arXiv subjects

Jianqing Liu

Publications and source records attributed to Jianqing Liu.

At least 19 recordsLinked to original sources

arXiv2026

Can Quantum Receiver Beat the SIC Limit in Multiple Access Networks?

Successive interference cancellation (SIC) is an important technique for 5G/B5G wireless receivers to resolve interfering signals from multiple users. While SIC has been proven to approach the channel capacity limit in many settings of multiple access networks, it remains unknown if this limit can be surpassed by an advanced yet practically implementable quantum receiver technique. In this work, we answer this quest by proposing a full-stack quantum receiver that integrates front-end quantum sensing with back-end quantum signal processing. The proposed technique leverages simple qubit ensembles without using any complex entanglement resources to parallelly extract interfering multiuser signals, with a channel capacity beyond the limit of SIC in some operational corners. In performance evaluation, we report notable quantum advantage over the ultimate multiple-access channel capacity limit and practical SIC algorithms in low-SNR regimes in terms of spectral efficiency and detection efficiency due to the receiver's ability to exploit quantum correlated measurements and superposition-enabled parallel processing.

cs.NI↗

arXiv2026

Towards Efficient Synthesis of Quantum Graph States by Fusing Graph Motifs

Photonic graph states with advanced topologies can enable measurement-based quantum computing, distributed quantum sensing, and quantum interconnects. However, the efficient generation of photonic graph states is limited by the probabilistic nature of photonic entangling operations and the exponential dependence of generation rate on resource cost. In this work, we study photonic graph state synthesis as a cost-aware decomposition problem, exploiting local Clifford (LC) equivalence to identify more synthesis-friendly representations of the target graph state before decomposition. Specifically, we propose Cost-aware Fusion-based Decomposition (CFD), a three-stage heuristic framework that decomposes a target graph state into ring, star, and linear motifs, and assembles them via Type-I fusion operations to minimize fusion overhead and physical-qubit consumption. We further show that selecting the LC-equivalent graph state with the minimum number of edges provides a highly effective proxy for near-optimal synthesis: in many cases it matches the best generation rate observed within the LC equivalence class under CFD, and in most remaining cases it remains close to it. Numerical evaluations on graph state orbit data and 2D and 3D lattice graph states demonstrate that CFD achieves up to 84.6\% reduction in resource overhead compared to baseline constructions, and yields improvements in photonic generation rate spanning multiple orders of magnitude. These results suggest that combining structure-aware motif decomposition with LC equivalence is a practical and scalable strategy for photonic graph state synthesis.

quant-ph↗

arXiv2026

Non-Abelian Mixer for QAOA on Hybrid Oscillator-Qubit Quantum Processors

The realization of universal control in hybrid oscillator-qubit quantum processors enables the systematic design and implementation of quantum algorithms. However, the algorithmic development for such platforms remains at an early stage. While the Quantum Approximate Optimization Algorithm (QAOA) has been extensively studied in both continuous-variable (CV) and discrete-variable (DV) quantum systems, its development in the hybrid CV-DV setting remains limited. In this paper, we propose a hardware-native non-Abelian mixer for QAOA on hybrid CV-DV quantum processors and develop a corresponding hybrid ansatz for the Max-Cut problem. We evaluate the proposed ansatz on unweighted Erdős-Rényi graphs and benchmark it against the standard transverse-field mixer using the approximation ratio and optimal-solution probability. Across all graph sizes and Fock cutoffs in our simulations, the proposed non-Abelian mixer consistently improves both expected solution quality and the probability of sampling an optimal solution relative to the transverse-field mixer. These results indicate that the proposed non-Abelian mixer is a promising building block for QAOA on hybrid oscillator-qubit platforms.

quant-ph↗

arXiv2026

Personalized Federated Learning by Energy-Efficient UAV Communications

Federated learning (FL) is an effective paradigm for enhancing the learning capability of edge devices while preserving data privacy. In geographically dispersed FL systems, such as sensor networks in remote areas, unmanned aerial vehicles (UAVs) can flexibly establish high-quality communication links to support parameter exchange. However, device heterogeneity and the limited battery capacity of UAVs pose significant challenges. Specifically, data heterogeneity slows convergence, while scheduling all devices for global collaboration incurs excessive communication and energy costs. To overcome these challenges, we adopt a strict separation between a globally shared backbone and permanently local personalization heads, thereby mitigating the impact of data heterogeneity. Furthermore, we propose a gradient-based scheduling strategy that jointly considers energy efficiency and learning performance. In each communication round, the backbone is updated only by the top-$α$ devices ranked by gradient $\ell_{2}$-norm, ensuring that optimization focuses on the most informative updates. Simulation results demonstrate that the proposed scheme achieves higher learning accuracy than state-of-the-art approaches while significantly reducing UAV energy consumption.

cs.LG↗

arXiv2025

TCFormer: A 5M-Parameter Transformer with Density-Guided Aggregation for Weakly-Supervised Crowd Counting

Crowd counting typically relies on labor-intensive point-level annotations and computationally intensive backbones, restricting its scalability and deployment in resource-constrained environments. To address these challenges, this paper proposes the TCFormer, a tiny, ultra-lightweight, weakly-supervised transformer-based crowd counting framework with only 5 million parameters that achieves competitive performance. Firstly, a powerful yet efficient vision transformer is adopted as the feature extractor, the global context-aware capabilities of which provides semantic meaningful crowd features with a minimal memory footprint. Secondly, to compensate for the lack of spatial supervision, we design a feature aggregation mechanism termed the Learnable Density-Weighted Averaging module. This module dynamically re-weights local tokens according to predicted density scores, enabling the network to adaptively modulate regional features based on their specific density characteristics without the need for additional annotations. Furthermore, this paper introduces a density-level classification loss, which discretizes crowd density into distinct grades, thereby regularizing the training process and enhancing the model's classification power across varying levels of crowd density. Therefore, although TCformer is trained under a weakly-supervised paradigm utilizing only image-level global counts, the joint optimization of count and density-level losses enables the framework to achieve high estimation accuracy. Extensive experiments on four benchmarks including ShanghaiTech A/B, UCF-QNRF, and NWPU datasets demonstrate that our approach strikes a superior trade-off between parameter efficiency and counting accuracy and can be a good solution for crowd counting tasks in edge devices.

cs.CV↗

arXiv2025

Quantum-enhanced Information Retrieval from Reflective Intelligent Surfaces

Information retrieval from passive backscatter systems is widely used in digital applications with tight energy budgets, short communication distances, and low data rates. Due to the fundamental limits of classical wireless receivers, the achievable data rate cannot be increased without compromising either energy efficiency or communication range, thereby hindering the broader adoption of this technology. In this work, we present a novel time-resolving quantum receiver combined with a multi-mode probing signal to extract large-alphabet information modulated by a passive reconfigurable intelligent surface (RIS). The adaptive nature of the proposed receiver yields significant quantum advantages over classical receivers without relying on complex or fragile quantum resources such as entanglement. Simulation results show that the proposed technique surpasses the classical standard quantum limit (SQL) for modulation sizes up to M = 2^8, meanwhile halving the probing energy or increasing the communication distance by a factor of 1.41.

quant-ph↗

arXiv2025

Distributed Quantum Magnetic Sensing for Infrastructure-free Geo-localization

Modern navigation systems rely heavily on Global Navigation Satellite Systems (GNSS), whose weak spaceborne signals are vulnerable to jamming, spoofing, and line-of-sight blockage. As an alternative, the Earth's magnetic field entails location information and is found critical to many animals' cognitive and navigation behavior. However, the practical use of the Earth's magnetic field for geo-localization hinges on an ultra-sensitive magnetometer. This work investigates how quantum magnetic sensing can be used for this purpose. We theoretically derive the Cramér-Rao lower bound (CRLB) for the estimation error of quantum sensing when using a nitrogen-vacancy (NV) center and prove the quantum advantage over classical magnetometers. Moreover, we employ a practical distributed quantum sensing protocol to saturate CRLB. Based on the estimated magnetic field and the earth's magnetic field map, we formulate geo-localization as a map-matching problem and introduce a coarse-to-fine Mahalanobis distance search in both gradient space (local field derivatives) and corner space (raw field samples). We simulate the proposed quantum sensing-based geo-localization framework over four cities in the United States and Canada. The results report that in high-gradient regions, gradient-space Mahalanobis search achieves sub-kilometer median localization error; while in magnetically smoother areas, corner-space search provides better accuracy and a $4-8\times$ reduction in runtime.

quant-ph↗

arXiv2025

Distributing Arbitrary Quantum Graph States by Graph Transformation

Quantum graph state is a special class of nonlocal state among multiple quantum particles, underpinning several nonclassical and promising applications such as quantum computing and quantum secret sharing. Recently, establishing quantum graph states among physically distant nodes has gained increasing popularity owing to its potential in expanding current quantum applications in scale. Existing research on this topic relies on a two-step approach: first distributing low-dimension elementary entanglement to target nodes, and then fusing them into a high-dimension quantum graph state. However, most existing studies focus solely on minimizing costs (e.g., the number of elementary entanglements consumed) to entangle target nodes, while neglecting the structure of the final quantum graph state. This can easily result in weak system entanglement, jeopardizing the graph state under partial measurement or noises. In this paper, we aim to establish any arbitrary quantum graph states of strong entanglement structures at a much lower cost than the state of the art. The method is to search for and establish an alternative state to the target state that is of lowest cost in creation. Subsequently, we transform such an alternative state back to the target state via compressed single-qubit Clifford operations. To verify the performance of our developed algorithm, we conduct comprehensive simulations based on an open dataset containing all graph state structures up to 8 qubits. The results demonstrate fast algorithm convergence, an increased success probability in distributing any graph states, and $53.57\%$ saving in ERP cost compared with the state-of-the-art baseline.

quant-ph↗

arXiv2025

A Two-stage Optimization Method for Wide-range Single-electron Quantum Magnetic Sensing

Quantum magnetic sensing based on spin systems has emerged as a new paradigm for detecting ultra-weak magnetic fields with unprecedented sensitivity, revitalizing applications in navigation, geo-localization, biology, and beyond. At the heart of quantum magnetic sensing, from the protocol perspective, lies the design of optimal sensing parameters to manifest and then estimate the underlying signals of interest (SoI). Existing studies on this front mainly rely on adaptive algorithms based on black-box AI models or formula-driven principled searches. However, when the SoI spans a wide range and the quantum sensor has physical constraints, these methods may fail to converge efficiently or optimally, resulting in prolonged interrogation times and reduced sensing accuracy. In this work, we report the design of a new protocol using a two-stage optimization method. In the 1st Stage, a Bayesian neural network with a fixed set of sensing parameters is used to narrow the range of SoI. In the 2nd Stage, a federated reinforcement learning agent is designed to fine-tune the sensing parameters within a reduced search space. The proposed protocol is developed and evaluated in a challenging context of single-shot readout of an NV-center electron spin under a constrained total sensing time budget; and yet it achieves significant improvements in both accuracy and resource efficiency for wide-range D.C. magnetic field estimation compared to the state of the art.

quant-ph↗

arXiv2025

A Full-Stage Refined Proposal Algorithm for Suppressing False Positives in Two-Stage CNN-Based Detection Methods

False positives in pedestrian detection remain a challenge that has yet to be effectively resolved. To address this issue, this paper proposes a Full-stage Refined Proposal (FRP) algorithm aimed at eliminating these false positives within a two-stage CNN-based pedestrian detection framework. The main innovation of this work lies in employing various pedestrian feature re-evaluation strategies to filter out low-quality pedestrian proposals during both the training and testing stages. Specifically, in the training phase, the Training mode FRP algorithm (TFRP) introduces a novel approach for validating pedestrian proposals to effectively guide the model training process, thereby constructing a model with strong capabilities for false positive suppression. During the inference phase, two innovative strategies are implemented: the Classifier-guided FRP (CFRP) algorithm integrates a pedestrian classifier into the proposal generation pipeline to yield high-quality proposals through pedestrian feature evaluation, and the Split-proposal FRP (SFRP) algorithm vertically divides all proposals, sending both the original and the sub-region proposals to the subsequent subnetwork to evaluate their confidence scores, filtering out those with lower sub-region pedestrian confidence scores. As a result, the proposed algorithm enhances the model's ability to suppress pedestrian false positives across all stages. Various experiments conducted on multiple benchmarks and the SY-Metro datasets demonstrate that the model, supported by different combinations of the FRP algorithm, can effectively eliminate false positives to varying extents. Furthermore, experiments conducted on embedded platforms underscore the algorithm's effectiveness in enhancing the comprehensive pedestrian detection capabilities of the small pedestrian detector in resource-constrained edge devices.

cs.CV↗

arXiv2025

A Virtual Quantum Network Prototype for Open Access

The rise of quantum networks has revolutionized domains such as communication, sensing, and cybersecurity. Despite this progress, current quantum network systems remain limited in scale, are highly application-specific (e.g., for quantum key distribution), and lack a clear road map for global expansion. These limitations are largely driven by a shortage of skilled professionals, limited accessibility to quantum infrastructure, and the high complexity and cost associated with building and operating quantum hardware. To address these challenges, this paper proposes an open-access software-based quantum network virtualization platform designed to facilitate scalable and remote interaction with quantum hardware. The system is built around a cloud application that virtualizes the core hardware components of a lab-scale quantum network testbed, including the time tagger and optical switch, enabling users to perform coincidence counts of the photon entanglements while ensuring fair resource allocation. The fairness is ensured by employing the Hungarian Algorithm to allocate nearly equal effective entanglement rates among users. We provide implementation details and performance analysis from the perspectives of hardware, software, and cloud platform, which demonstrates the functionality and efficiency of the developed prototype.

quant-ph↗

arXiv2024

Two Birds with One Stone: Differential Privacy by Low-power SRAM Memory

The software-based implementation of differential privacy mechanisms has been shown to be neither friendly for lightweight devices nor secure against side-channel attacks. In this work, we aim to develop a hardware-based technique to achieve differential privacy by design. In contrary to the conventional software-based noise generation and injection process, our design realizes local differential privacy (LDP) by harnessing the inherent hardware noise into controlled LDP noise when data is stored in the memory. Specifically, the noise is tamed through a novel memory design and power downscaling technique, which leads to double-faceted gains in privacy and power efficiency. A well-round study that consists of theoretical design and analysis and chip implementation and experiments is presented. The results confirm that the developed technique is differentially private, saves 88.58% system power, speeds up software-based DP mechanisms by more than 10^6 times, while only incurring 2.46% chip overhead and 7.81% estimation errors in data recovery.

cs.CR↗

arXiv2023

FENDI: Toward High-Fidelity Entanglement Distribution in the Quantum Internet

A quantum network distributes quantum entanglements between remote nodes, and is key to many applications in secure communication, quantum sensing and distributed quantum computing. This paper explores the fundamental trade-off between the throughput and the quality of entanglement distribution in a multi-hop quantum repeater network. Compared to existing work which aims to heuristically maximize the entanglement distribution rate (EDR) and/or entanglement fidelity, our goal is to characterize the maximum achievable worst-case fidelity, while satisfying a bound on the maximum achievable expected EDR between an arbitrary pair of quantum nodes. This characterization will provide fundamental bounds on the achievable performance region of a quantum network, which can assist with the design of quantum network topology, protocols and applications. However, the task is highly non-trivial and is NP-hard as we shall prove. Our main contribution is a fully polynomial-time approximation scheme to approximate the achievable worst-case fidelity subject to a strict expected EDR bound, combining an optimal fidelity-agnostic EDR-maximizing formulation and a worst-case isotropic noise model. The EDR and fidelity guarantees can be implemented by a post-selection-and-storage protocol with quantum memories. By developing a discrete-time quantum network simulator, we conduct simulations to show the characterized performance region (the approximate Pareto frontier) of a network, and demonstrate that the designed protocol can achieve the performance region while existing protocols exhibit a substantial gap.

cs.NI↗

arXiv2023

Qubit Recycling in Entanglement Distillation

Quantum entanglement distillation is a process to extract a small number of high-fidelity entanglement from a large number of low-fidelity ones, which in essence is to trade yield (or survival rate) for fidelity. Among existing distillation approaches, Gisin's local filtering protocol is commonly adopted in photonic quantum systems for distilling entangled photons in polarization basis. Yet, the performance of Gisin's filter is cursed by the same fundamental trade-off between fidelity and yield. To address this challenge, in this work, we propose a protocol to recycle the disposed photons and improve their fidelity by a designed (and optimized) local operator. The key parameters of the proposed protocol are calculated by solving a constrained optimization problem. In so doing, we achieve significantly higher yield of high-fidelity entanglement pairs. We further evaluate the performance of our designed protocol under two common configurations of Gisin's filter, namely full filter and partial filter. Compared with existing distillation protocols, the results demonstrate that our design achieves as much as 31.2% gain in yield under the same fidelity, while only incurring moderate system complexity in terms of invested hardware and extra signaling for synchronization.

quant-ph↗

arXiv2021

Empirical Optimization on Post-Disaster Communication Restoration for Social Equality

Disasters are constant threats to humankind, and beyond losses in lives, they cause many implicit yet profound societal issues such as wealth disparity and digital divide. Among those recovery measures in the aftermath of disasters, restoring and improving communication services is of vital importance. Although existing works have proposed many architectural and protocol designs, none of them have taken human factors and social equality into consideration. Recent sociological studies have shown that people from marginalized groups (e.g., minority, low income, and poor education) are more vulnerable to communication outages. In this work, we take pioneering efforts in integrating human factors into an empirical optimization model to determine strategies for post-disaster communication restoration. We cast the design into a mix-integer non-linear programming problem, which is proven too complex to be solved. Through a suite of convex relaxations, we then develop heuristic algorithms to efficiently solve the transformed optimization problem. Based on a collected dataset, we further evaluate and demonstrate how our design will prioritize communication services for vulnerable people and promote social equality compared with an existing modeling benchmark.

cs.NI↗

arXiv2020

Enabling Cross-chain Transactions: A Decentralized Cryptocurrency Exchange Protocol

Inspired by Bitcoin, many different kinds of cryptocurrencies based on blockchain technology have turned up on the market. Due to the special structure of the blockchain, it has been deemed impossible to directly trade between traditional currencies and cryptocurrencies or between different types of cryptocurrencies. Generally, trading between different currencies is conducted through a centralized third-party platform. However, it has the problem of a single point of failure, which is vulnerable to attacks and thus affects the security of the transactions. In this paper, we propose a distributed cryptocurrency trading scheme to solve the problem of centralized exchanges, which can achieve trading between different types of cryptocurrencies. Our scheme is implemented with smart contracts on the Ethereum blockchain and deployed on the Ethereum test network. We not only implement transactions between individual users, but also allow transactions between multiple users. The experimental result proves that the cost of our scheme is acceptable.

cs.CR↗

arXiv2020

Optimizing IoT Energy Efficiency on Edge (EEE): a Cross-layer Design in a Cognitive Mesh Network

Battery-powered wireless IoT devices are now widely seen in many critical applications. Given the limited battery capacity and inaccessibility to external power recharge, optimizing energy efficiency (EE) plays a vital role in prolonging the lifetime of these IoT devices. However, a sheer amount of existing works only focus on the EE design at the infrastructure level such as base stations (BSs) but with little attention to the EE design at the device level. In this paper, we propose a novel idea that aims to shift energy consumption to a grid-powered cognitive radio mesh network thus preserving energy of battery-powered devices. Under this line of thinking, we cast the design into a cross-layer optimization problem with an objective to maximize devices' energy efficiency. To solve this problem, we propose a parametric transformation technique to convert the original problem into a more tractable one. A baseline scheme is used to demonstrate the advantage of our design. We also carry out extensive simulations to exhibit the optimality of our proposed algorithms and the network performance under various settings.

eess.SP↗

arXiv2019

A Robust Algorithm for Sniffing BLE Long-Lived Connections in Real-time

Bluetooth Low Energy (BLE) has become an intrinsic wireless technology for the Internet of Things (IoT). With the proliferation of BLE-embedded IoT devices, it is important to study the security and privacy implications of BLE. The forefront attack to BLE devices is the wireless sniffing attack, which would lead to more detrimental threats like jamming, encryption cracking or system penetration. Existing sniffing attacks are based on the correct detection of BLE connection initiation state, but they become ineffective for BLE long-lived connections. In this paper, we focus on the adversary setting with a low-cost single radio and develop a suite of real-time algorithms to determine the key parameters necessary to follow and sniff a BLE connection in the connected state. We implement our algorithms in the open source platform -Ubertooth One and evaluate its performance in terms of sniffing overhead and accuracy. By comparing with state-of-the-art schemes, experimental results show that our sniffer achieves much higher sniffing accuracy (over 80\%) and better stability to BLE operational dynamics.

cs.CR↗