielding a CNOT gate with 84% success probability, 93% purity, and unity fidelity.

quant-ph↗

arXiv2026

Quantum photonic frequency processor on thin-film lithium niobate

The rapid development of photonic quantum information processing necessitates precise and programmable control over optical frequency, a capability critical not only for achieving photon indistinguishability but also for exploiting a virtually unbounded frequency dimension. However, efficient and scalable processing of frequency-encoded photon states remains challenging, primarily due to the limited nonlinear optical interaction in most photonic materials. Here, by harnessing the high-performance thin-film lithium niobate electro-optic (EO) platform, we demonstrate an integrated quantum photonic frequency processor that enables coherent and programmable control of photon frequency with high precision. We establish a scalable architecture for frequency-encoded quantum information processing. Using a fully integrated photonic chip, we realize a universal set of frequency-encoded quantum logic gates, including arbitrary single-qubit rotation gates and the two-qubit controlled-phase gate. Furthermore, we demonstrate its application in high fidelity characterization of frequency-bin entangled states. Our work reveals the unprecedented potential of utilizing the frequency degree of freedom in integrated quantum photonic systems.

quant-ph↗

arXiv2026

Optically locked low-noise photonic microwave oscillator

The next-generation sensing and communication applications rely on high-frequency microwave generation with low-noise. The microwave photonic technology is promising by the practical application is limited by its complex architecture so far. Here, we demonstrate an optically locked low-noise photonic microwave oscillator, so that all the optical components are packaged within a small module of 166 mL, and low noise microwave generation is achieved at 10.4 GHz with single-sideband phase noise of -54 dBc/Hz at 10 Hz, -141 dBc/Hz at 10 kHz, and -162 dBc/Hz at 10 MHz offset. Above performance arises from a dual-laser self-injection-locking scheme to a single Fabry-Perot cavity with high Q exceeding 10^8, with over 20 dB common-mode noise suppression. The low-noise nature of such reference is coherently transferred to the X-band through a high-performance TFLN electro-optic comb chip, thereby overcoming long-standing barriers in photonic microwave integration to enable truly field-deployable low-noise microwave generation.

physics.optics↗

arXiv2026

Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models

While Mixture-of-Experts (MoE) scales capacity via conditional computation, Transformers lack a native primitive for knowledge lookup, forcing them to inefficiently simulate retrieval through computation. To address this, we introduce conditional memory as a complementary sparsity axis, instantiated via Engram, a module that modernizes classic $N$-gram embedding for O(1) lookup. By formulating the Sparsity Allocation problem, we uncover a U-shaped scaling law that optimizes the trade-off between neural computation (MoE) and static memory (Engram). Guided by this law, we scale Engram to 27B parameters, achieving superior performance over a strictly iso-parameter and iso-FLOPs MoE baseline. Most notably, while the memory module is expected to aid knowledge retrieval (e.g., MMLU +3.4; CMMLU +4.0), we observe even larger gains in general reasoning (e.g., BBH +5.0; ARC-Challenge +3.7) and code/math domains~(HumanEval +3.0; MATH +2.4). Mechanistic analyses reveal that Engram relieves the backbone's early layers from static reconstruction, effectively deepening the network for complex reasoning. Furthermore, by delegating local dependencies to lookups, it frees up attention capacity for global context, substantially boosting long-context retrieval (e.g., Multi-Query NIAH: 84.2 to 97.0). Finally, Engram establishes infrastructure-aware efficiency: its deterministic addressing enables runtime prefetching from host memory, incurring negligible overhead. We envision conditional memory as an indispensable modeling primitive for next-generation sparse models.

cs.CL↗

arXiv2025

mHC: Manifold-Constrained Hyper-Connections

Recently, studies exemplified by Hyper-Connections (HC) have extended the ubiquitous residual connection paradigm established over the past decade by expanding the residual stream width and diversifying connectivity patterns. While yielding substantial performance gains, this diversification fundamentally compromises the identity mapping property intrinsic to the residual connection, which causes severe training instability and restricted scalability, and additionally incurs notable memory access overhead. To address these challenges, we propose Manifold-Constrained Hyper-Connections (mHC), a general framework that projects the residual connection space of HC onto a specific manifold to restore the identity mapping property, while incorporating rigorous infrastructure optimization to ensure efficiency. Empirical experiments demonstrate that mHC is effective for training at scale, offering tangible performance improvements and superior scalability. We anticipate that mHC, as a flexible and practical extension of HC, will contribute to a deeper understanding of topological architecture design and suggest promising directions for the evolution of foundational models.

cs.CL↗

arXiv2025

Entanglement distribution over metropolitan fiber using an on-chip broadband polarization entangled photon source

Entangled photon pairs are of crucial importance in quantum networks. For the future demands of large-scale and secure quantum communication, integrated photon sources are highly effective solutions. Here, we report entanglement distribution over a 30 km metropolitan area using on-chip broadband silicon nanowire biphoton polarization entangled source based on a silicon-on-insulator (SOI) platform. This source generates a continuous spectrum spanning the entire C-band (4.5 THz), achieving a locally detected coincidence counts of about 154 kHz within 100 GHz bandwidth, making it suitable for long-distance entanglement distribution among multiple users. By combining this source with quantum entanglement, enhanced by high-precision clock synchronization that achieves a standard deviation of 56.8 ps over 600 s, we observe a violation of the CHSH inequality by 27.8 standard deviations. Our results showcase the potential of silicon photonic technology as a scalable and practical platform for quantum technologies.

quant-ph↗

arXiv2025

Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

Long-context modeling is crucial for next-generation language models, yet the high computational cost of standard attention mechanisms poses significant computational challenges. Sparse attention offers a promising direction for improving efficiency while maintaining model capabilities. We present NSA, a Natively trainable Sparse Attention mechanism that integrates algorithmic innovations with hardware-aligned optimizations to achieve efficient long-context modeling. NSA employs a dynamic hierarchical sparse strategy, combining coarse-grained token compression with fine-grained token selection to preserve both global context awareness and local precision. Our approach advances sparse attention design with two key innovations: (1) We achieve substantial speedups through arithmetic intensity-balanced algorithm design, with implementation optimizations for modern hardware. (2) We enable end-to-end training, reducing pretraining computation without sacrificing model performance. As shown in Figure 1, experiments show the model pretrained with NSA maintains or exceeds Full Attention models across general benchmarks, long-context tasks, and instruction-based reasoning. Meanwhile, NSA achieves substantial speedups over Full Attention on 64k-length sequences across decoding, forward propagation, and backward propagation, validating its efficiency throughout the model lifecycle.

cs.CL↗

arXiv2025

Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling

In this work, we introduce Janus-Pro, an advanced version of the previous work Janus. Specifically, Janus-Pro incorporates (1) an optimized training strategy, (2) expanded training data, and (3) scaling to larger model size. With these improvements, Janus-Pro achieves significant advancements in both multimodal understanding and text-to-image instruction-following capabilities, while also enhancing the stability of text-to-image generation. We hope this work will inspire further exploration in the field. Code and models are publicly available.

cs.AI↗

arXiv2024

DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

We present DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL, through two key major upgrades. For the vision component, we incorporate a dynamic tiling vision encoding strategy designed for processing high-resolution images with different aspect ratios. For the language component, we leverage DeepSeekMoE models with the Multi-head Latent Attention mechanism, which compresses Key-Value cache into latent vectors, to enable efficient inference and high throughput. Trained on an improved vision-language dataset, DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Our model series is composed of three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively. DeepSeek-VL2 achieves competitive or state-of-the-art performance with similar or fewer activated parameters compared to existing open-source dense and MoE-based models. Codes and pre-trained models are publicly accessible at https://github.com/deepseek-ai/DeepSeek-VL2.

cs.CV↗

arXiv2024

High-gain optical parametric amplification with a continuous-wave pump using a domain-engineered thin-film lithium niobate waveguide

While thin film lithium niobate (TFLN) is known for efficient signal generation, on-chip signal amplification remains challenging from fully integrated optical communication circuits. Here we demonstrate the continuous-wave-pump optical parametric amplification (OPA) using an x-cut domain-engineered TFLN waveguide, with high gain over the telecom band up to 13.9 dB, and test it for high signal-to-noise ratio signal amplification using a commercial optical communication module pair. Fabricated in wafer scale using common process as devices including modulators, this OPA device marks an important step in TFLN photonic integration.

physics.optics↗

arXiv2024

JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation

We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. JanusFlow introduces a minimalist architecture that integrates autoregressive language models with rectified flow, a state-of-the-art method in generative modeling. Our key finding demonstrates that rectified flow can be straightforwardly trained within the large language model framework, eliminating the need for complex architectural modifications. To further improve the performance of our unified model, we adopt two key strategies: (i) decoupling the understanding and generation encoders, and (ii) aligning their representations during unified training. Extensive experiments show that JanusFlow achieves comparable or superior performance to specialized models in their respective domains, while significantly outperforming existing unified approaches across standard benchmarks. This work represents a step toward more efficient and versatile vision-language models.

cs.CV↗

arXiv2024

Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation

In this paper, we introduce Janus, an autoregressive framework that unifies multimodal understanding and generation. Prior research often relies on a single visual encoder for both tasks, such as Chameleon. However, due to the differing levels of information granularity required by multimodal understanding and generation, this approach can lead to suboptimal performance, particularly in multimodal understanding. To address this issue, we decouple visual encoding into separate pathways, while still leveraging a single, unified transformer architecture for processing. The decoupling not only alleviates the conflict between the visual encoder's roles in understanding and generation, but also enhances the framework's flexibility. For instance, both the multimodal understanding and generation components can independently select their most suitable encoding methods. Experiments show that Janus surpasses previous unified model and matches or exceeds the performance of task-specific models. The simplicity, high flexibility, and effectiveness of Janus make it a strong candidate for next-generation unified multimodal models.

cs.CV↗

arXiv2024

DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence

We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K. In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks.

cs.SE↗

arXiv2024

DeepSeek-VL: Towards Real-World Vision-Language Understanding

We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead. This design choice ensures the model's ability to capture critical semantic and detailed information across various visual tasks. We posit that a proficient Vision-Language Model should, foremost, possess strong language abilities. To ensure the preservation of LLM capabilities during pretraining, we investigate an effective VL pretraining strategy by integrating LLM training from the beginning and carefully managing the competitive dynamics observed between vision and language modalities. The DeepSeek-VL family (both 1.3B and 7B models) showcases superior user experiences as a vision-language chatbot in real-world applications, achieving state-of-the-art or competitive performance across a wide range of visual-language benchmarks at the same model size while maintaining robust performance on language-centric benchmarks. We have made both 1.3B and 7B models publicly accessible to foster innovations based on this foundation model.

cs.AI↗

arXiv2024

Miniature narrow-linewidth 1 μm Laser

Self-injection locking scheme has the potential to narrow the linewidth of lasers in a compact setup. Here, we report a narrow linewidth laser source near 1 μm by self-injection locking scheme using a Fabry-Perot (FP) hollow resonator with a high-quality factor (Q>10^8). The measured fundamental linewidth of the laser is 41 Hz, and a coarse tuning range over 5.5 nm is achieved by changing the driving current of the laser source. Meanwhile, a fine-tuning range of 373 MHz is achieved without mode hops by changing the voltage applied to the PZT on the resonator. More importantly, benefiting from the low thermal refractive noise and low thermal expansion of the FP hollow resonator, the beat-note linewidth and the frequency Allan deviation are measured to be 510.3 Hz in and 10^-11 (1s averaging time), respectively, by using a fully stabilized frequency comb as reference. Such a high-performance laser is fully integrated with a palm-sized package (52.3 mL) for field-deployable applications.

physics.optics↗

arXiv2024

DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

The rapid development of large language models has revolutionized code intelligence in software development. However, the predominance of closed-source models has restricted extensive research and development. To address this, we introduce the DeepSeek-Coder series, a range of open-source code models with sizes from 1.3B to 33B, trained from scratch on 2 trillion tokens. These models are pre-trained on a high-quality project-level code corpus and employ a fill-in-the-blank task with a 16K window to enhance code generation and infilling. Our extensive evaluations demonstrate that DeepSeek-Coder not only achieves state-of-the-art performance among open-source code models across multiple benchmarks but also surpasses existing closed-source models like Codex and GPT-3.5. Furthermore, DeepSeek-Coder models are under a permissive license that allows for both research and unrestricted commercial use.

cs.SE↗

arXiv2024

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up model parameters. However, conventional MoE architectures like GShard, which activate the top-$K$ out of $N$ experts, face challenges in ensuring expert specialization, i.e. each expert acquires non-overlapping and focused knowledge. In response, we propose the DeepSeekMoE architecture towards ultimate expert specialization. It involves two principal strategies: (1) finely segmenting the experts into $mN$ ones and activating $mK$ from them, allowing for a more flexible combination of activated experts; (2) isolating $K_s$ experts as shared ones, aiming at capturing common knowledge and mitigating redundancy in routed experts. Starting from a modest scale with 2B parameters, we demonstrate that DeepSeekMoE 2B achieves comparable performance with GShard 2.9B, which has 1.5 times the expert parameters and computation. In addition, DeepSeekMoE 2B nearly approaches the performance of its dense counterpart with the same number of total parameters, which set the upper bound of MoE models. Subsequently, we scale up DeepSeekMoE to 16B parameters and show that it achieves comparable performance with LLaMA2 7B, with only about 40% of computations. Further, our preliminary efforts to scale up DeepSeekMoE to 145B parameters consistently validate its substantial advantages over the GShard architecture, and show its performance comparable with DeepSeek 67B, using only 28.5% (maybe even 18.2%) of computations.

cs.CL↗

arXiv2024

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5.

cs.CL↗