arXiv subjects

Guosheng Feng

Publications and source records attributed to Guosheng Feng.

7 recordsLinked to original sources

arXiv2026

Single-sideband-interference twin-field quantum key distribution without global phase locking

Twin-field quantum key distribution (TF QKD) can overcome the fundamental rate loss limit of repeaterless quantum links, but its practical deployment has long been hindered by the requirement of global phase locking between two independent lasers. By revisiting the fundamental principles of optical interference, this work reveals that interference in TF QKD inherently relies only on the instantaneous phase alignment of two independent optical pulses at the moment they temporally overlap, rather than on continuous global phase synchronization. Guided by this insight, we propose and demonstrate a single-sideband-interference TF-QKD protocol that eliminates global phase locking. Each user employs an IQ modulator to generate a weak single sideband as the quantum signal, while the intrinsically phase-correlated optical carrier propagates as a real-time phase reference. Carrier interference at the receiver enables real-time phase extraction and feedback compensation for the sidebands. Unlike prior no phase locking approaches requiring second- or microsecond-level coherence, in principle, our scheme reduces this requirement to nanoseconds. We achieve 98% interference visibility over 100.8 km fibre and secure key rates surpassing the PLOB bound in the high-loss regime, providing a simpler route towards practical long-distance quantum communication networks.

quant-ph↗

arXiv2026

Quantum compressed sensing

How many measurements are fundamentally required to capture a signal. Shannon's information theory established the bedrock of this question in 1948, the Nyquist Shannon theorem set the first answer, and compressed sensing (CS) rewrote it in 2006 by reducing the required measurement number to M = O(Klog(N/K)) for a K sparse signal. Here, we propose quantum compressed sensing (QCS), a paradigm that reframes signal acquisition as a unitary quantum evolution. By encoding high dimensional signal information into a single quantum probe state, then introducing domain-alignment evolution,a physically realizable unitary transformation that maps the sparse basis directly onto the measurement basis. QCS executes the support-set search at the quantum level without consuming measurement trials. The logarithmic penalty vanishes, compressing the required measurement number from the classical bound to M =O(K) and reducing reconstruction from ill posed optimization to linear estimation. We experimentally validate QCS using frequency and time domain sparse signals, confirming that the measurement number scales linearly with sparsity and decouples entirely from the signal dimension. Our work provides a physical pathway toward ultimate information acquisition efficiency, with broad implications for sensing, imaging, and communication.

quant-ph↗

arXiv2026

Quantum Compressed Sensing Enables Image Classification with a Single Photon

Image classification is a core task of intelligent sensing, conventionally follows a sequential imaging then processing pipeline. However, redundant high-dimensional image reconstruction is inherently inefficient, especially in photon limited scenarios. Here we report a photon level image classification method using quantum compressed sensing, which reformulates the classification task as a sparse signal measurement problem directly oriented toward class labels. By exploiting the parallelism of photonic quantum superposition states, a single photon can be encoded the complete spatial information of a high-dimensional image. Through a diffractive deep neural network, we physically construct a dedicated measurement basis aligned with the class space, enabling signal-dependent adaptive compressive measurement. Ideally, our method can extract class information via a single quantum projective measurement, reducing the required number of measurements from the logarithmic scaling O(Klog(N/K)) of classical compressed sensing to the constant-order information-theoretic limit M = K = 1. Experimental results show that a classification accuracy of 69.0% can be achieved by using a single-photon detection event as the decision criterion, while it increases to 95.0% with four-photon detection events. This work demonstrates image classification at the energy efficiency limit and introduces a measurement as decision framework. It provides a foundation for intelligent sensing systems that operate under extreme photon budgets and harsh environments.

quant-ph↗

arXiv2024

Edisum: Summarizing and Explaining Wikipedia Edits at Scale

An edit summary is a succinct comment written by a Wikipedia editor explaining the nature of, and reasons for, an edit to a Wikipedia page. Edit summaries are crucial for maintaining the encyclopedia: they are the first thing seen by content moderators and they help them decide whether to accept or reject an edit. Additionally, edit summaries constitute a valuable data source for researchers. Unfortunately, as we show, for many edits, summaries are either missing or incomplete. To overcome this problem and help editors write useful edit summaries, we propose a model for recommending edit summaries generated by a language model trained to produce good edit summaries given the representation of an edit diff. To overcome the challenges of mixed-quality training data and efficiency requirements imposed by the scale of Wikipedia, we fine-tune a small generative language model on a curated mix of human and synthetic data. Our model performs on par with human editors. Commercial large language models are able to solve this task better than human editors, but are not well suited for Wikipedia, while open-source ones fail on this task. More broadly, we showcase how language modeling technology can be used to support humans in maintaining one of the largest and most visible projects on the Web.

cs.CL↗

arXiv2021

Search For Deep Graph Neural Networks

Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem. To further explore the benefits from structural diversity and depth of GNN architectures, we propose a GNN generation pipeline with a novel two-stage search space, which aims at automatically generating high-performance while transferable deep GNN models in a block-wise manner. Meanwhile, to alleviate the 'over-smooth' problem, we incorporate multiple flexible residual connection in our search space and apply identity mapping in the basic GNN layers. For the search algorithm, we use deep-q-learning with epsilon-greedy exploration strategy and reward reshaping. Extensive experiments on real-world datasets show that our generated GNN models outperforms existing manually designed and NAS-based ones.

cs.LG↗

arXiv2020

Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation

The search space of neural architecture search (NAS) for convolutional neural network (CNN) is huge. To reduce searching cost, most NAS algorithms use fixed outer network level structure, and search the repeatable cell structure only. Such kind of fixed architecture performs well when enough cells and channels are used. However, when the architecture becomes more lightweight, the performance decreases significantly. To obtain better lightweight architectures, more flexible and diversified neural architectures are in demand, and more efficient methods should be designed for larger search space. Motivated by this, we propose MoARR algorithm, which utilizes the existing research results and historical information to quickly find architectures that are both lightweight and accurate. We use the discovered high-performance cells to construct network architectures. This method increases the network architecture diversity while also reduces the search space of cell structure design. In addition, we designs a novel multi-objective method to effectively analyze the historical evaluation information, so as to efficiently search for the Pareto optimal architectures with high accuracy and small parameter number. Experimental results show that our MoARR can achieve a powerful and lightweight model (with 1.9% error rate and 2.3M parameters) on CIFAR-10 in 6 GPU hours, which is better than the state-of-the-arts. The explored architecture is transferable to ImageNet and achieves 76.0% top-1 accuracy with 4.9M parameters.

cs.CV↗

arXiv2017

Fano effect in an ultracold atom-molecule coupled system

The Fano effect or Fano resonance with a characteristically asymmetric line shape originates from quantum interference between direct and indirect transition pathways in continuum-bound coupled systems, and is a ubiquitous phenomenon in atomic, molecular, nuclear and solid-state physics. In optical nanoscale structures, the Fano effect has wide-ranging applications that include optical filtering, sensing, all-optical switching, quantum interferometry and nonlinear optics, and this opens new avenues for photonic devices. The emergent area of ultracold atomic and molecular gases presents an ideal platform for studying Fano resonances, since the physical parameters of these gases can be extensively tuned with high precision using external fields. However, an experimental demonstration of the Fano effect in hybridized atom-molecular coupled systems has remained elusive. Here, we report on observations of the Fano effect in molecular spectra obtained by photoassociation near a d-wave Feshbach resonance. This effect occurs due to quantum interference in PA transitions involving the continuum of atom-atom scattering states, the underlying Feshbach and photoassociated excited bound molecular states. We measure the variation in atom loss rate with an external magnetic field close to the Feshbach resonance in the presence of PA laser, and thereby clearly demonstrate the Fano effect. Our results further reveal that the Fano effect has significant influence on spectral shifts. Based on Fano's method, we develop a theory that explains the observed experimental results relatively well. Our theoretical formulation takes into account quantum interference between or among multiple transition pathways and between inelastic channels. Our results present a novel method for tuning the collisional interaction strength with laser light using Fano resonance.

quant-ph↗