arXiv subjects

Rui Guan

Publications and source records attributed to Rui Guan.

10 recordsLinked to original sources

arXiv2025

Field-Trial Quantum Key Distribution with Qubit-Based Frame Synchronization

Quantum key distribution (QKD) is a cryptographic technique that uses quantum mechanical principles to enable secure key exchange. Practical deployment of QKD requires robust, cost-effective systems that can operate in challenging field environments. A major challenge is achieving reliable clock synchronization without adding hardware complexity. Conventional approaches often use separate classical light signals, which increase costs and introduce noise that degrades quantum channel performance. To address this limitation, we demonstrate a QKD system incorporating a recently proposed qubit-based distributed frame synchronization method, deployed over a metropolitan fiber network in Nanning, China. Using the polarization-encoded one-decoy-state BB84 protocol and the recently proposed qubit-based distributed frame synchronization method, our system achieves synchronization directly from the quantum signal, eliminating the need for dedicated synchronization hardware. Furthermore, to counteract dynamic polarization disturbances in urban fibers, the system integrates qubit-based polarization feedback control, enabling real-time polarization compensation through an automated polarization controller using data recovered from the qubit-based synchronization signals. During 12 hours of continuous operation, the system maintained a low average quantum bit error rate (QBER) of 1.12/%, achieving a secure key rate of 26.6 kbit/s under 18 dB channel loss. Even under a high channel loss of 40 dB, a finite-key secure rate of 115 bit/s was achieved. This study represents the first successful long-term validation of a frame-synchronization based QKD scheme in a real urban environment, demonstrating exceptional stability and high-loss tolerance, and offering an alternative for building practical, scalable, and cost-efficient quantum-secure communication networks.

quant-ph↗

arXiv2025

Dean of LLM Tutors: A Framework for Automated Quality Review of AI-generated Feedback

Large language model (LLM) tutors are increasingly used to generate educational feedback, but existing research has focused mainly on feedback generation rather than feedback evaluation. As a result, LLM-generated feedback may offer limited pedagogical value and carry risks of hallucination. The current study introduces DeanLLM, an automated review framework for comprehensively evaluating feedback generated by LLM tutors before it is shared with students. We developed a 16-dimension evaluation framework covering feedback content, educational effectiveness, and hallucination risks, and validated it using using human-expert annotations of LLM-generated tutor feedback on synthetic computer science assignment submissions derived from real coursework. We then examined whether LLMs could serve as automated LLM-generated tutor feedback reviewers, and used the best-performing reviewer to benchmark tutor feedback generated by 10 commercial LLMs. Psychometric analyses supported the reliability of the proposed framework and showed that human reviewers tended to evaluate feedback holistically, whereas the LLM reviewer separated rubric dimensions more mechanically. Standard zero-shot and few-shot prompting showed limited agreement with human experts for content-quality judgments. Supervised fine-tuning of GPT-4.1 with human-labelled examples containing scores only, without explanatory rationales, achieved the strongest alignment with expert judgments. Reasoning LLMs were particularly effective at hallucination detection and produced automated tutor feedback with stronger educational effectiveness and factuality than lightweight models. The findings indicate that DeanLLM offers a scalable way for automatically improving the reliability and safety of LLM tutor feedback, while also demonstrating that reviewer calibration and model choice remain critical for educational deployment.

cs.CY↗

arXiv2025

SCALEFeedback: A Large-Scale Dataset of Synthetic Computer Science Assignments for LLM-generated Educational Feedback Research

Using Large Language Models (LLMs) to give educational feedback to students for their assignments has attracted much attention in the AI in Education (AIED) field. Yet, there is currently no large-scale open-source dataset of student assignments that includes detailed assignment descriptions, rubrics, and student submissions across various courses. As a result, research on generalisable methodology for automatic generation of effective and responsible educational feedback remains limited. In this paper, we introduce a synthetic computer science university assignment dataset for LLM-based educational feedback research, called SCALEFeedback (Synthetic Computer science Assignments for LLM Educational Feedback Research). The dataset is generated via Sophisticated Assignment Mimicry (SAM) framework specifically designed to synthesise this dataset and that utilizes one-to-one LLM-based imitation from real assignment descriptions, rubrics, and student submissions. Our open-source dataset contains 10,000 synthetic student submissions spanning 155 assignments across 59 university-level computer science courses. Technical validation confirmed that the synthetic dataset closely resembles real data while successfully eliminating personally identifiable information present in the source material. The creation of this dataset is a valuable contribution to researchers who aim to develop LLM-based generalisable methods for offering high-quality, automated educational feedback in a scalable way.

cs.CY↗

arXiv2025

Towards Reliable Generative AI-Driven Scaffolding: Reducing Hallucinations and Enhancing Quality in Self-Regulated Learning Support

Generative Artificial Intelligence (GenAI) holds a potential to advance existing educational technologies with capabilities to automatically generate personalised scaffolds that support students' self-regulated learning (SRL). While advancements in large language models (LLMs) promise improvements in the adaptability and quality of educational technologies for SRL, there remain concerns about the hallucinations in content generated by LLMs, which can compromise both the learning experience and ethical standards. To address these challenges, we proposed GenAI-enabled approaches for evaluating personalised SRL scaffolds before they are presented to students, aiming for reducing hallucinations and improving the overall quality of LLM-generated personalised scaffolds. Specifically, two approaches are investigated. The first approach involved developing a multi-agent system approach for reliability evaluation to assess the extent to which LLM-generated scaffolds accurately target relevant SRL processes. The second approach utilised the "LLM-as-a-Judge" technique for quality evaluation that evaluates LLM-generated scaffolds for their helpfulness in supporting students. We constructed evaluation datasets, and compared our results with single-agent LLM systems and machine learning approach baselines. Our findings indicate that the reliability evaluation approach is highly effective and outperforms the baselines, showing almost perfect alignment with human experts' evaluations. Moreover, both proposed evaluation approaches can be harnessed to effectively reduce hallucinations. Additionally, we identified and discussed bias limitations of the "LLM-as-a-Judge" technique in evaluating LLM-generated scaffolds. We suggest incorporating these approaches into GenAI-powered personalised SRL scaffolding systems to mitigate hallucination issues and improve the overall scaffolding quality.

cs.CY↗

arXiv2025

Anomalous flow in correlated quantum systems: No-go result and multiple-charge scenario

Correlated quantum systems can exhibit thermodynamic behaviors that defy classical expectations, with anomalous energy flow (AEF) against temperature gradients serving as a paradigmatic example. While AEF has been shown to arise from the consumption of initial quantum correlations, little is known about whether AEF can occur without correlation depletion, or if analogous anomalous transport exists for conserved quantities--dubbed charges--other than energy. Here, we develop a general global-local thermodynamic approach to describe charge exchange between arbitrary correlated quantum systems. For energy-conserving systems, we analytically rule out AEF in initially uncorrelated states, even with the involvement of quantum catalysts, thereby complementing existing studies. In contrast, in systems with multiple conserved charges, we uncover a mechanism for AEF that requires no initial correlations but is instead induced by a drag effect from normal flows of non-energy charges. Furthermore, by treating all conserved charges on equal footing, we generalize AEF to a broader concept of anomalous charge flow, applicable to any conserved charge. We confirm theoretical expectations with numerical examples. These findings deepen our understanding of nonequilibrium quantum thermodynamics and open new avenues for controlling transport phenomena in correlated quantum systems.

quant-ph↗

arXiv2025

Global-Local Duality of Energetic Control Cost in Multipartite Quantum Correlated Systems

Multipartite quantum correlated systems (MQCSs) are widely utilized in diverse quantum information tasks, where their sophisticated control inherently incurs energetic costs. However, the fundamental characteristics of these control costs remain elusive, largely due to the lack of thermodynamic descriptions capable of capturing the full complexities of MQCSs. Here, we uncover universal thermodynamic relations for arbitrary MQCSs weakly coupled to a thermal bath, establishing an intrinsic global-local duality of control costs. Using these relations, we elucidate the exact role of multipartite correlation--a defining quantum feature of MQCSs--in shaping control costs at finite times. We also demonstrate that the relative magnitude between global and local control costs is undetermined, which perplexes the cost management of MQCSs under finite-time controls. Our results are numerically corroborated with applications to experimentally realizable multi-qubit systems undergoing finite-time qubit reset processes.

quant-ph↗

arXiv2025

Scalable twin-field quantum key distribution network enabled by adaptable architecture

Quantum key distribution (QKD) is a key application in quantum communication, enabling secure key exchange between parties using quantum states. Twin-field (TF) QKD offers a promising solution that surpasses the repeaterless limits, and its measurement-device-independent nature makes it suitable for star-type network architectures. In this work, we propose a scalable TF-QKD network with adaptable architecture, where users prepare quantum signals and send them to network nodes. These nodes use an optical switch to route the signals to multi-user measurement units, enabling secure key distribution among arbitrary users and adapting to complex connection demands of the network. A proof-of-principle demonstration with three users successfully achieved secure key sharing over simulated link losses of up to $30$ dB, with an average rate of $19.57$ bit/s. Additionally, simulations show that the proposed architecture can achieve a total secure key rate of $4.84 \times 10^{4}$ bit/s at $100$ km in a symmetric $32$-user network. This approach represents a significant advancement in the topology of untrusted-node QKD networks and holds promise for practical, large-scale applications in secure communication.

quant-ph↗

arXiv2025

Realizing degree sequences with $\mathcal S_3$-connected graphs

A graph $G$ is $\mathcal S_3$-connected if, for any mapping $\beta : V (G) \mapsto {\mathbb Z}_3$ with $\sum_{v\in V(G)} \beta(v)\equiv 0\pmod3$, there exists a strongly connected orientation $D$ satisfying $d^{+}_D(v)-d^{-}_D(v)\equiv \beta(v)\pmod{3}$ for any $v \in V(G)$. It is known that $\mathcal S_3$-connected graphs are contractible configurations for the property of flow index strictly less than three. In this paper, we provide a complete characterization of graphic sequences that have an $\mathcal{S}_{3}$-connected realization: A graphic sequence $\pi=(d_1,\, \ldots,\, d_n )$ has an $\mathcal S_3$-connected realization if and only if $\min \{d_1,\, \ldots,\, d_n\} \ge 4$ and $\sum^n_{i=1}d_i \ge 6n - 4$. Consequently, every graphic sequence $\pi=(d_1,\, \ldots,\, d_n )$ with $\min \{d_1,\, \ldots,\, d_n\} \ge 6$ has a realization $G$ with flow index strictly less than three. This supports a conjecture of Li, Thomassen, Wu and Zhang [European J. Combin., 70 (2018) 164-177] that every $6$-edge-connected graph has flow index strictly less than three.

math.CO↗

arXiv2024

Experimental secure entanglement-free quantum remote sensing over 50 km of optical fiber

Secure quantum remote sensing (SQRS) uses quantum states to gather information about distant objects or environments while ensuring secure data transmission against eavesdropping. It has potential applications in various fields, including environmental monitoring, military surveillance, and disaster response, where both data accuracy and transmission security are critical. Recent experiments have demonstrated the feasibility of SQRS using entanglement states. Here, we experimentally demonstrate an SQRS that can estimate a phase without requiring entanglement, offering the practical advantage that single-qubit states are easier to prepare. We successfully estimate the preset phase information at a remote site over a fiber distance of 50 km, which serves as a key step toward long-distance applications.

quant-ph↗

arXiv2024

Self-regulated Learning Processes in Secondary Education: A Network Analysis of Trace-based Measures

While the capacity to self-regulate has been found to be crucial for secondary school students, prior studies often rely on self-report surveys and think-aloud protocols that present notable limitations in capturing self-regulated learning (SRL) processes. This study advances the understanding of SRL in secondary education by using trace data to examine SRL processes during multi-source writing tasks, with higher education participants included for comparison. We collected fine-grained trace data from 66 secondary school students and 59 university students working on the same writing tasks within a shared SRL-oriented learning environment. The data were labelled using Bannert's validated SRL coding scheme to reflect specific SRL processes, and we examined the relationship between these processes, essay performance, and educational levels. Using epistemic network analysis (ENA) to model and visualise the interconnected SRL processes in Bannert's coding scheme, we found that: (a) secondary school students predominantly engaged in three SRL processes -- Orientation, Re-reading, and Elaboration/Organisation; (b) high-performing secondary students engaged more in Re-reading, while low-performing students showed more Orientation process; and (c) higher education students exhibited more diverse SRL processes such as Monitoring and Evaluation than their secondary education counterparts, who heavily relied on following task instructions and rubrics to guide their writing. These findings highlight the necessity of designing scaffolding tools and developing teacher training programs to enhance awareness and development of SRL skills for secondary school learners.

cs.HC↗