Author name cluster

Xinyu Ye

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8 papers

2 author rows

ICLR Conference 2025 Conference Paper

Beyond Circuit Connections: A Non-Message Passing Graph Transformer Approach for Quantum Error Mitigation

Tianyi Bao
Xinyu Ye
Hang Ruan
Chang Liu 0021
Wenjie Wu
Junchi Yan

Despite the progress in quantum computing, one major bottleneck against the practical utility is its susceptibility to noise, which frequently occurs in current quantum systems. Existing quantum error mitigation (QEM) methods either lack generality to noise and circuit types or fail to capture the global dependencies of entire systems in addition to circuit structure. In this work, we first propose a unique circuit-to-graph encoding scheme with qubit-wise noisy measurement aggregated. Then, we introduce GTranQEM, a non-message passing graph transformer designed to mitigate errors in expected circuit measurement outcomes effectively. GTranQEM is equipped with a quantum-specific positional encoding, a structure matrix as attention bias guiding nonlocal aggregation, and a virtual quantum-representative node to further grasp graph representations, which guarantees to model the long-range entanglement. Experimental evaluations demonstrate that GTranQEM outperforms state-of-the-art QEM methods on both random and structured quantum circuits across noise types and scales among diverse settings.

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NeurIPS Conference 2025 Conference Paper

KRIS-Bench: Benchmarking Next-Level Intelligent Image Editing Models

Yongliang Wu
Zonghui Li
Xinting Hu
Xinyu Ye
Xianfang Zeng
Gang Yu
Wenbo Zhu
Bernt Schiele

Recent advances in multi-modal generative models have enabled significant progress in instruction-based image editing. However, while these models produce visually plausible outputs, their capacity for knowledge-based reasoning editing tasks remains under-explored. In this paper, We introduce KRIS-Bench (Knowledge-based Reasoning in Image-editing Systems Benchmark), a diagnostic benchmark designed to assess models through a cognitively informed lens. Drawing from educational theory, KRIS-Bench categorizes editing tasks across three foundational knowledge types: Factual, Conceptual, and Procedural. Based on this taxonomy, we design 22 representative tasks spanning 7 reasoning dimensions and release 1, 267 high-quality annotated editing instances. To support fine-grained evaluation, we propose a comprehensive protocol that incorporates a novel Knowledge Plausibility metric, enhanced by knowledge hints and calibrated through human studies. Empirical results on nine state-of-the-art models reveal significant gaps in reasoning performance, highlighting the need for knowledge-centric benchmarks to advance the development of intelligent image editing systems.

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ICLR Conference 2025 Conference Paper

On Designing General and Expressive Quantum Graph Neural Networks with Applications to MILP Instance Representation

Xinyu Ye
Hao Xiong 0003
Jianhao Huang
Ziang Chen
Jia Wang
Junchi Yan

Graph-structured data is ubiquitous, and graph learning models have recently been extended to address complex problems like mixed-integer linear programming (MILP). However, studies have shown that the vanilla message-passing based graph neural networks (GNNs) suffer inherent limitations in learning MILP instance representation, i.e., GNNs may map two different MILP instance graphs to the same representation. In this paper, we introduce an expressive quantum graph learning approach, leveraging quantum circuits to recognize patterns that are difficult for classical methods to learn. Specifically, the proposed General Quantum Graph Learning Architecture (GQGLA) is composed of a node feature layer, a graph message interaction layer, and an optional auxiliary layer. Its generality is reflected in effectively encoding features of nodes and edges while ensuring node permutation equivariance and flexibly creating different circuit structures for various expressive requirements and downstream tasks. GQGLA is well suited for learning complex graph tasks like MILP representation. Experimental results highlight the effectiveness of GQGLA in capturing and learning representations for MILPs. In comparison to traditional GNNs, GQGLA exhibits superior discriminative capabilities and demonstrates enhanced generalization across various problem instances, making it a promising solution for complex graph tasks.

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ICML Conference 2025 Conference Paper

QEM-Bench: Benchmarking Learning-based Quantum Error Mitigation and QEMFormer as a Multi-ranged Context Learning Baseline

Tianyi Bao
Ruizhe Zhong
Xinyu Ye
Yehui Tang 0002
Junchi Yan

Quantum Error Mitigation (QEM) has emerged as a pivotal technique for enhancing the reliability of noisy quantum devices in the Noisy Intermediate-Scale Quantum (NISQ) era. Recently, machine learning (ML)-based QEM approaches have demonstrated strong generalization capabilities without sampling overheads compared to conventional methods. However, evaluating these techniques is often hindered by a lack of standardized datasets and inconsistent experimental settings across different studies. In this work, we present QEM-Bench, a comprehensive benchmark suite of twenty-two datasets covering diverse circuit types and noise profiles, which provides a unified platform for comparing and advancing ML-based QEM methods. We further propose a refined ML-based QEM pipeline QEMFormer, which leverages a feature encoder that preserves local, global, and topological information, along with a two-branch model that captures short-range and long-range dependencies within the circuit. Empirical evaluations on QEM-Bench illustrate the superior performance of QEMFormer over existing baselines, underscoring the potential of integrated ML-QEM strategies.

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IJCAI Conference 2025 Conference Paper

Tensor Network: from the Perspective of AI4Science and Science4AI

Junchi Yan
Yehui Tang
Xinyu Ye
Hao Xiong
Xiaoqiu Zhong
Yuhan Wang
Yuan Qi

Tensor network has been a promising numerical tool for computational problems across science and AI. For their emerging and fast development especially in the intersection between AI and science, this paper tries to present a compact review, regarding both their applications and its own recent technical development including open-source tools. Specifically, we make the observations that tensor network plays a functional role in matrix compression and representation, information fusion, as well as quantum-inspired algorithms, which can be generally regarded as Science4AI in our survey. On the other hand, there is an emerging line of research in tensor network in AI4Science especially like learning quantum many-body physics by using e. g. neural network quantum state. Importantly, we unify tensorization methodologies across classical and modern architectures, and particularly show how tensorization bridges low-order parameter spaces to high-dimensional representations without exponential parameter growth, and further point out their potential use in scientific computing. We conclude the paper with outlook for future trends.

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NeurIPS Conference 2024 Conference Paper

QVAE-Mole: The Quantum VAE with Spherical Latent Variable Learning for 3-D Molecule Generation

Huanjin Wu
Xinyu Ye
Junchi Yan

Molecule generation ideally in its 3-D form has enjoyed wide applications in material, chemistry, life science, etc. We propose the first quantum parametric circuit for 3-D molecule generation for its potential quantum advantage especially considering the arrival of Noisy Intermediate-Scale Quantum (NISQ) era. We choose the Variational AutoEncoder (VAE) scheme for its simplicity and one-shot generation ability, which we believe is more quantum-friendly compared with the auto-regressive generative models or diffusion models as used in classic approaches. Specifically, we present a quantum encoding scheme designed for 3-D molecules with qubits complexity $\mathcal{O}(C\log n)$ ($n$ is the number of atoms) and adopt a von Mises-Fisher (vMF) distributed latent space to meet the inherent coherence of the quantum system. We further design to encode conditions into quantum circuits for property-specified generation. Experimentally, our model could generate plausible 3-D molecules and achieve competitive quantitative performance with significantly reduced circuit parameters compared with their classic counterparts. The source code will be released upon publication.

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ICML Conference 2023 Conference Paper

Towards Quantum Machine Learning for Constrained Combinatorial Optimization: a Quantum QAP Solver

Xinyu Ye
Ge Yan 0001
Junchi Yan

Combinatorial optimization (CO) on the graph is a crucial but challenging research topic. Recent quantum algorithms provide a new perspective for solving CO problems and have the potential to demonstrate quantum advantage. Quantum Approximate Optimization Algorithm (QAOA) is a well-known quantum heuristic for CO constructed by a parametric quantum circuit. However, QAOA is originally designed for unconstrained problems and the circuit parameters and solutions are jointly solved with time-consuming iterations. In this paper, we propose a novel quantum neural network (QNN) for learning CO problems in a supervised manner to achieve better and faster results. We focus on the Quadratic Assignment Problem (QAP) with matching constraints and the node permutation invariance property. To this end, a quantum neural network called QAP-QNN is devised to translate the QAP into a constrained vertex classification task. Moreover, we study two QAP tasks: Graph Matching and Traveling Salesman Problem on TorchQauntum simulators, and empirically show the effectiveness of our approach.

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ICRA Conference 2021 Conference Paper

Visual Place Recognition via Local Affine Preserving Matching

Xinyu Ye
Jiayi Ma 0001

Visual Place Recognition (VPR) is a crucial component for long-term mobile robot autonomy. In this paper, we exploit a coarse-to-fine paradigm to recognize places. In particular, we first select candidate frames for each query image, and then check the spatial geometric relationship between the query and its candidate frames to determine the final place match. In the coarse match stage, we employ the deep learning network to extract global features that encode semantic information of images, then by comparing the similarity between features to obtain a candidate list of the query place. In the fine match stage, we propose an effective and efficient feature matching algorithm for real-time geometrical verification of candidate places, termed as local affine preserving matching (LAP). Extensive experimental results demonstrate that our LAP can significantly promote the VPR performance, and the proposed overall VPR method can achieve much better performance over the current state-of-the-art approaches.

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