arXiv subjects

Li Qian

Publications and source records attributed to Li Qian.

At least 19 recordsLinked to original sources

arXiv2026

Proof-of-principle long-distance Sagnac twin-field quantum key distribution network

Twin-field (TF) quantum key distribution (QKD) offers a promising approach to long-distance QKD networks due to its superior performance over large channel losses. Due to specialized hardware requirements, nearly all long-distance TFQKD demonstrations have only two users exchanging keys, rather than a network with three or more users. In this work, we experimentally demonstrate a proof-of-principle three-user-pair Sagnac TFQKD network spanning 127-km using single-photon avalanche detectors without any active phase stabilization or postcompensation. We implement efficient procedures for maintaining polarization stability and circumventing Rayleigh backscattering noise to achieve a stable Sagnac interference visibility of $93\pm1$% over one hour. A secure key rate of $1.398\times10^{-5}$ bits per pulse is achieved over an asymmetric communication channel with 102-km fiber and 45-dB overall loss. To our knowledge, this is the first TFQKD network without active phase stabilization or postcompensation achieved over long fibers. Our results represent a highly practical and cost-effective approach to long-distance QKD networks.

quant-ph↗

arXiv2026

A Universal Entanglement Witness Generator

Entanglement witnesses are essential for certifying entanglement, yet constructing ones that are both noise-robust and economical in measurement settings remains challenging - particularly beyond qubits and for non-stabilizer ("magic") states. We present a machine-learning method that, given a target state and a user-specified number of measurement settings, generates an entanglement witness optimized for noise tolerance in the neighborhood of that state, requiring only local measurements. The approach is fully general, applying to multipartite qubit and qudit systems alike, including non-stabilizer states. For N qudits of dimension d, we train on the fully-separable eigenstates of each qudit's SU(d) generators to find a prototype witness, then tune the witness's bias term via gradient descent to maximize noise tolerance. Adversarial training further strengthens the witnesses, delivering greater noise tolerance with even fewer settings; critically, under this scheme the required training-set size becomes independent of system size. We package the entire pipeline as an automated script that, in every case we tested, produces witnesses surpassing all existing methods in noise tolerance and/or number of measurement settings. We demonstrate the method on Bell, GHZ, W, and hypergraph states, along with a range of qudit states, spanning 2-6 qubits, bipartite qudits up to d=10, and tripartite qutrits. Our witnesses achieve perfect accuracy across both physical experimental test states and large numerical sets of separable mixed states-including 30 million test states for a 3-qubit W-state witness and 10 million for a 4-qubit hypergraph-state witness-and we experimentally confirm the noise tolerance of Bell- and hypergraph state witnesses on both photonic and superconducting platforms, respectively.

quant-ph↗

arXiv2026

Re-examining the Role of State Texture in Gate Identification and Fixed-Point Resource Theories

A protocol for identifying controlled-NOT (CNOT) gates versus single-qubit-only gates in universal quantum circuits using randomized input states was recently shown to be intimately connected to the quantum resource of state texture. Here we revisit this gate identification protocol and demonstrate that a more general fidelity-based formulation succeeds for nearly all laboratory bases. We then examine a broader family of quantum resource theories, where a distinct resource theory can be defined for each choice of reference pure state, establishing core resource-theoretic requirements without the computational shortcut offered by the "grand sum" employed in the original formulation of state texture. By extending from single "resourceless" states to convex sets via a convex-roof construction, we recover single-qubit measures of known resource theories such as imaginarity and coherence. Finally, we introduce a family of "fixed-point resource theories" that includes fixed-point instances of the theories of state texture, genuine coherence, purity, and athermality. For these fixed-point resource theories we show that, under free operations, the fidelity-based lower bound is weakly monotonic, while specific violations of strong monotonicity are found for the convex-roof logarithmic measure.

quant-ph↗

arXiv2025

LightSearcher: Efficient DeepSearch via Experiential Memory

DeepSearch paradigms have become a core enabler for deep reasoning models, allowing them to invoke external search tools to access up-to-date, domain-specific knowledge beyond parametric boundaries, thereby enhancing the depth and factual reliability of reasoning. Building upon this foundation, recent advances in reinforcement learning (RL) have further empowered models to autonomously and strategically control search tool usage, optimizing when and how to query external knowledge sources. Yet, these RL-driven DeepSearch systems often reveal a see-saw trade-off between accuracy and efficiency-frequent tool invocations can improve factual correctness but lead to unnecessary computational overhead and diminished efficiency. To address this challenge, we propose LightSearcher, an efficient RL framework that incorporates textual experiential memory by learning contrastive reasoning trajectories to generate interpretable summaries of successful reasoning patterns. In addition, it employs an adaptive reward shaping mechanism that penalizes redundant tool calls only in correct-answer scenarios. This design effectively balances the inherent accuracy-efficiency trade-off in DeepSearch paradigms. Experiments on four multi-hop QA benchmarks show that LightSearcher maintains accuracy comparable to SOTA baseline ReSearch, while reducing search tool invocations by 39.6%, inference time by 48.6%, and token consumption by 21.2%, demonstrating its superior efficiency.

cs.AI↗

arXiv2025

C$^2$-Cite: Contextual-Aware Citation Generation for Attributed Large Language Models

The attribution technique enhances the credibility of LLMs by adding citations to the generated sentences, enabling users to trace back to the original sources and verify the reliability of the output. However, existing instruction-tuned attributed LLMs often fail to properly interpret the contextual semantics of citation symbols (e.g., [i]) during text generation. This shortcoming arises from their insufficient awareness of the context information surrounding citation markers, which in turn leads to disjointed references and poor integration of retrieved knowledge into the generated content. To address this issue, we propose a novel \textbf{C}ontextual-aware \textbf{C}itation generation framework (\textbf{C$^2$}-\textbf{Cite}) that explicitly integrates the semantic relationships between citation markers and their referenced content. Specifically, a contextual citation alignment mechanism is adopted: it first encodes the retrieved document contexts into the symbol representation of citations, then aligns the marker numbers by decoding information from a citation router function. This mechanism enables the transformation of citation markers from generic placeholders into active knowledge pointers that link to the referenced source information. Experimental results on the ALCE benchmark across three datasets validate our framework C$^2$-Cite++: it outperforms the SOTA baseline by an average of 5.8\% in citation quality and 17.4\% in response correctness. The implementation is publicly available at https://github.com/BAI-LAB/c2cite

cs.IR↗

arXiv2025

Material Synthesis 2025 (MatSyn25) Dataset for 2D Materials

Two-dimensional (2D) materials have shown broad application prospects in fields such as energy, environment, and aerospace owing to their unique electrical, mechanical, thermal and other properties. With the development of artificial intelligence (AI), the discovery and design of novel 2D materials have been significantly accelerated. However, due to the lack of basic theories of material synthesis, identifying reliable synthesis processes for theoretically designed materials is a challenge. The emergence of large language model offers new approaches for the reliability prediction of material synthesis processes. However, its development is limited by the lack of publicly available datasets of material synthesis processes. To address this, we present the Material Synthesis 2025 (MatSyn25), a large-scale open dataset of 2D material synthesis processes. MatSyn25 contains 163,240 pieces of synthesis process information extracted from 85,160 high-quality research articles, each including basic material information and detailed synthesis process steps. Based on MatSyn25, we developed MatSyn AI which specializes in material synthesis, and provided an interactive web platform that enables multifaceted exploration of the dataset (https://matsynai.stpaper.cn/). MatSyn25 is publicly available, allowing the research community to build upon our work and further advance AI-assisted materials science.

cond-mat.mtrl-sci↗

arXiv2025

Mitigating Phase Correlations in Quantum Key Distribution Using Path-Selection Modulation

Phase correlations are an under-explored vulnerability in QKD. Here, we present an experimental and simulated characterization of correlations arising from electro-optic phase encoding, over repetition rates up to the GHz level. To mitigate this vulnerability (and all side channels arising from active phase modulators), we propose a "path-selection modulation" source that eliminates the need for active phase modulation altogether. Encoding is achieved by randomly selecting between multiple paths, each path corresponding to one of the desired encoded states. Phase randomization is achieved using gain-switching. We characterize this source at a clock rate of 1 GHz.

quant-ph↗

arXiv2025

Random Party Distillation on a Superconducting Processor

Random party distillation refers to the process by which Einstein-Podolsky-Rosen pairs are probabilistically extracted from a single copy of a multipartite entangled state after multiple rounds of local operations and classical communications (LOCC). In this work, we propose a qubit-based implementation of a random party distillation protocol and demonstrate it on the superconducting hardware device, ibm_aachen. We implement up to 4-rounds of the protocol, achieving a distillation rate of ~0.85 pairs/ W state, surpassing previous experimental demonstrations. Our results demonstrate the utility of multi-round LOCC protocols in an experimental regime that remains comparatively unexplored.

quant-ph↗

arXiv2025

Trojan Horse Prompting: Jailbreaking Conversational Multimodal Models by Forging Assistant Message

The rise of conversational interfaces has greatly enhanced LLM usability by leveraging dialogue history for sophisticated reasoning. However, this reliance introduces an unexplored attack surface. This paper introduces Trojan Horse Prompting, a novel jailbreak technique. Adversaries bypass safety mechanisms by forging the model's own past utterances within the conversational history provided to its API. A malicious payload is injected into a model-attributed message, followed by a benign user prompt to trigger harmful content generation. This vulnerability stems from Asymmetric Safety Alignment: models are extensively trained to refuse harmful user requests but lack comparable skepticism towards their own purported conversational history. This implicit trust in its "past" creates a high-impact vulnerability. Experimental validation on Google's Gemini-2.0-flash-preview-image-generation shows Trojan Horse Prompting achieves a significantly higher Attack Success Rate (ASR) than established user-turn jailbreaking methods. These findings reveal a fundamental flaw in modern conversational AI security, necessitating a paradigm shift from input-level filtering to robust, protocol-level validation of conversational context integrity.

cs.AI↗

arXiv2025

Investigation of Rare-Earth Ion-Photon Interaction and Strong Coupling in Optical Microcavities

The strong coupling between an emitter and a cavity is significant for advancing quantum networks. Due to their long optical and spin coherence times, rare-earth ions (REIs) represent a compelling platform for quantum networks. However, their inherently weak intra-4f optical transitions typically result in low coupling strength, thus restricting most current achievements to the weak coupling regime. This work proposes a scheme to realize an on-chip quantum network by coupling REIs to high-quality whispering gallery mode (WGM) microcavities. Additionally, we provide numerical validation for a parametric amplification technique to enhance the emitter-cavity coupling strength. As an extension of this approach, the coupled system efficiently achieves the quantum entanglement of local and flying qubits. This study deepens the understanding of emitter-cavity interactions and contributes to realizing REIs-based photonic platforms, which are crucial to distributed quantum computing and developing robust quantum networks.

quant-ph↗

arXiv2025

CITYWALK: Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge

Unit testing plays a pivotal role in the software development lifecycle, as it ensures code quality. However, writing high-quality unit tests remains a time-consuming task for developers in practice. More recently, the application of large language models (LLMs) in automated unit test generation has demonstrated promising results. Existing approaches primarily focus on interpreted programming languages (e.g., Java), while mature solutions tailored to compiled programming languages like C++ are yet to be explored. The intricate language features of C++, such as pointers, templates, and virtual functions, pose particular challenges for LLMs in generating both executable and high-coverage unit tests. To tackle the aforementioned problems, this paper introduces CITYWALK, a novel LLM-based framework for C++ unit test generation. CITYWALK enhances LLMs by providing a comprehensive understanding of the dependency relationships within the project under test via program analysis. Furthermore, CITYWALK incorporates language-specific knowledge about C++ derived from project documentation and empirical observations, significantly improving the correctness of the LLM-generated unit tests. We implement CITYWALK by employing the widely popular LLM GPT-4o. The experimental results show that CITYWALK outperforms current state-of-the-art approaches on a collection of ten popular C++ projects. Our findings demonstrate the effectiveness of CITYWALK in generating high-quality C++ unit tests.

cs.SE↗

arXiv2024

SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model

Large language models (LLMs) have shown remarkable achievements across various language tasks.To enhance the performance of LLMs in scientific literature services, we developed the scientific literature LLM (SciLit-LLM) through pre-training and supervised fine-tuning on scientific literature, building upon the iFLYTEK Spark LLM. Furthermore, we present a knowledge service system Spark Research Assistant (SparkRA) based on our SciLit-LLM. SparkRA is accessible online and provides three primary functions: literature investigation, paper reading, and academic writing. As of July 30, 2024, SparkRA has garnered over 50,000 registered users, with a total usage count exceeding 1.3 million.

cs.CL↗

arXiv2024

Long-fiber Sagnac interferometers for twin field quantum key distribution networks

A Sagnac loop structure can help overcome the major difficulty in the practical implementation of a twin field quantum key distribution (TFQKD) network, namely, the need to stabilize the phase of a quantum state over many kilometers of fiber. Unfortunately, Rayleigh backscattering noise limits the signal-to-noise ratio for Sagnac systems containing long fibers and lossy photonic devices. Here, we solve this problem by sending optical pulses in long on-off bursts and using time post-selection on measurements taken with free-run single-photon avalanche detectors. We also investigate the impact of the residual phase noise uncompensated by the Sagnac structure and find that the variance of the phase noise scales as loop length to the third power, verifying an existing calculation in the literature. We measure the interference visibility in Sagnac loops of varying length without active phase stabilization and achieve > 97% visibility in 200 km ultra-low-loss fiber, which is, to our knowledge, the longest fiber Sagnac interferometer demonstrated. Our results suggest that a Sagnac system is feasible for long-distance TFQKD networks, an important step towards the practical implementation of metropolitan quantum networks.

quant-ph↗

arXiv2024

Hidden multi-dimensional modulation side channels in quantum protocols

Quantum protocols including quantum key distribution and blind quantum computing often require the preparation of quantum states of known dimensions. Here, we show that, rather surprisingly, hidden multi-dimensional modulation is often performed by practical devices. This violates the dimensional assumption in quantum protocols, thus creating side channels and security loopholes. Our work has important impacts on the security of quantum cryptographic protocols.

quant-ph↗

arXiv2023

An Exponential Reduction in Training Data Sizes for Machine Learning Derived Entanglement Witnesses

We propose a support vector machine (SVM) based approach for generating an entanglement witness that requires exponentially less training data than previously proposed methods. SVMs generate hyperplanes represented by a weighted sum of expectation values of local observables whose coefficients are optimized to sum to a positive number for all separable states and a negative number for as many entangled states as possible near a specific target state. Previous SVM-based approaches for entanglement witness generation used large amounts of randomly generated separable states to perform training, a task with considerable computational overhead. Here, we propose a method for orienting the witness hyperplane using only the significantly smaller set of states consisting of the eigenstates of the generalized Pauli matrices and a set of entangled states near the target entangled states. With the orientation of the witness hyperplane set by the SVM, we tune the plane's placement using a differential program that ensures perfect classification accuracy on a limited test set as well as maximal noise tolerance. For $N$ qubits, the SVM portion of this approach requires only $O(6^N)$ training states, whereas an existing method needs $O(2^{4^N})$. We use this method to construct witnesses of 4 and 5 qubit GHZ states with coefficients agreeing with stabilizer formalism witnesses to within 6.5 percent and 1 percent, respectively. We also use the same training states to generate novel 4 and 5 qubit W state witnesses. Finally, we computationally verify these witnesses on small test sets and propose methods for further verification.

quant-ph↗

arXiv2023

Entangling entanglement: coupling frequency and polarization of biphotons on demand

Quantum information is often carried in the frequency and polarization degrees of freedom (DoFs) in single photons and entangled photons. We demonstrate a new approach to couple and decouple the frequency and polarization DoFs of broadband biphotons. Our approach is based on a common-path nonlinear interferometer (CP-NLI) with a linear dispersive medium and a polarization controller sandwiched in between two nonlinear media that generate the interfering biphotons. By adjusting the polarization controller, we can effectively manipulate the two DoFs. When the two DoFs are decoupled, maximally polarization-entangled biphotons are observed in the polarization DoF, while interference fringes are observed in the spectral intensity of the biphotons. When the two DoFs are coupled, however, interference fringes disappear from the spectral intensity and instead appear in the degree of polarization entanglement. The degree of polarization entanglement quantified by concurrence in principle can vary from 0 to 1 depending on the signal and idler photon frequencies. Our approach offers a convenient means of tuning the polarization entanglement and can be employed for arbitrary biphoton polarization state generation, with applications in quantum information processing and the study of fundamental physics.

quant-ph↗

arXiv2023

UniMC: A Unified Framework for Long-Term Memory Conversation via Relevance Representation Learning

Open-domain long-term memory conversation can establish long-term intimacy with humans, and the key is the ability to understand and memorize long-term dialogue history information. Existing works integrate multiple models for modelling through a pipeline, which ignores the coupling between different stages. In this paper, we propose a Unified framework for Long-term Memory Conversations (UniMC), which increases the connection between different stages by learning relevance representation. Specifically, we decompose the main task into three subtasks based on probability graphs: 1) conversation summarization, 2) memory retrieval, 3) memory-augmented generation. Each subtask involves learning a representation for calculating the relevance between the query and memory, which is modelled by inserting a special token at the beginning of the decoder input. The relevance representation learning strengthens the connection across subtasks through parameter sharing and joint training. Extensive experimental results show that the proposed method consistently improves over strong baselines and yields better dialogue consistency and engagingness.

cs.CL↗

arXiv2023

Entanglement distillation based on polarization and frequency hyperentanglement

Entanglement distillation has many applications in quantum information processing and is an important tool for improving the quality and efficiency of quantum communication, cryptography, computing, and simulation. We propose an entanglement distillation scheme using only one pair of polarization-frequency hyperentangled photons, which can be equivalently viewed as containing two pairs of entangled logical qubits: a pair of polarization-entangled qubits and a pair of frequency-entangled qubits. To perform the required CNOT operation between the two qubits we consider the use of a polarization-dependent frequency converter. Compared to past methods of entanglement distillation that relied on polarization and spatial-mode/energy-time degree of freedom, the utilization of frequency-encoded qubits offers an advantage in that it is immune to bit-flip errors when the channel is linear. After distillation, the fidelity of polarization entanglement can be significantly improved by sacrificing the frequency degree of freedom. Through simulation, we show that high fidelity gains, large yield, and high distillation rate can be achieved. Our distillation scheme is simple to implement with current technologies, compatible with existing telecommunication fiber networks, and is a promising approach for achieving efficient quantum communication.

quant-ph↗