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Kaining Zhang

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

SGPFeat: Semantic and Geometric Priors for Multi-modal Image Matching

Yuxin Deng
Botian Wang
Kaining Zhang
Hao Zhang
Jiayi Ma

Multi-modal image matching is a fundamental task in multi-view and multi-modal image processing. Its key challenge lies in extracting features that remain consistent despite drastic appearance variations across modalities. However, the learning of the feature is hindered by the scarcity and the inaccurate alignment of existing multi-modal datasets. To address this, we propose a knowledge distillation framework termed SGPFeat that transfers rich prior knowledge from large-scale unimodal tasks to enhance multi-modal representation learning. Specifically, semantic priors from a vision foundation model guide the feature extractor to identify shared semantic structures across modalities, enabling better generalization under large appearance gaps. In parallel, geometric priors derived from accurately aligned visible-light datasets improve detection precision on noisy aligned multi-modal pairs. Furthermore, we introduce a Heterogeneous Feature Aggregation (HFA) module to facilitate effective distillation and feature representation. Extensive experiments demonstrate that semantic and geometric priors bring significant improvement for our SGPFeat across diverse multi-modal image matching benchmarks.

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NeurIPS Conference 2025 Conference Paper

AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation

Xinbiao Wang
Yuxuan Du
Zihan Lou
Yang Qian
Kaining Zhang
Yong Luo
Bo Du
Dacheng Tao

Quantum many-body problems are central to various scientific disciplines, yet their ground-state properties are intrinsically challenging to estimate. Recent advances in deep learning (DL) offer potential solutions in this field, complementing prior purely classical and quantum approaches. However, existing DL-based models typically assume access to a large-scale and noiseless labeled dataset collected by infinite sampling. This idealization raises fundamental concerns about their practical utility, especially given the limited availability of quantum hardware in the near term. To unleash the power of these DL-based models, we propose AiDE-Q (\underline{a}utomat\underline{i}c \underline{d}ata \underline{e}ngine for \underline{q}uantum property estimation), an effective framework that addresses this challenge by iteratively generating high-quality synthetic labeled datasets. Specifically, AiDE-Q utilizes a confidence-check method to assess the quality of synthetic labels and continuously improves the employed DL models with the identified high-quality synthetic dataset. To verify the effectiveness of AiDE-Q, we conduct extensive numerical simulations on a diverse set of quantum many-body and molecular systems, with up to 50 qubits. The results show that AiDE-Q enhances prediction performance for various reference learning models, with improvements of up to $14. 2\\%$. Moreover, we exhibit that a basic supervised learning model integrated with AiDE-Q outperforms advanced reference models, highlighting the importance of a synthetic dataset. Our work paves the way for more efficient and practical applications of DL for quantum property estimation.

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NeurIPS Conference 2025 Conference Paper

TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQE

Yifeng Peng
Xinyi Li
Samuel Yen-Chi Chen
Kaining Zhang
Zhiding Liang
Ying Wang
Yuxuan Du

Variational quantum Eigensolver (VQE) is a leading candidate for harnessing quantum computers to advance quantum chemistry and materials simulations, yet its training efficiency deteriorates rapidly for large Hamiltonians. Two issues underlie this bottleneck: (i) the no-cloning theorem imposes a linear growth in circuit evaluations with the number of parameters per gradient step; and (ii) deeper circuits encounter barren plateaus (BPs), leading to exponentially increasing measurement overheads. To address these challenges, here we propose a deep learning framework, dubbed Titan, which identifies and freezes inactive parameters of a given ansätze at initialization for a specific class of Hamiltonians, reducing the optimization overhead without sacrificing accuracy. The motivation of Titan starts with our empirical findings that a subset of parameters consistently has negligible influence on training dynamics. Its design combines a theoretically grounded data construction strategy, ensuring each training example is informative and BP-resilient, with an adaptive neural architecture that generalizes across ansätze of varying sizes. Across benchmark transverse-field Ising models, Heisenberg models, and multiple molecule systems up to $30$ qubits, Titan achieves up to $3\times$ faster convergence and $40$–$60\%$ fewer circuit evaluations than state-of-the-art baselines, while matching or surpassing their estimation accuracy. By proactively trimming parameter space, Titan lowers hardware demands and offers a scalable path toward utilizing VQE to advance practical quantum chemistry and materials science.

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AAAI Conference 2024 Conference Paper

ResMatch: Residual Attention Learning for Feature Matching

Yuxin Deng
Kaining Zhang
Shihua Zhang
Yansheng Li
Jiayi Ma

Attention-based graph neural networks have made great progress in feature matching. However, the literature lacks a comprehensive understanding of how the attention mechanism operates for feature matching. In this paper, we rethink cross- and self-attention from the viewpoint of traditional feature matching and filtering. To facilitate the learning of matching and filtering, we incorporate the similarity of descriptors into cross-attention and relative positions into self-attention. In this way, the attention can concentrate on learning residual matching and filtering functions with reference to the basic functions of measuring visual and spatial correlation. Moreover, we leverage descriptor similarity and relative positions to extract inter- and intra-neighbors. Then sparse attention for each point can be performed only within its neighborhoods to acquire higher computation efficiency. Extensive experiments, including feature matching, pose estimation and visual localization, confirm the superiority of the proposed method. Our codes are available at https://github.com/ACuOoOoO/ResMatch.

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ICML Conference 2024 Conference Paper

Sparse-to-dense Multimodal Image Registration via Multi-Task Learning

Kaining Zhang
Jiayi Ma 0001

Aligning image pairs captured by different sensors or those undergoing significant appearance changes is crucial for various computer vision and robotics applications. Existing approaches cope with this problem via either Sparse feature Matching (SM) or Dense direct Alignment (DA) paradigms. Sparse methods are efficient but lack accuracy in textureless scenes, while dense ones are more accurate in all scenes but demand for good initialization. In this paper, we propose SDME, a Sparse-to-Dense Multimodal feature Extractor based on a novel multi-task network that simultaneously predicts SM and DA features for robust multimodal image registration. We propose the sparse-to-dense registration paradigm: we first perform initial registration via SM and then refine the result via DA. By using the well-designed SDME, the sparse-to-dense approach combines the merits from both SM and DA. Extensive experiments on MSCOCO, GoogleEarth, VIS-NIR and VIS-IR-drone datasets demonstrate that our method achieves remarkable performance on multimodal cases. Furthermore, our approach exhibits robust generalization capabilities, enabling the fine-tuning of models initially trained on single-modal datasets for use with smaller multimodal datasets. Our code is available at https: //github. com/KN-Zhang/SDME.

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AAAI Conference 2023 Conference Paper

Offline Quantum Reinforcement Learning in a Conservative Manner

Zhihao Cheng
Kaining Zhang
Li Shen
Dacheng Tao

Recently, to reap the quantum advantage, empowering reinforcement learning (RL) with quantum computing has attracted much attention, which is dubbed as quantum RL (QRL). However, current QRL algorithms employ an online learning scheme, i.e., the policy that is run on a quantum computer needs to interact with the environment to collect experiences, which could be expensive and dangerous for practical applications. In this paper, we aim to solve this problem in an offline learning manner. To be more specific, we develop the first offline quantum RL (offline QRL) algorithm named CQ2L (Conservative Quantum Q-learning), which learns from offline samples and does not require any interaction with the environment. CQ2L utilizes variational quantum circuits (VQCs), which are improved with data re-uploading and scaling parameters, to represent Q-value functions of agents. To suppress the overestimation of Q-values resulting from offline data, we first employ a double Q-learning framework to reduce the overestimation bias; then a penalty term that encourages generating conservative Q-values is designed. We conduct abundant experiments to demonstrate that the proposed method CQ2L can successfully solve offline QRL tasks that the online counterpart could not.

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NeurIPS Conference 2022 Conference Paper

Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits

Kaining Zhang
Liu Liu
Min-Hsiu Hsieh
Dacheng Tao

Variational quantum circuits have been widely employed in quantum simulation and quantum machine learning in recent years. However, quantum circuits with random structures have poor trainability due to the exponentially vanishing gradient with respect to the circuit depth and the qubit number. This result leads to a general standpoint that deep quantum circuits would not be feasible for practical tasks. In this work, we propose an initialization strategy with theoretical guarantees for the vanishing gradient problem in general deep quantum circuits. Specifically, we prove that under proper Gaussian initialized parameters, the norm of the gradient decays at most polynomially when the qubit number and the circuit depth increase. Our theoretical results hold for both the local and the global observable cases, where the latter was believed to have vanishing gradients even for very shallow circuits. Experimental results verify our theoretical findings in quantum simulation and quantum chemistry.

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ICRA Conference 2021 Conference Paper

Appearance-based Loop Closure Detection via Bidirectional Manifold Representation Consensus

Kaining Zhang
Zizhuo Li
Jiayi Ma 0001

Loop closure detection (LCD), which aims to deal with the drift emerging when robots travel around the route, plays a key role in a simultaneous localization and mapping system. Unlike most current methods which focus on seeking an appropriate representation of images, we propose a novel two-stage pipeline dominated by the estimation of spatial geometric relationship. When a query image occurs, we select candidates on-line according to the similarity of global semantic features in the first stage, and then conduct robust geometric confirmation to verify true loop-closing pairs in the second stage. To this end, a robust feature matching algorithm, termed as bidirectional manifold representation consensus (BMRC), is proposed. In particular, we utilize manifold representation to construct local neighborhood structures of feature points and formulate the matching problem into an optimization model, enabling linearithmic time complexity via a closed-form solution. Furthermore, we propose a dynamic place partition strategy based on BMRC to segment image streams with similar content into a place, which can mine more valid candidate frames, improving the recall rate of the whole system. Extensive experiments on several publicly available datasets reveal that BMRC has a good performance in the general feature matching task and the proposed pipeline outperforms the current state-of-the-art approaches in the LCD task.

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IROS Conference 2021 Conference Paper

Motion Field Consensus with Locality Preservation: A Geometric Confirmation Strategy for Loop Closure Detection

Kaining Zhang
Xingyu Jiang
Xiaoguang Mei
Huabing Zhou
Jiayi Ma 0001

Loop closure detection (LCD), which aims to deal with the drift emerging when robots travel around the route, plays a key role in a simultaneous localization and mapping system. Unlike most current methods which focus on seeking an appropriate representation of images, we propose a novel two-stage pipeline dominated by the estimation of spatial geometric relationship. When a query image occurs, we select semantically similar images based on the SuperPoint network and the aggregated selective match kernel in the first stage, and then conduct robust geometric confirmation to verify true loop-closing pairs in the second stage. Based on the potential property of motion field in the LCD scene, a robust feature matching algorithm, termed as motion field consensus with locality preservation (MFC-LP), is proposed. In particular, we exploit the smoothness prior to guide the learning of the motion field for an image pair in a reproducing kernel Hilbert space (RKHS). Meanwhile, to enhance the local relevance of motion vectors, we design a locality preservation mechanism thus making the learned motion field more accurate. Extensive experiments on several publicly available datasets reveal that MFC-LP has a good performance in the general feature matching task and the proposed pipeline outperforms the current state-of-the-art approaches in the LCD task.

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