arXiv subjects

Yuancheng Liu

Publications and source records attributed to Yuancheng Liu.

7 recordsLinked to original sources

arXiv2026

Correlation Geometry of Quantum Sensor Networks: Local-Global Information Flow and Local Privacy

Quantum sensor networks (QSN) typically encode N unknown parameters while targeting a single linear combination, rendering the N-1 remaining parameters as nuisance directions. To rigorously quantify estimation precision under such nuisances, we use the effective quantum Fisher information (EQFI) and establish a ``barrel-effect'' bottleneck: the global EQFI cannot exceed the weakest weighted local sensing capacity. To elucidate the information allocation mechanism underlying this bottleneck, we derive an exact local--global phase map that delineates how the trade-off between local and global EQFI depends dynamically on quantum correlations, and accordingly we identify concrete conditions for saturating the bottleneck bound. Notably, this geometric map uncovers a counterintuitive ``overcorrelated'' regime where excessive correlations actively degrade both local and global performance. Finally, we apply the phase map to intrinsic local privacy and identify the condition under which every local parameter is inaccessible while the desired global combination remains estimable. Overall, our work provides a principled methodology for engineering optimal network states in quantum sensing architectures.

quant-ph↗

arXiv2026

Informationally Complete Distributed Metrology Without a Shared Reference Frame

In quantum information processing, implementing arbitrary preparations and measurements on qubits necessitates precise information to identify a specific reference frame (RF). In space quantum communication and sensing, where a shared RF is absent, the interplay between locality and symmetry imposes fundamental restrictions on physical systems. A restriction on realizable unitary operations results in a no-go theorem prohibiting the extraction of locally encoded information in RF-independent distributed metrology. Here, we propose a reversed-encoding method applied to two copies of local-unitary-invariant network states. This approach circumvents the no-go theorem while simultaneously mitigating decoherence-like noise caused by RF misalignment, thereby enabling the complete recovery of the quantum Fisher information (QFI). Furthermore, we confirm local Bell-state measurements as an optimal strategy to saturate the QFI. Our findings pave the way for the field application of distributed quantum sensing, which is inherently subject to unknown RF misalignment and was previously precluded by the no-go theorem.

quant-ph↗

arXiv2025

CircuitSeer: Mining High-Quality Data by Probing Mathematical Reasoning Circuits in LLMs

Large language models (LLMs) have demonstrated impressive reasoning capabilities, but scaling their performance often relies on massive reasoning datasets that are computationally expensive to train on. Existing data selection methods aim to curate smaller, high-quality subsets but often rely on costly external models or opaque heuristics. In this work, we shift the focus from external heuristics to the model's internal mechanisms. We find that complex reasoning tasks consistently activate a sparse, specialized subset of attention heads, forming core reasoning circuits. Building on this insight, we propose CircuitSeer, a novel data selection method that quantifies the reasoning complexity of data by measuring its influence on these crucial circuits. Extensive experiments on 4 models and 9 datasets demonstrate CircuitSeer's superiority. Notably, fine-tuning Qwen2.5-Math-7B on just 10% of data selected by our method achieves a 1.4-point gain in average Pass@1 over training on the full dataset, highlighting its efficiency and effectiveness.

cs.AI↗

arXiv2025

Signals and Symptoms: ICS Attack Dataset From Railway Cyber Range

The prevalence of cyberattacks on Industrial Control Systems (ICS) has highlighted the necessity for robust security measures and incident response to protect critical infrastructure. This is prominent when Operational Technology (OT) systems undergo digital transformation by integrating with Information Technology (IT) systems to enhance operational efficiency, adaptability, and safety. To support analysts in staying abreast of emerging attack patterns, there is a need for ICS datasets that reflect indicators representative of contemporary cyber threats. To address this, we conduct two ICS cyberattack simulations to showcase the impact of trending ICS cyberattacks on a railway cyber range that resembles the railway infrastructure. The attack scenario is designed to blend trending attack trends with attack patterns observed from historical ICS incidents. The resulting evidence is collected as datasets, serving as an essential resource for cyberattack analysis. This captures key indicators that are relevant to the current threat landscape, augmenting the effectiveness of security systems and analysts to protect against ICS cyber threats.

cs.CR↗

arXiv2024

Invested and Potential Magic Resources in Measurement-Based Quantum Computation

Magic states and magic gates are crucial for achieving universal quantum computation, but important questions about how magic resources should be implemented to attain maximal quantum advantage have remained unexplored, especially in the context of measurement-based quantum computation (MQC). This work bridges the gap between MQC and the resource theory of magic by introducing the key concepts of "invested" and "potential" magic resources. The former quantifies the magic cost associated with MQC, serving as both a resource witness and a feasible upper bound for the practical realization, and is gate-order independent; The latter represents the maximal achievable magic resource in a given graph structure defining MQC. We utilize both concepts to analyze the quantum Fourier transform (QFT) and provide a fresh perspective on the universality of MQC, highlighting the crucial role of non-Pauli measurements in injecting magic. In particular, we theoretically prove that high-dimensional graphs can generate an exponential advantage of MQC compared to classical computing. We demonstrate experimentally our theoretical findings in a high-fidelity four-photon setup, surpassing conventional magic state injection (MSI) methods in both qubit efficiency and resource utilization. Our findings pave the way for future research exploring magic resource optimization and novel distillation schemes within the MQC framework, advancing fault-tolerant universal quantum computation.

quant-ph↗

arXiv2023

Cooling and work extraction under memory-assisted Markovian thermal processes

We investigate the limits on cooling and work extraction via Markovian thermal processes assisted by a finite-dimensional memory. Here the memory is a $d$-dimensional quantum system with trivial Hamiltonian and initially in a maximally mixed state. For cooling a qubit system, we consider two paradigms: cooling under coherent control and cooling under incoherent control. For both paradigms, we derive the optimal ground-state populations under the set of general thermal processes (TP) and the set of Markovian thermal processes (MTP), and we further propose memory-assisted protocols, which bridge the gap between the performances of TP and MTP. For the task of work extraction, we prove that when the target system is a qubit in the excited state the minimum extraction error achieved by TP can be approximated by Markovian thermal processes assisted by a large enough memory. Our results can bridge the performances of TP and MTP in thermodynamic tasks including cooling and work extraction.

quant-ph↗

arXiv2023

Using Large Language Models for Cybersecurity Capture-The-Flag Challenges and Certification Questions

The assessment of cybersecurity Capture-The-Flag (CTF) exercises involves participants finding text strings or ``flags'' by exploiting system vulnerabilities. Large Language Models (LLMs) are natural-language models trained on vast amounts of words to understand and generate text; they can perform well on many CTF challenges. Such LLMs are freely available to students. In the context of CTF exercises in the classroom, this raises concerns about academic integrity. Educators must understand LLMs' capabilities to modify their teaching to accommodate generative AI assistance. This research investigates the effectiveness of LLMs, particularly in the realm of CTF challenges and questions. Here we evaluate three popular LLMs, OpenAI ChatGPT, Google Bard, and Microsoft Bing. First, we assess the LLMs' question-answering performance on five Cisco certifications with varying difficulty levels. Next, we qualitatively study the LLMs' abilities in solving CTF challenges to understand their limitations. We report on the experience of using the LLMs for seven test cases in all five types of CTF challenges. In addition, we demonstrate how jailbreak prompts can bypass and break LLMs' ethical safeguards. The paper concludes by discussing LLM's impact on CTF exercises and its implications.

cs.AI↗