arXiv subjects

Zihao Gong

Publications and source records attributed to Zihao Gong.

10 recordsLinked to original sources

arXiv2026

No-Go Theorem for Ancilla-Assisted Gaussian Enhancement in Passive-Unitary Estimation

We study the maximum quantum Fisher information (QFI) for estimating a single parameter embedded in a generic multimode lossless passive Gaussian unitary using general Gaussian probes under a signal-energy constraint. Unlike previous work, which imposed a total energy constraint on the full probe, we constrain only the transmitted signal modes while allowing an arbitrary number of locally retained ancilla modes with arbitrarily large energy. We prove that this additional freedom does not increase the maximum achievable QFI; the optimum remains identical to that attainable without extra ancilla energy. The same conclusion also extends to the sequential setting under a total energy constraint. We also characterize the family of optimal probe states and show that entanglement is not necessary to attain the optimum in the lossless setting. This extends the result of Matsubara et al. to the physically motivated signal-energy-constrained scenario and establishes a no-go theorem for ancilla-assisted Gaussian enhancement in noiseless passive-unitary estimation.

quant-ph↗

arXiv2026

Two-stage Quantum Estimation and the Asymptotics of Quantum-enhanced Transmittance Sensing

We consider estimation of a single unknown parameter embedded in a quantum state. Quantum Cramér-Rao bound (QCRB) is the ultimate limit of the mean squared error for any unbiased estimator. While it can be achieved asymptotically for a large number of quantum state copies, the measurement required often depends on the true value of the parameter of interest. Prior work addresses this paradox using a two-stage approach: in the first stage, a preliminary estimate is obtained by applying, on a vanishing fraction of quantum state copies, a sub-optimal measurement that does not depend on the parameter of interest. In the second stage, the preliminary estimate is used to construct the QCRB-achieving measurement that is applied to the remaining quantum state copies. This is akin to two-step estimators for classical problems with nuisance parameters. Unfortunately, the original analysis imposes conditions that severely restrict the class of classical estimators applied to the quantum measurement outcomes, hindering applications of this method. We relax these conditions to substantially broaden the class of usable estimators for single-parameter problems at the cost of slightly weakening the asymptotic properties of the two-stage method. We also account for nuisance parameters. We apply our results to obtain the asymptotics of quantum-enhanced transmittance sensing.

quant-ph↗

arXiv2026

Quantum-enhanced Network Tomography

Network tomography refers to the use of inference techniques for inferring internal network states from end-to-end probes. Quantum probes, implemented by sending blocks of $n$ coherent-state pulses augmented with continuous-variable (CV) squeezing ($n=1$) or weak temporal-mode entanglement ($n>1$) over a lossy channel to a receiver with homodyne detection capabilities, are known to carry information about the channel transmissivity. Assuming a subset of nodes in an optical network is capable of sending and receiving such probes through intermediate nodes with all-optical switching capabilities, we leverage these quantum probes to estimate link transmissivities. To determine how to route the probes in a network, we propose a probe construction algorithm that guarantees link identifiability, while maximizing the number of information orthogonal sets of transmissivities. A set of probes induces a Fisher information matrix (FIM). We then derive two metrics, the determinant of the FIM and the trace of its inverse, to evaluate the performance of the probes. In particular, our results can be used to characterize the quantum improvement in estimating link transmissivities in a general optical network.

quant-ph↗

arXiv2026

Quantum-Enhanced Change Detection and Joint Communication-Detection

Quick detection of transmittance changes in optical channel is crucial for secure communication. We demonstrate that pre-shared entanglement using two-mode squeezed vacuum states significantly reduces detection latency compared to classical and entanglement-augmented coherent-state probes. The change detection latency is inversely proportional to the quantum relative entropy (QRE), which goes to infinity in the absence of thermal noise, suggesting idealized instantaneous detection. However, in realistic scenarios, we show that QRE scales logarithmically with the inverse of the thermal noise mean photon number. We propose a receiver that achieves this scaling and quantify its performance gains over existing methods. Additionally, we explore the fundamental trade-off between communication capacity and change detection latency, highlighting how pre-shared entanglement enhances both.

quant-ph↗

arXiv2025

Quantum-enhanced quickest change detection of transmission loss

Augmenting a train of bright phase-modulated laser-light pulses of a coherent communications system with infinitesimally small quantum photons per pulse -- entangled across several time bins -- prepared by splitting squeezed light in a temporal-mode interferometer can dramatically enhance a homodyne receiver's ability to detect a sudden change in the channel loss, by up to a factor that is the inverse of the pre-change loss, without affecting the communications rate. We discuss the quantum limit of quickest change detection, and the problem of joint communications and change detection that our study opens up.

quant-ph↗

arXiv2025

Incoherent Imaging with Spatially Structured Quantum Probes

Incoherent imaging, including fluorescence and absorption microscopy, is often limited by weak signals and resolution constraints -- notoriously, Rayleigh's curse. We investigate how spatially structured quantum probes, combined with quantum detection strategies like spatial mode demultiplexing and photon counting, overcome these limitations. We propose a novel imaging protocol based on twin-beam echoes that maps the generalized incoherent-imaging model -- comprising both absorption and fluorescence -- onto distinct passive imaging channels that separately encode the absorption and fluorescence signatures. This enables (i) simultaneous absorption and fluorescence imaging and (ii) direct application of well-known results from passive imaging, all featuring quantum-enhanced measurement sensitivity. Remarkably, the same protocol supports displacement-field reconstruction of multiple quadratures (e.g., oscillators' positions) and works for both conventional and subdiffraction imaging, thereby functioning as a universal quantum imaging module. We also examine the utility of Fock states in a structured spatial mode basis, which offer comparable performance in principle. Though developed for optical imaging, our framework applies broadly to quantum-optical microscopy, phononic or acoustic imaging, and mapping stochastic forces, fields, or charge distributions using an array of mechanical oscillators.

quant-ph↗

arXiv2024

Motor Focus: Fast Ego-Motion Prediction for Assistive Visual Navigation

Assistive visual navigation systems for visually impaired individuals have become increasingly popular thanks to the rise of mobile computing. Most of these devices work by translating visual information into voice commands. In complex scenarios where multiple objects are present, it is imperative to prioritize object detection and provide immediate notifications for key entities in specific directions. This brings the need for identifying the observer's motion direction (ego-motion) by merely processing visual information, which is the key contribution of this paper. Specifically, we introduce Motor Focus, a lightweight image-based framework that predicts the ego-motion - the humans (and humanoid machines) movement intentions based on their visual feeds, while filtering out camera motion without any camera calibration. To this end, we implement an optical flow-based pixel-wise temporal analysis method to compensate for the camera motion with a Gaussian aggregation to smooth out the movement prediction area. Subsequently, to evaluate the performance, we collect a dataset including 50 clips of pedestrian scenes in 5 different scenarios. We tested this framework with classical feature detectors such as SIFT and ORB to show the comparison. Our framework demonstrates its superiority in speed (> 40FPS), accuracy (MAE = 60pixels), and robustness (SNR = 23dB), confirming its potential to enhance the usability of vision-based assistive navigation tools in complex environments.

cs.CV↗

arXiv2024

VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation

This paper explores the potential of Large Language Models(LLMs) in zero-shot anomaly detection for safe visual navigation. With the assistance of the state-of-the-art real-time open-world object detection model Yolo-World and specialized prompts, the proposed framework can identify anomalies within camera-captured frames that include any possible obstacles, then generate concise, audio-delivered descriptions emphasizing abnormalities, assist in safe visual navigation in complex circumstances. Moreover, our proposed framework leverages the advantages of LLMs and the open-vocabulary object detection model to achieve the dynamic scenario switch, which allows users to transition smoothly from scene to scene, which addresses the limitation of traditional visual navigation. Furthermore, this paper explored the performance contribution of different prompt components, provided the vision for future improvement in visual accessibility, and paved the way for LLMs in video anomaly detection and vision-language understanding.

cs.CV↗

arXiv2024

LLM-based Smart Reply (LSR): Enhancing Collaborative Performance with ChatGPT-mediated Smart Reply System

Interactive user interfaces have increasingly explored AI's role in enhancing communication efficiency and productivity in collaborative tasks. The emergence of Large Language Models (LLMs) such as ChatGPT has revolutionized conversational agents, employing advanced deep learning techniques to generate context-aware, coherent, and personalized responses. Consequently, LLM-based AI assistants provide a more natural and efficient user experience across various scenarios. In this paper, we study how LLM models can be used to improve work efficiency in collaborative workplaces. Specifically, we present an LLM-based Smart Reply (LSR) system utilizing the ChatGPT to generate personalized responses in professional collaborative scenarios while adapting to context and communication style based on prior responses. Our two-step process involves generating a preliminary response type (e.g., Agree, Disagree) to provide a generalized direction for message generation, thus reducing response drafting time. We conducted an experiment where participants completed simulated work tasks involving a Dual N-back test and subtask scheduling through Google Calendar while interacting with co-workers. Our findings indicate that the proposed LSR reduces overall workload, as measured by the NASA TLX, and improves work performance and productivity in the N-back task. We also provide qualitative analysis based on participants' experiences, as well as design considerations to provide future directions for improving such implementations.

cs.HC↗

arXiv2022

Quantum-Enhanced Transmittance Sensing

We consider the problem of estimating unknown transmittance $θ$ of a target bathed in thermal background light. As quantum estimation theory yields the fundamental limits, we employ the lossy thermal-noise bosonic channel model, which describes sensor-target interaction quantum mechanically in many practical active-illumination systems (e.g., using emissions at optical, microwave, or radio frequencies). We prove that quantum illumination using two-mode squeezed vacuum (TMSV) states asymptotically achieves minimal quantum Cramér-Rao bound (CRB) over all quantum states (not necessarily Gaussian) in the limit of low transmitted power. We characterize the optimal receiver structure for TMSV input, and show its advantage over other receivers using both analysis and Monte Carlo simulation.

quant-ph↗