Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Journal of Artificial Intelligence and Machine Learning

Quantum Logic Gates for Accelerating Graph Convolutional Network Operations

M Kasiselvanathan, Dr.M Kalaivani, Dr.P Ambika, K Keerthika

Published May 24, 2026• 0 citations
Publisher

Abstract

Graph Convolutional Networks (GCNs) have been widely used to learn from graph structured data, but GCNs suffer from a high complexity in the handling of large-scale graphs, since processing large graphs demands recursive aggregation of features and high dimensional node embeddings. Scalability can become a tight bottleneck in traditional CPU/GPU implementations, which detracts from their ability to be used in real-time and resource-constrained settings. In this study, Quantum-assisted GCN (QGCN) is proposed as a quantum-assisted GCN model with the help of quantum logic gates, thereby speeding up the graph convolution operation. The idea proposed in the paper is to represent the queries and graph features as qubits and use a sequence of both single and multi-qubit gates such as Hadamard, CNOT and rotation gates with learned parameters to convolute a graph over qubits simultaneously. Once measured, the quantum state is represented classically, in the form of features, which are then used to feed downstream node classification tasks. On the benchmark datasets Cora, PubMed, and Citeseer, experiments show that the quantum-assisted GCN delivers substantial speed-up over a classical implementation, approaches the performance of the GPU-optimized implementation, and maintains classification within a 0.5–1% margin. If NISQ simulators are to scale up to very large graphs, there are resource utilization issues, which is manageable for current simulators given the number of qubits and gate depth. These results show concrete compromises between acceleration, accuracy preservation, and quantum resource efficiency. The results of this study could be useful for designing hybrid quantum-classical graph learning architectures and represent one possible method for performing faster GCN operations in both academic research and applications. Extensions and developments are focused on multi-layer quantum GCNs as well as hybrid architectures for large-scale GWs and real-time deployment strategies.

DOI
10.51483/ijaiml.6.2s.2026.602-606

Related papers

On the Design of Quantum Graph Convolutional Neural Network in the NISQ-Era and Beyond

Zhirui Hu, Jinyang Li, Zhenyu Pan +6 more

The rapid growth in the size of Graph Convolutional Neural Networks (GCNs) encounters both computational- and memory-wall on classical computing platforms (e.g., CPU, GPU, FPGA, etc.). Quantum computing, on the other hand, provides extremely high parallelism for computation. Although quantum neural networks have been recently studied, the research on quantum graph neural networks is still in its infancy. The key challenge here is how to integrate both the graph topology information and the learning ability of GCNs into quantum circuits. In this work, we leverage the Givens rotations and its quantum implementation to encode graph information; in addition, we employ the widely used variational quantum circuit to bring the learnable parameters. On top of these, we present a full-quantum design of Graph Convolutional Neural Networks, namely "QuGCN", for semi-supervised learning on graph-structured data. Experiment results show our design is competitive with classical GCNs in terms of node classification accuracy on Cora sub-dataset. More importantly, we show the potential advantages that can be achieved by the proposed quantum GCN design when the number of features grows.

IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD)•Oct 1, 2022•28 citations

Quantum-Inspired Hardware Acceleration for Graph Convolution

Jincheng Zhang

Graph Convolution (GC) is a fundamental operation in numerous machine learning tasks, including node classification, link prediction, and graph classification. However, the computational complexity of traditional GC algorithms, particularly when applied to large-scale graphs, presents a significant bottleneck. This paper proposes a novel approach to accelerate GC operations by designing specialized hardware accelerators inspired by quantum computing principles. The core idea is to exploit superposition and entanglement-like behavior to enable fundamentally parallel computations. We present a detailed framework for such accelerators, focusing on the mathematical underpinnings of the GC operation and translating them into hardware-executable instructions. The design emphasizes efficient data representation, parallel processing, and minimizing redundant computations. The proposed architecture aims to drastically reduce the time complexity of GC, paving the way for more efficient and scalable graph-based machine learning applications. Specifically, we explore approaches for representing graph adjacency matrices and node features in a manner conducive to parallel processing, and introduce a novel dataflow model optimized for the inherent parallelism of GC. The theoretical analysis demonstrates a potential reduction in computational complexity, and we outline a design strategy for implementing this acceleration on custom hardware.

•Sep 5, 2026•0 citations

A Novel Quantum Convolution Neural Network for Image Classification Applications

Venkatachalapathy Madhavanna Venkatappa, Venkateshappa

Image classification plays a vital role in large-scale data analysis, especially in object recognition tasks using advanced Deep Learning (DL) frameworks. However, the growing complexity and computational demands of modern DL models have introduced challenges related to scalability and efficiency. Quantum Computing (QC) has emerged as a promising alternative, capable of addressing these limitations by leveraging the principles of Quantum Machine Learning (QML). However, many existing QML models require a large number of qubits, which poses limitations within the current Noisy Intermediate-Scale Quantum (NISQ) era. This work introduces a novel Adaptive Quantum Convolutional Neural Network (AQCNN) designed for efficient and scalable image classification within the constraints of NISQ devices. Addressing the limitations of existing QML approaches, particularly the high qubit requirements, AQCNN incorporates a resource-efficient quantum convolutional layer that performs localized quantum filtering using parameterized quantum circuits. A classical preprocessing layer encodes input features to reduce qubit load, followed by quantum embedding and hybrid quantum-classical layers that optimize feature extraction and classification performance. The model leverages an adaptive quantum convolution strategy, minimizing quantum gate depth and circuit complexity while preserving critical spatial hierarchies in image data. Evaluated on benchmark datasets, AQCNN achieved 95.88% accuracy on MNIST and 95.68% on FMNIST, outperforming comparable QML architectures. Additionally, the model supports scalable execution through parallel quantum circuit arrays, enabling practical deployment on current quantum hardware. This architecture demonstrates a significant advance in quantum-assisted image classification, balancing performance with qubit and gate efficiency. The integration of adaptive quantum convolution and hybrid processing not only enhances classification accuracy but also provides a viable path forward for deploying QML solutions under realistic hardware constraints.

Engineering Technology & Applied Science Research•Oct 6, 2025•2 citations

Quantum Graph Convolutional Neural Networks

Jin Yan Zheng, Qing Zhong Gao, Yanxuan Lü

At present, there are a large number of quantum neural network models to deal with Euclidean spatial data, while little research have been conducted on non-Euclidean spatial data. In this paper, we propose a novel quantum graph convolutional neural network (QGCN) model based on quantum parametric circuits and utilize the computing power of quantum systems to accomplish graph classification tasks in traditional machine learning. The proposed QGCN model has a similar architecture as the classical graph convolutional neural networks, which can illustrate the topology of the graph type data and efficiently learn the hidden layer representation of node features as well. Numerical simulation results on a graph dataset demonstrate that the proposed model can be effectively trained and has good performance in graph level classification tasks.

arXiv:2107.03257•Chinese Control Conference (CCC)•Jul 26, 2021•36 citations

Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Era

Armin Ahmadkhaniha, Jake Doliskani

Graph neural networks (GNNs) are a powerful framework for learning representations from graph-structured data, but their direct implementation on near-term quantum hardware remains challenging due to circuit depth, multi-qubit interactions, and qubit scalability constraints. In this work, we introduce a hybrid quantum graph learning architecture designed explicitly for unsupervised learning in the noisy intermediate-scale quantum (NISQ) regime. Our approach combines a variational quantum feature extraction layer with an edge-local and qubit-efficient quantum message-passing mechanism inspired by the Quantum Alternating Operator Ansatz (QAOA) framework. The message-passing operation is decomposed into pairwise interactions along graph edges using standard single- and two-qubit gates. For a graph with $N$ nodes and $n$-qubit feature registers, this reduces the number of qubits required at one time from $Nn$ to at most $2n$. We train the model using the Deep Graph Infomax objective to perform unsupervised node representation learning. The external class labels are not used during graph construction or training and are used only to evaluate the learned embeddings. Experiments on the Cora citation network and the Phase 3 release of the 1000 Genomes Project show that the quantum edge interaction contributes to the quality of the learned node representations.

quant-pharXiv:2602.16018•Feb 19, 2026•0 citations

Novel Architecture of Parameterized Quantum Circuit for Graph Convolutional Network

Yanhu Chen, Cen Wang, Hongxiang Guo +1 more

Recently, the implementation of quantum neural networks is based on noisy intermediate-scale quantum (NISQ) devices. Parameterized quantum circuit (PQC) is such the method, and its current design just can handle linear data classification. However, data in the real world often shows a topological structure. In the machine learning field, the classical graph convolutional layer (GCL)-based graph convolutional network (GCN) can well handle the topological data. Inspired by the architecture of a classical GCN, in this paper, to expand the function of the PQC, we design a novel PQC architecture to realize a quantum GCN (QGCN). More specifically, we first implement an adjacent matrix based on linear combination unitary and a weight matrix in a quantum GCL, and then by stacking multiple GCLs, we obtain the QGCN. In addition, we first achieve gradients decent on quantum circuit following the parameter-shift rule for a GCL and then for the QGCN. We evaluate the performance of the QGCN by conducting a node classification task on Cora dataset with topological data. The numerical simulation result shows that QGCN has the same performance as its classical counterpart, the GCN, in contrast, requires less tunable parameters. Compared to a traditional PQC, we also verify that deploying an extra adjacent matrix can significantly improve the classification performance for quantum topological data.

arXiv:2203.03251•Mar 7, 2022•1 citation
Graph Convolutional Networks (GCNs) have been widely used to learn from graph structured data, but GCNs suffer from a high complexity in the handling of large-scale graphs, since processing large graphs demands recursive aggregation of features and high dimensional node embeddings. Scalability can become a tight bottleneck in traditional CPU/GPU implementations, which detracts from their ability to be used in real-time and resource-constrained settings. In this study, Quantum-assisted GCN (QGCN) is proposed as a quantum-assisted GCN model with the help of quantum logic gates, thereby speeding up the graph convolution operation. The idea proposed in the paper is to represent the queries and graph features as qubits and use a sequence of both single and multi-qubit gates such as Hadamard, CNOT and rotation gates with learned parameters to convolute a graph over qubits simultaneously. Once measured, the quantum state is represented classically, in the form of features, which are then used to feed downstream node classification tasks. On the benchmark datasets Cora, PubMed, and Citeseer, experiments show that the quantum-assisted GCN delivers substantial speed-up over a classical implementation, approaches the performance of the GPU-optimized implementation, and maintains classification within a 0.5–1% margin. If NISQ simulators are to scale up to very large graphs, there are resource utilization issues, which is manageable for current simulators given the number of qubits and gate depth. These results show concrete compromises between acceleration, accuracy preservation, and quantum resource efficiency. The results of this study could be useful for designing hybrid quantum-classical graph learning architectures and represent one possible method for performing faster GCN operations in both academic research and applications. Extensions and developments are focused on multi-layer quantum GCNs as well as hybrid architectures for large-scale GWs and real-time deployment strategies.
The rapid growth in the size of Graph Convolutional Neural Networks (GCNs) encounters both computational- and memory-wall on classical computing platforms (e.g., CPU, GPU, FPGA, etc.). Quantum computing, on the other hand, provides extremely high parallelism for computation. Although quantum neural networks have been recently studied, the research on quantum graph neural networks is still in its infancy. The key challenge here is how to integrate both the graph topology information and the learning ability of GCNs into quantum circuits. In this work, we leverage the Givens rotations and its quantum implementation to encode graph information; in addition, we employ the widely used variational quantum circuit to bring the learnable parameters. On top of these, we present a full-quantum design of Graph Convolutional Neural Networks, namely "QuGCN", for semi-supervised learning on graph-structured data. Experiment results show our design is competitive with classical GCNs in terms of node classification accuracy on Cora sub-dataset. More importantly, we show the potential advantages that can be achieved by the proposed quantum GCN design when the number of features grows.
Graph Convolution (GC) is a fundamental operation in numerous machine learning tasks, including node classification, link prediction, and graph classification. However, the computational complexity of traditional GC algorithms, particularly when applied to large-scale graphs, presents a significant bottleneck. This paper proposes a novel approach to accelerate GC operations by designing specialized hardware accelerators inspired by quantum computing principles. The core idea is to exploit superposition and entanglement-like behavior to enable fundamentally parallel computations. We present a detailed framework for such accelerators, focusing on the mathematical underpinnings of the GC operation and translating them into hardware-executable instructions. The design emphasizes efficient data representation, parallel processing, and minimizing redundant computations. The proposed architecture aims to drastically reduce the time complexity of GC, paving the way for more efficient and scalable graph-based machine learning applications. Specifically, we explore approaches for representing graph adjacency matrices and node features in a manner conducive to parallel processing, and introduce a novel dataflow model optimized for the inherent parallelism of GC. The theoretical analysis demonstrates a potential reduction in computational complexity, and we outline a design strategy for implementing this acceleration on custom hardware.
Image classification plays a vital role in large-scale data analysis, especially in object recognition tasks using advanced Deep Learning (DL) frameworks. However, the growing complexity and computational demands of modern DL models have introduced challenges related to scalability and efficiency. Quantum Computing (QC) has emerged as a promising alternative, capable of addressing these limitations by leveraging the principles of Quantum Machine Learning (QML). However, many existing QML models require a large number of qubits, which poses limitations within the current Noisy Intermediate-Scale Quantum (NISQ) era. This work introduces a novel Adaptive Quantum Convolutional Neural Network (AQCNN) designed for efficient and scalable image classification within the constraints of NISQ devices. Addressing the limitations of existing QML approaches, particularly the high qubit requirements, AQCNN incorporates a resource-efficient quantum convolutional layer that performs localized quantum filtering using parameterized quantum circuits. A classical preprocessing layer encodes input features to reduce qubit load, followed by quantum embedding and hybrid quantum-classical layers that optimize feature extraction and classification performance. The model leverages an adaptive quantum convolution strategy, minimizing quantum gate depth and circuit complexity while preserving critical spatial hierarchies in image data. Evaluated on benchmark datasets, AQCNN achieved 95.88% accuracy on MNIST and 95.68% on FMNIST, outperforming comparable QML architectures. Additionally, the model supports scalable execution through parallel quantum circuit arrays, enabling practical deployment on current quantum hardware. This architecture demonstrates a significant advance in quantum-assisted image classification, balancing performance with qubit and gate efficiency. The integration of adaptive quantum convolution and hybrid processing not only enhances classification accuracy but also provides a viable path forward for deploying QML solutions under realistic hardware constraints.
At present, there are a large number of quantum neural network models to deal with Euclidean spatial data, while little research have been conducted on non-Euclidean spatial data. In this paper, we propose a novel quantum graph convolutional neural network (QGCN) model based on quantum parametric circuits and utilize the computing power of quantum systems to accomplish graph classification tasks in traditional machine learning. The proposed QGCN model has a similar architecture as the classical graph convolutional neural networks, which can illustrate the topology of the graph type data and efficiently learn the hidden layer representation of node features as well. Numerical simulation results on a graph dataset demonstrate that the proposed model can be effectively trained and has good performance in graph level classification tasks.
Graph neural networks (GNNs) are a powerful framework for learning representations from graph-structured data, but their direct implementation on near-term quantum hardware remains challenging due to circuit depth, multi-qubit interactions, and qubit scalability constraints. In this work, we introduce a hybrid quantum graph learning architecture designed explicitly for unsupervised learning in the noisy intermediate-scale quantum (NISQ) regime. Our approach combines a variational quantum feature extraction layer with an edge-local and qubit-efficient quantum message-passing mechanism inspired by the Quantum Alternating Operator Ansatz (QAOA) framework. The message-passing operation is decomposed into pairwise interactions along graph edges using standard single- and two-qubit gates. For a graph with NNN nodes and nnn-qubit feature registers, this reduces the number of qubits required at one time from NnNnNn to at most 2n2n2n. We train the model using the Deep Graph Infomax objective to perform unsupervised node representation learning. The external class labels are not used during graph construction or training and are used only to evaluate the learned embeddings. Experiments on the Cora citation network and the Phase 3 release of the 1000 Genomes Project show that the quantum edge interaction contributes to the quality of the learned node representations.
Recently, the implementation of quantum neural networks is based on noisy intermediate-scale quantum (NISQ) devices. Parameterized quantum circuit (PQC) is such the method, and its current design just can handle linear data classification. However, data in the real world often shows a topological structure. In the machine learning field, the classical graph convolutional layer (GCL)-based graph convolutional network (GCN) can well handle the topological data. Inspired by the architecture of a classical GCN, in this paper, to expand the function of the PQC, we design a novel PQC architecture to realize a quantum GCN (QGCN). More specifically, we first implement an adjacent matrix based on linear combination unitary and a weight matrix in a quantum GCL, and then by stacking multiple GCLs, we obtain the QGCN. In addition, we first achieve gradients decent on quantum circuit following the parameter-shift rule for a GCL and then for the QGCN. We evaluate the performance of the QGCN by conducting a node classification task on Cora dataset with topological data. The numerical simulation result shows that QGCN has the same performance as its classical counterpart, the GCN, in contrast, requires less tunable parameters. Compared to a traditional PQC, we also verify that deploying an extra adjacent matrix can significantly improve the classification performance for quantum topological data.