arXiv subjects

Meihong Wang

Publications and source records attributed to Meihong Wang.

At least 19 recordsLinked to original sources

arXiv2026

On-chip squeezed light in the audio frequency band

Squeezed light in the audio-frequency band is a key resource for quantum metrology and quantum sensing. However, realizing stable audio-frequency squeezed light on integrated photonic platforms remains challenging due to technical noise and the difficulty of scalable phase referencing. Here, we demonstrate on-chip generation of audio-band two-mode squeezed states down to 60 Hz in a silica microcavity. To enable phase-stable operation without directly locking fragile quantum modes, we develop a coherent-comb control method in which a weak electro-optic reference comb co-propagates with the vacuum at the quantum frequency modes in an orthogonal polarization. This scheme provides quadrature measurement and long-timescale phase stability, thereby enabling covariance-matrix reconstruction. We verify the entanglement with the positive partial transposition criterion, which confirms inseparability via a minimum symplectic eigenvalue of 0.395 ($<0.5$). Our results establish an experimentally accessible route toward on-chip phase-stable audio-band squeezing and support the scalable framework for continuous-variable quantum information processing with integrated photonics.

quant-ph↗

arXiv2026

Analog photonic simulator for large-scale transport

Transport equations describe how physical quantities -- such as mass, energy, momentum, concentration, probability, or fields -- are carried, propagated, or redistributed through space and time, forming a foundational class of partial differential equations across science and engineering. However, high-dimensional partial differential equations are difficult to represent on digital grids because the number of degrees of freedom grows exponentially with dimension. Continuous-variable quantum photonics on the other hand can represent and evolve these large-scale fields without first discretizing space into a discrete grid. We demonstrate a large-scale analog photonic simulator for the constant-coefficient advection equation, a transport equation that is a fundamental benchmark for scientific computing. The solution of a $d$-variable advection equation is encoded into $d$ optical modes, so that the partial differential equation evolution maps directly to programmable phase-space displacements generated by optical quadrature momenta. Using a time-domain continuous-variable quantum photonic platform, we validate programmable control with $20,000$ single-mode squeezed states and $20,000$ two-mode squeezed states, and implement transport dynamics on a $20,000$-mode cluster-state resource. Homodyne measurements then verifies mode-resolved displacement control, which can provide first and second-order moment information of the solution to the advection equation, with final achievable relative error as low as $0.8\%$ and $0.92\%$ for first and second-order moment observables respectively. Our results establish continuous-variable photonics as a suitable programmable analog platform for large-scale advection equations.

quant-ph↗

arXiv2026

ANCHOR: Abductive Network Construction with Hierarchical Orchestration for Reliable Probability Inference in Large Language Models

A central challenge in large-scale decision-making under incomplete information is estimating reliable probabilities. Recent approaches use Large Language Models (LLMs) to generate explanatory factors and coarse-grained probability estimates, which are then refined by a Na\"ive Bayes model over factor combinations. However, sparse factor spaces often yield ``unknown'' predictions, while expanding factors increases noise and spurious correlations, weakening conditional independence and degrading reliability. To address these limitations, we propose \textsc{Anchor}, an aggregated Bayesian inference framework over a hierarchical factor space. It constructs dense factor hierarchies through iterative generation and clustering, maps contexts via hierarchical retrieval and refinement, and augments Na\"ive Bayes with a Causal Bayesian Network to model latent factor dependencies. Experiments show that \textsc{Anchor} markedly reduces ``unknown'' predictions and produces more reliable probability estimates than direct LLM baselines, achieving state-of-the-art performance while significantly reducing time and token overhead.

cs.CL↗

arXiv2026

GCoT-Decoding: Unlocking Deep Reasoning Paths for Universal Question Answering

Chain-of-Thought reasoning can enhance large language models, but it requires manually designed prompts to guide the model. Recently proposed CoT-decoding enables the model to generate CoT-style reasoning paths without prompts, but it is only applicable to problems with fixed answer sets. To address this limitation, we propose a general decoding strategy GCoT-decoding that extends applicability to a broader range of question-answering tasks. GCoT-decoding employs a two-stage branching method combining Fibonacci sampling and heuristic error backtracking to generate candidate decoding paths. It then splits each path into a reasoning span and an answer span to accurately compute path confidence, and finally aggregates semantically similar paths to identify a consensus answer, replacing traditional majority voting. We conduct extensive experiments on six datasets covering both fixed and free QA tasks. Our method not only maintains strong performance on fixed QA but also achieves significant improvements on free QA, demonstrating its generality.

cs.CL↗

arXiv2026

AGSC: Adaptive Granularity and Semantic Clustering for Uncertainty Quantification in Long-text Generation

Large Language Models (LLMs) have demonstrated impressive capabilities in long-form generation, yet their application is hindered by the hallucination problem. While Uncertainty Quantification (UQ) is essential for assessing reliability, the complex structure makes reliable aggregation across heterogeneous themes difficult, in addition, existing methods often overlook the nuance of neutral information and suffer from the high computational cost of fine-grained decomposition. To address these challenges, we propose AGSC (Adaptive Granularity and GMM-based Semantic Clustering), a UQ framework tailored for long-form generation. AGSC first uses NLI neutral probabilities as triggers to distinguish irrelevance from uncertainty, reducing unnecessary computation. It then applies Gaussian Mixture Model (GMM) soft clustering to model latent semantic themes and assign topic-aware weights for downstream aggregation. Experiments on BIO and LongFact show that AGSC achieves state-of-the-art correlation with factuality while reducing inference time by about 60% compared to full atomic decomposition.

cs.CL↗

arXiv2026

DTCRS: Dynamic Tree Construction for Recursive Summarization

Retrieval-Augmented Generation (RAG) mitigates the hallucination problem of Large Language Models (LLMs) by incorporating external knowledge. Recursive summarization constructs a hierarchical summary tree by clustering text chunks, integrating information from multiple parts of a document to provide evidence for abstractive questions involving multi-step reasoning. However, summary trees often contain a large number of redundant summary nodes, which not only increase construction time but may also negatively impact question answering. Moreover, recursive summarization is not suitable for all types of questions. We introduce DTCRS, a method that dynamically generates summary trees based on document structure and query semantics. DTCRS determines whether a summary tree is necessary by analyzing the question type. It then decomposes the question and uses the embeddings of sub-questions as initial cluster centers, reducing redundant summaries while improving the relevance between summaries and the question. Our approach significantly reduces summary tree construction time and achieves substantial improvements across three QA tasks. Additionally, we investigate the applicability of recursive summarization to different question types, providing valuable insights for future research.

cs.CL↗

arXiv2025

Robust quantum computational advantage with programmable 3050-photon Gaussian boson sampling

The creation of large-scale, high-fidelity quantum computers is not only a fundamental scientific endeavour in itself, but also provides increasingly robust proofs of quantum computational advantage (QCA) in the presence of unavoidable noise and the dynamic competition with classical algorithm improvements. To overcome the biggest challenge of photon-based QCA experiments, photon loss, we report new Gaussian boson sampling (GBS) experiments with 1024 high-efficiency squeezed states injected into a hybrid spatial-temporal encoded, 8176-mode, programmable photonic quantum processor, Jiuzhang 4.0, which produces up to 3050 photon detection events. Our experimental results outperform all classical spoofing algorithms, particularly the matrix product state (MPS) method, which was recently proposed to utilise photon loss to reduce the classical simulation complexity of GBS. Using the state-of-the-art MPS algorithm on the most powerful supercomputer EI Capitan, it would take > $10^{42}$ years to construct the required tensor network for simulation, while our Jiuzhang 4.0 quantum computer takes 25.6 $\mu$s to produce a sample. This work establishes a new frontier of QCA and paves the way to fault-tolerant photonic quantum computing hardware.

quant-ph↗

arXiv2025

High-Efficiency Quantum Memory of Full-Bandwidth Squeezed Light

In continuous-variable quantum information processing, it is crucial to develop high-efficiency and broadband quantum memory of squeezed light, which enables the storage of full-bandwidth information. Here, we present a quantum memory of squeezed light with up to 24 MHz bandwidth, which is at least 12 times that of previous narrowband resonant memory systems, via a far-off resonant Raman process. We achieve output squeezing of as high as 1.0 dB with fidelity above 92% and a memory efficiency of 80%, corresponding to an end-to-end efficiency of 64.2%, when input squeezing is 1.6 dB. The lowest excess noise of 0.025 shot-noise-unit in the memory system is estimated by the noisy channel model which is benefited from optimizing quantum memory performance with a backward retrieval strategy. Our results represent a breakthrough in high-performance memory for squeezed states within tens of MHz-level bandwidth, which has potential applications in high-speed quantum information processing.

quant-ph↗

arXiv2024

One-Sided Device-Independent Random Number Generation Through Fiber Channels

Randomness is an essential resource and plays important roles in various applications ranging from cryptography to simulation of complex systems. Certified randomness from quantum process is ensured to have the element of privacy but usually relies on the device's behavior. To certify randomness without the characterization for device, it is crucial to realize the one-sided device-independent random number generation based on quantum steering, which guarantees security of randomness and relaxes the demands of one party's device. Here, we distribute quantum steering between two distant users through a 2 km fiber channel and generate quantum random numbers at the remote station with untrustworthy device. We certify the steering-based randomness by reconstructing covariance matrix of the Gaussian entangled state shared between distant parties. Then, the quantum random numbers with a generation rate of 7.06 Mbits/s are extracted from the measured amplitude quadrature fluctuation of the state owned by the remote party. Our results demonstrate the first realization of steering-based random numbers extraction in a practical fiber channel, which paves the way to the quantum random numbers generation in asymmetric networks.

quant-ph↗

arXiv2024

Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization

In the modern iron and steel industry, the efficient distribution of byproduct gases faces significant challenges due to quantity- and quality-related uncertainties of gases. This study presents an optimal approach to gas distribution that addresses this issue by incorporating the energy flow network and the uncertain surplus gases from the manufacturing system. The uncertain optimization problem is formulated as a two-stage robust optimization (TSRO) model, including "here-and-now" decisions aimed at minimizing the start-stop cost of energy conversion units, as well as "wait-and-see" decisions aimed at minimizing the operating cost of gasholders and the penalties resulting from energy excess or shortage. To facilitate practical implementation, we propose a "first quantify, then optimize" approach: (1) quantifying the uncertainty of surplus gases via a conditional quantile regression (CDQ)-based T-step time series model, and (2)finding the optimal solution through a column-and-constraint generation algorithm. Furthermore, a case study is conducted on an industrial energy system to validate the proposed methodology. Computational results, using evaluation indicators, such as MAPE, RMSE, PICP, and PINAW, confirm the effectiveness of the data-driven time series model in accurately quantifying uncertainties in each period. Sensitivity analysis demonstrates that the proposed TSRO model achieves a favorable balance between robustness and flexibility by selecting the combination of "budget and quantile" and the parameters of storage and conversion units. Consequently, TSRO can efficiently find a robust gas distribution solution with the desired level of conservativeness for integrated iron and steel plants.

math.OC↗

arXiv2023

Simultaneous preparation of two optical cat states based on a nondegenerate optical parametric amplifier

The optical cat state, known as the superposition of coherent states, has broad applications in quantum computation and quantum metrology. Increasing the number of optical cat states is crucial to implement complex quantum information tasks based on them. Here, we prepare two optical cat states simultaneously based on a nondegenerate optical parametric amplifier. By subtracting one photon from each of two squeezed vacuum states, two odd cat states with orthogonal superposition direction in phase space are prepared simultaneously, which have similar fidelity of 60% and amplitude of 1.2. Compared with the traditional method to generate two odd optical cat states based on two degenerate optical parametric amplifiers, only one nondegenerate optical parametric amplifier is applied in our experiment, which saves half of the quantum resource of nonlinear cavities. The presented results make a step toward preparing the four-component cat state, which has potential applications in fault-tolerant quantum computation.

quant-ph↗

arXiv2023

Remote preparation of optical cat states based on Gaussian entanglement

Remote state preparation enables one to prepare and manipulate quantum state non-locally. As an essential quantum resource, optical cat state is usually prepared locally by subtracting photons from a squeezed vacuum state. For remote quantum information processing, it is essential to prepare and manipulate optical cat states remotely based on Gaussian entanglement, which remains a challenge. Here, we present experimental preparation of optical cat states based on a remotely distributed two-mode Gaussian entangled state in a lossy channel. By performing photon subtraction and homodyne projective measurement at Alice's station, an optical cat state is prepared remotely at Bob's station. Furthermore, the prepared cat state is rotated by changing Alice's measurement basis of homodyne detection, which demonstrates the remote manipulation of it. By distributing two modes of the two-mode Gaussian entangled state in lossy channels, we demonstrate that the remotely prepared cat state can tolerate much more loss in Alice's channel than that in Bob's channel. We also show that cat states with amplitudes larger than 2 can be prepared by increasing the squeezing level and subtracting photon numbers. Our results make a crucial step toward remote hybrid quantum information processing involving discrete- and continuous-variable techniques.

quant-ph↗

arXiv2022

Remote preparation and manipulation of squeezed light

Remote state preparation enables one to create and manipulate a quantum state based on the shared entanglement between distant nodes. Here, we experimentally demonstrate remote preparation and manipulation of squeezed light. By performing homodyne projective measurement on one mode of the continuous variable entangled state at Alice's station, a squeezed state is created at Bob's station. Moreover, rotation and displacement operations are applied on the prepared squeezed state by changing the projective parameters on Alice's state. We also show that the remotely prepared squeezed state is robust against loss and N-1 squeezed states can be remotely prepared based on a N-mode continuous variable Greenberger-Horne-Zeilinger-like state. Our results verify the entanglement-based model used in security analysis of quantum key distribution with continuous variables and have potential application in remote quantum information processing.

quant-ph↗

arXiv2022

Experimental preparation and manipulation of squeezed cat states via an all-optical in-line squeezer

The squeezed cat state, an essential quantum resource, can be used for quantum error correction and slowing decoherence of the optical cat state. However, preparing a squeezed cat state with high generation rate, and effectively manipulating it, remain challenging. In this work, a high-performance all-optical in-line squeezer is developed to prepare a squeezed cat state and manipulate the phase of the quadrature squeezing. This scheme has the advantages that the phase of the quadrature squeezing of the squeezed cat state can be manipulated by changing the working condition of the squeezer, and that a higher generation rate can be achieved via the deterministic squeezing operation of the in-line squeezer. The generation rate of squeezed cat states reaches 2 kHz, the same as that of the initial cat state. The all-optical in-line squeezer proposed here removes the requirements of electro-optic and opto-electric conversions necessary for an off-line squeezer, thus enabling high-bandwidth squeezing operations on non-Gaussian states. These results provide an efficient method to prepare and manipulate optical squeezed cat states, which makes a step closer to their applications in all-optical quantum information processing.

quant-ph↗

arXiv2022

I^2R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation

In this paper, we present the Intra- and Inter-Human Relation Networks (I^2R-Net) for Multi-Person Pose Estimation. It involves two basic modules. First, the Intra-Human Relation Module operates on a single person and aims to capture Intra-Human dependencies. Second, the Inter-Human Relation Module considers the relation between multiple instances and focuses on capturing Inter-Human interactions. The Inter-Human Relation Module can be designed very lightweight by reducing the resolution of feature map, yet learn useful relation information to significantly boost the performance of the Intra-Human Relation Module. Even without bells and whistles, our method can compete or outperform current competition winners. We conduct extensive experiments on COCO, CrowdPose, and OCHuman datasets. The results demonstrate that the proposed model surpasses all the state-of-the-art methods. Concretely, the proposed method achieves 77.4% AP on CrowPose dataset and 67.8% AP on OCHuman dataset respectively, outperforming existing methods by a large margin. Additionally, the ablation study and visualization analysis also prove the effectiveness of our model.

cs.CV↗

arXiv2022

Distillation of Gaussian Einstein-Podolsky-Rosen steering with noiseless linear amplification

Einstein-Podolsky-Rosen (EPR) steering is one of the most intriguing features of quantum mechanics and an important resource for quantum communication. For practical applications, it remains a challenge to protect EPR steering from decoherence due to its intrinsic difference from entanglement. Here, we experimentally demonstrate the distillation of Gaussian EPR steering and entanglement in lossy and noisy environments using measurement-based noiseless linear amplification. Different from entanglement distillation, the extension of steerable region happens in the distillation of EPR steering besides the enhancement of steerabilities. We demonstrate that the two-way or one-way steerable region is extended after the distillation of EPR steering when the NLA is implemented based on Bob's or Alice's measurement results. We also show that the NLA helps to extract secret key from insecure region in one-sided device-independent quantum key distribution with EPR steering. Our work paves the way for quantum communication exploiting EPR steering in practical quantum channels.

quant-ph↗

arXiv2022

Experimental demonstration of remotely creating Wigner negativity via quantum steering

Non-Gaussian states with Wigner negativity are of particular interest in quantum technology due to their potential applications in quantum computing and quantum metrology. However, how to create such states at a remote location remains a challenge, which is important for efficiently distributing quantum resource between distant nodes in a network. Here, we experimentally prepare optical non-Gaussian state with negative Wigner function at a remote node via local non-Gaussian operation and shared Gaussian entangled state existing quantum steering. By performing photon subtraction on one mode, Wigner negativity is created in the remote target mode. We show that the Wigner negativity is sensitive to loss on the target mode, but robust to loss on the mode performing photon subtraction. This experiment confirms the connection between the remotely created Wigner negativity and quantum steering. As an application, we present that the generated non-Gaussian state exhibits metrological power in quantum phase estimation.

quant-ph↗

arXiv2022

Deterministic distribution of orbital angular momentum multiplexed continuous-variable entanglement and quantum steering

Orbital angular momentum (OAM) multiplexing provides an efficient method to improve data-carrying capacity in various quantum communication protocols. It is a precondition to distribute OAM multiplexed quantum resources in quantum channels for implementing quantum communication. However, quantum steering of OAM multiplexed optical fields and the effect of channel noise on OAM multiplexed quantum resources remain unclear. Here, we generate OAM multiplexed continuous-variable (CV) entangled states and distribute them in lossy or noisy channels. We show that the decoherence property of entanglement and quantum steering of the OAM multiplexed states carrying topological charges $l=1$ and $l=2$ are the same as that of the Gaussian mode with $l=0$ in lossy and noisy channels. The sudden death of entanglement and quantum steering of high-order OAM multiplexed states is observed in the presence of excess noise. Our results demonstrate the feasibility to realize high data-carrying capacity quantum information processing by utilizing OAM multiplexed CV entangled states.

quant-ph↗