arXiv subjects

Guolong Cui

Publications and source records attributed to Guolong Cui.

At least 19 recordsLinked to original sources

arXiv2026

Label-Free Performance Estimation of Deep Learning Based Radar Detectors in Time-Varying Environments

Deep-learning-based radar target detectors can achieve strong performance in complex sensing environments. However, in time-varying environments, changes in clutter characteristics may cause the distribution of echoes to deviate from that of the training data, thereby degrading detector reliability. Timely identification of such degradation requires an accurate assessment of the deployed detector's performance; however, ground-truth labels for radar echoes are typically unavailable during deployment. This paper investigates label-free performance estimation for deployed deep learning-based radar target detectors. We first show that confidence-based estimators tend to overestimate detection performance under distribution shift because they fail to account for the discrepancy between the detector-induced pseudo-label distribution and the true label distribution. To address this limitation, we propose probabilistic optimal transport (POT), which assesses deployment performance by computing the Wasserstein distance between the detector's output probability distribution on unlabeled deployment echoes and the label distribution in the training data. We further develop thresholded probabilistic optimal transport (TPOT), which suppresses the influence of anomalous transport costs on the Wasserstein-distance-based estimate, thereby improving estimation robustness. Experiments on a publicly measured radar dataset demonstrate that POT and TPOT consistently outperform representative label-free baselines across deployment scenarios with varying degrees of data distribution shift.

eess.SP↗

arXiv2026

Continuous-Phase Frequency-Hopping MIMO Signaling and Processing for ISAC

Frequency-hopping MIMO integrated sensing and communications (FH-MIMO-ISAC) supports agile multidimensional signaling but faces challenges in phase-continuous transmission, demodulation without channel-phase information, and coherent processing of multi-frequency echoes. We propose a continuous-phase frequency-hopping (CP-FH) MIMO ISAC waveform with matched communication and radar processing. Information is conveyed jointly by frequency combinations and continuous-phase-modulation symbols. CPM phase accumulation and frequency-hopping linear-frequency-modulation phase compensation maintain phase continuity and reduce spectral leakage. We derive exact inter-element orthogonality conditions, residual cross-correlation bounds, and a bound on averaged transmit-beampattern ripple. For communication, projection-energy demodulation requires no channel-phase information and approximates least-squares detection under weak inter-branch correlation. It separates frequency-combination search from CPM-symbol decisions, reducing complexity while retaining direct joint-search decisions. For radar, structure-matched coherent processing combines space-time-frequency matched filtering, velocity-consistent Doppler focusing, and frequency-aperture focusing to form a three-dimensional coarse-range--velocity--fine-range response. It reduces Doppler-mismatch loss and extra detections from periodic range grating lobes in conventional pulse-Doppler processing. We further derive the joint range response, output signal and noise powers, and receive beampattern. The analysis characterizes target-discrimination limits under periodic ambiguity and shows that the proposed processor preserves frequency-aperture range resolution and full-aperture angular resolution, with equal output noise power under the stated conditions.

eess.SP↗

arXiv2026

Radar Intelligent Detection with Coarse-Grained Labels

Many deep learning based radar target detectors rely on range-cell level labels for training, which are expensive to obtain. To reduce the labeling burden, this paper presents a training strategy that uses only range-window level labels. Specifically, two sub-echoes are randomly cropped from the same window echo, resulting in a known relative shift between them. A siamese Transformer is then used to identify target-salient responses from one sub-echo and form pseudo labels, which are mapped to the paired sub-echo according to the known shift. In addition, a shift-equivariant consistency (SEC) constraint is imposed to align the predicted responses of the two sub-echoes after shift compensation. By exploiting shift priors within each labeled window, the proposed approach learns reliable detection without requiring cell-level labels. Experimental results show that it consistently surpasses classical constant false alarm rate (CFAR) detectors and achieves performance close to range-cell level labels training, while substantially reducing labeling cost.

eess.SP↗

arXiv2026

A Robust Learning Framework for Deep-Learning-Based Radar Target Detection With Partially Mislabeled Training Data

Deep-learning-based radar target detection has substantially improved detection performance, but its effectiveness depends critically on the correctness of the training labels assigned to radar echo samples. In practical applications, training data may contain partially mislabeled samples, which can cause detection models to learn biased supervisory information and thereby degrade detection performance. To address this issue, this paper proposes SL-RLF, a robust learning framework for deep-learning-based radar target detection with partially mislabeled training data. Specifically, we establish a probabilistic model to characterize the generation mechanisms of label errors in radar target detection and analyze the robustness of detection models under partial label errors within a risk minimization formulation. Theoretical analysis shows that, under appropriate assumptions, a loss function satisfying the symmetry condition enables the detection model in the learning framework to be robust against partial label errors. Guided by this result, we design a loss function satisfying the symmetry condition and construct the proposed robust learning framework, SL-RLF. Theoretically, under the corresponding label-error conditions, the optimal detection model learned by SL-RLF from partially mislabeled training data can achieve the same detection performance under the true data distribution as the optimal model learned from accurately labeled data. Experimental results demonstrate that, under different types and levels of partial label errors, the proposed SL-RLF consistently outperforms representative methods, including Co-teaching and TCE, and its robustness is generally consistent with the theoretical analysis.

eess.SP↗

arXiv2026

Hamiltonian-Aware ADAPT Variational Quantum Eigensolver for Molecular Ground-State Simulation

Designing compact ansätze in Variational Quantum Eigensolver (VQE) is crucial for solving energetic problems of practical molecules on near-term quantum devices. However, existing Adaptive Derivative-Assembled Pseudo-Trotter (ADAPT) ansätze face two challenges: improper operator selection and accumulation of degraded operators. In this paper, we propose the Hamiltonian-Aware (HA) ADAPT-VQE algorithm to address these issues. First, we establish a novel excitation operator selection criterion. It breaks the local constraint of existing criteria by incorporating Hamiltonian information, prioritizes physically meaningful excitation operators, and incurs no extra classical or quantum computational overhead. Furthermore, we develop a problem-adaptive method for discriminating and pruning redundant excitation operators stemming from improper selection and inevitable degradation. This method balances redundant operator pruning and convergence guarantee, and is applicable to ansätze with arbitrary scales. Systematic numerical experiments on typical strongly correlated molecular systems demonstrate that our HA-ADAPT-VQE avoids energy plateaus and outperforms baseline algorithms in terms of energy error, ansatz size, and measurement cost. This work offers an efficient, robust ansatz construction paradigm, facilitating the development and practical deployment of large-scale VQE in quantum chemistry.

quant-ph↗

arXiv2026

WiCompass: Oracle-driven Data Scaling for mmWave Human Pose Estimation

Millimeter-wave Human Pose Estimation (mmWave HPE) promises privacy but suffers from poor generalization under distribution shifts. We demonstrate that brute-force data scaling is ineffective for out-of-distribution (OOD) robustness; efficiency and coverage are the true bottlenecks. To address this, we introduce WiCompass, a coverage-aware data-collection framework. WiCompass leverages large-scale motion-capture corpora to build a universal pose space ``oracle'' that quantifies dataset redundancy and identifies underrepresented motions. Guided by this oracle, WiCompass employs a closed-loop policy to prioritize collecting informative missing samples. Experiments show that WiCompass consistently improves OOD accuracy at matched budgets and exhibits superior scaling behavior compared to conventional collection strategies. By shifting focus from brute-force scaling to coverage-aware data acquisition, this work offers a practical path toward robust mmWave sensing.

cs.CV↗

arXiv2026

Constructing Compact ADAPT Unitary Coupled-Cluster Ansatz with Parameter-Based Criterion

The adaptive derivative-assembled pseudo-trotter variational quantum eigensolver (ADAPT-VQE) is a promising hybrid quantum-classical algorithm for molecular ground state energy calculation, yet its practical scalability is hampered by redundant excitation operators and excessive measurement costs. To address these challenges, we propose Param-ADAPT-VQE, a novel improved algorithm that selects excitation operators based on a parameter-based criterion instead of the traditional gradient-based metric. This strategy effectively eludes redundant operators. We further develop a sub-Hamiltonian technique and integrate a hot-start VQE optimization strategy, achieving a significant reduction in measurement costs. Numerical experiments on typical molecular systems demonstrate that Param-ADAPT-VQE outperforms the original ADAPT-VQE in computational accuracy, ansatz size, and measurement costs. Furthermore, our scheme retains the fundamental framework of ADAPT-VQE and is thus fully compatible with its various modified versions, enabling further performance improvements in specific aspects. This work presents an efficient and scalable enhancement to ADAPT-VQE, mitigating the core obstacles that impede its practical implementation in the field of molecular quantum chemistry.

quant-ph↗

arXiv2025

Hamiltonian-Informed Point Group Symmetry-Respecting Ansatz for Variational Quantum Eigensolver

Solving molecular energy levels via the Variational Quantum Eigensolver (VQE) algorithm represents one of the most promising applications for demonstrating practically meaningful quantum advantage in the noisy intermediate-scale quantum (NISQ) era. To strike a balance between ansatz complexity and computational stability in VQE calculations, we propose the HiUCCSD, a novel symmetry-respecting ansatz engineered from the intrinsic information of the Hamiltonian. We theoretically prove the effectiveness of HiUCCSD within the scope of Abelian point groups. Furthermore, we compare the performance of HiUCCSD and the established SymUCCSD via VQE and Adaptive Derivative-Assembled Pseudo-Trotter (ADAPT)-VQE numerical experiments on ten molecules with distinct point groups. The results show that HiUCCSD achieves equivalent performance to SymUCCSD for Abelian point group molecules, while avoiding the potential performance failure of SymUCCSD in the case of non-Abelian point group molecules. Across the studied molecular systems, HiUCCSD cuts the parameter count by 18%-83% for VQE and reduces the excitation operator pool size by 27%-84% for ADAPT-VQE, as compared with the UCCSD ansatz. With enhanced robustness and broader applicability, HiUCCSD offers a new ansatz option for advancing large-scale molecular VQE implementation.

physics.chem-ph↗

arXiv2025

Radar Network Waveform Design for Target Tracking

This paper addresses the synthesis of slow-time coded waveforms for single target tracking in a radar network operating under colored Gaussian interference. Based on the Posterior Cramér Rao Lower Bound (PCRLB), which characterizes the theoretically optimal accuracy of target state estimation, the problem at each tracking frame is formulated as the minimization of the trace of the PCRLB, together with power budget requirements and a similarity constraint to account for transmitter limitations and appropriate waveform features. To tackle this challenging optimization problem, an approximation solution technique is proposed, aimed at better tracking accuracy than the reference code. The resulting approximated problems, endowed with more tractable objective functions through Taylor-series expansion, are solved using a customized block Majorization-Minimization (block-MM) algorithm. The convergence properties of the developed procedure are thoroughly analyzed. Numerical results illustrate the accuracy improvements in the target state estimation process, and robust tracking performance under uncertain target state conditions achieved by the proposed technique.

eess.SP↗

arXiv2025

LCB-CV-UNet: Enhanced Detector for High Dynamic Range Radar Signals

We propose the LCB-CV-UNet to tackle performance degradation caused by High Dynamic Range (HDR) radar signals. Initially, a hardware-efficient, plug-and-play module named Logarithmic Connect Block (LCB) is proposed as a phase coherence preserving solution to address the inherent challenges in handling HDR features. Then, we propose the Dual Hybrid Dataset Construction method to generate a semi-synthetic dataset, approximating typical HDR signal scenarios with adjustable target distributions. Simulation results show about 1% total detection probability improvement with under 0.9% computational complexity added compared with the baseline. Furthermore, it excels 5% over the baseline at the range in 11-13 dB signal-to-noise ratio typical for urban targets. Finally, the real experiment validates the practicality of our model.

eess.SP↗

arXiv2025

Gradient-Based Excitation Filter for Molecular Ground-State Simulation

Molecular ground-state simulation is one of the most promising fields for demonstrating practical quantum advantage on near-term quantum computers. However, the Variational Quantum Eigensolver (VQE), a leading algorithm for this task, still faces significant challenges due to excessive circuit depth. This paper introduces a method to efficiently simplify the Unitary Coupled-Cluster with Single and Double Excitations (UCCSD) ansatz on classical computers. We propose to estimate the correlation energy contributions of excitations using their gradients at Hartree-Fock state, supported by a theoretical proof. For molecular systems with $K$ orbitals, these gradients can be obtained with complexity only $O(K^8)$, which can be efficiently implemented on classical computers, especially in parallel. By sorting and truncating the excitations based on these gradients, the simplified ansatz can be obtained immediately, avoiding the challenging task of optimizing ansatz structure on a quantum computer. Furthermore, we introduce a strategy to indirectly identify critical excitations through spin-adapted constraints, reducing gradient computations by $60\%$. Numerical experiments on prototype molecular systems (H${_4}$, HF, H${_2}$O, BeH${_2}$ and NH$_3$) demonstrate that our approach achieves up to $46\%$ parameter decrease, $60\%$ circuit depth reduction and $678\times$ runtime speedup compared to the state-of-the-art ADAPT-VQE algorithm, enabling significantly more compact quantum circuits with enhanced near-term feasibility.

quant-ph↗

arXiv2025

RH: An Architecture for Redesigning Quantum Circuits on Quantum Hardware Devices

In this paper we present an architecture that enables the redesign of large-scale quantum circuits on quantum hardware based on the entangling quantum generative adversarial network (EQ-GAN). Specifically, by prepending a random quantum circuit module to the standard EQ-GAN framework, we extend its capability from quantum state learning to unitary transformation learning. The completeness of this architecture is theoretically proved. Moreover, an efficient local random circuit is proposed, which significantly enhances the practicality of our architecture. For concreteness, we apply this architecture to three crucial applications in circuit optimization, including the equivalence checking of (non-) parameterized circuits, as well as the variational reconstruction of quantum circuits. The feasibility of our approach is demonstrated by excellent results in both classical and noisy intermediate-scale quantum (NISQ) hardware implementations. We believe our work will facilitate the implementation and validation of the advantages of quantum algorithms.

quant-ph↗

arXiv2025

Radar Code Design for the Joint Optimization of Detection Performance and Measurement Accuracy in Track Maintenance

This paper deals with the design of slow-time coded waveforms which jointly optimize the detection probability and the measurements accuracy for track maintenance in the presence of colored Gaussian interference. The output signal-to-interference-plus-noise ratio (SINR) and Cramér Rao bounds (CRBs) on time delay and Doppler shift are used as figures of merit to accomplish reliable detection as well as accurate measurements. The transmitted code is subject to radar power budget requirements and a similarity constraint. To tackle the resulting non-convex multi-objective optimization problem, a polynomial-time algorithm that integrates scalarization and tensor-based relaxation methods is developed. The corresponding relaxed multi-linear problems are solved by means of the maximum block improvement (MBI) framework, where the optimal solution at each iteration is obtained in closed form. Numeral results demonstrate the trade-off between the detection and the estimation performance, along with the acceptable Doppler robustness achieved by the proposed algorithm.

eess.SP↗

arXiv2024

Efficient dual-scale generalized Radon-Fourier transform detector family for long time coherent integration

Long Time Coherent Integration (LTCI) aims to accumulate target energy through long time integration, which is an effective method for the detection of a weak target. However, for a moving target, defocusing can occur due to range migration (RM) and Doppler frequency migration (DFM). To address this issue, RM and DFM corrections are required in order to achieve a well-focused image for the subsequent detection. Since RM and DFM are induced by the same motion parameters, existing approaches such as the generalized Radon-Fourier transform (GRFT) or the keystone transform (KT)-matching filter process (MFP) adopt the same search space for the motion parameters in order to eliminate both effects, thus leading to large redundancy in computation. To this end, this paper first proposes a dual-scale decomposition of the target motion parameters, consisting of well designed coarse and fine motion parameters. Then, utilizing this decomposition, the joint correction of the RM and DFM effects is decoupled into a cascade procedure, first RM correction on the coarse search space and then DFM correction on the fine search spaces. As such, step size of the search space can be tailored to RM and DFM corrections, respectively, thus avoiding large redundant computation effectively. The resulting algorithms are called dual-scale GRFT (DS-GRFT) or dual-scale GRFT (DS-KTMFP) which provide comparable performance while achieving significant improvement in computational efficiency compared to standard GRFT (KT-MFP). Simulation experiments verify their effectiveness and efficiency.

eess.SY↗

arXiv2023

Enhanced Low-Redundancy Restricted Array for Direction of Arrival Estimation

Sensor arrays play a significant role in direction of arrival (DOA) estimation. Specifically, arrays with low redundancy and reduced mutual coupling are desirable. In this paper, we investigate a sensor array configuration that has a restricted sensor spacing and propose a closed-form expression. We also propose several classes of low redundancy (LR) arrays. Interestingly, compared with super nested arrays (SNA) and maximum inter-element spacing constraint (MISC) arrays, one of the proposed arrays has a significant reduction in both redundancy ratio and mutual coupling. Numerical simulations are also conducted to verify the superiority of the proposed array over the known sparse arrays in terms of weight functions, mutual coupling matrices as well and DOA estimation performance.

cs.IT↗

arXiv2023

A quantum Poisson solver implementable on NISQ devices (improved version)

Solving differential equations is one of the most compelling applications of quantum computing. Most existing quantum algorithms addressing general ordinary and partial differential equations are thought to be too expensive to execute successfully on Noisy Intermediate-Scale Quantum (NISQ) devices. Here we propose a compact quantum algorithm for solving one-dimensional Poisson equation based on simple Ry rotation. The major operations are performed on probability amplitudes. Therefore, the present algorithm avoids the need to do phase estimation, Hamiltonian simulation and arithmetic. The solution error comes only from the finite difference approximation of the Poisson equation. Our quantum Poisson solver (QPS) has gate-complexity of 3n in qubits and 4n^3 in one- and two-qubit gates, where n is the logarithmic of the dimension of the linear system of equations. In terms of solution error ε, the complexity is log(1/ε) in qubits and poly(log(1/ε)) in operations, which is consist with the best known results. The present QPS may represent a potential application on NISQ devices.

quant-ph↗

arXiv2021

Black-Box Quantum State Preparation with Inverse Coefficients

Black-box quantum state preparation is a fundamental building block for many higher-level quantum algorithms, which is applied to transduce the data from computational basis into amplitude. Here we present a new algorithm for performing black-box state preparation with inverse coefficients based on the technique of inequality test. This algorithm can be used as a subroutine to perform the controlled rotation stage of the Harrow-Hassidim-Lloyd (HHL) algorithm and the associated matrix inversion algorithms with exceedingly low cost. Furthermore, we extend this approach to address the general black-box state preparation problem where the transduced coefficient is a general non-linear function. The present algorithm greatly relieves the need to do arithmetic and the error is only resulted from the truncated error of binary string. It is expected that our algorithm will find wide usage both in the NISQ and fault-tolerant quantum algorithms.

quant-ph↗

arXiv2021

Quantum-inspired Complex Convolutional Neural Networks

Quantum-inspired neural network is one of the interesting researches at the junction of the two fields of quantum computing and deep learning. Several models of quantum-inspired neurons with real parameters have been proposed, which are mainly used for three-layer feedforward neural networks. In this work, we improve the quantum-inspired neurons by exploiting the complex-valued weights which have richer representational capacity and better non-linearity. We then extend the method of implementing the quantum-inspired neurons to the convolutional operations, and naturally draw the models of quantum-inspired convolutional neural networks (QICNNs) capable of processing high-dimensional data. Five specific structures of QICNNs are discussed which are different in the way of implementing the convolutional and fully connected layers. The performance of classification accuracy of the five QICNNs are tested on the MNIST and CIFAR-10 datasets. The results show that the QICNNs can perform better in classification accuracy on MNIST dataset than the classical CNN. More learning tasks that our QICNN can outperform the classical counterparts will be found.

quant-ph↗