ABSTRACT While Quantum Convolutional Neural Networks (QCNNs) offer a structured framework for data processing in the noisy intermediate‐scale quantum (NISQ) regime, conventional architectures often rely on over‐parameterized ansätze that hinder trainability and increase sensitivity to hardware noise. Furthermore, standard quantum pooling strategies frequently discard useful coherent correlations. To address these limitations, we propose a parameter‐efficient QCNN architecture integrating a ‐equivariant convolutional ( Eq‐Conv ) kernel with a guided variational pooling ( Guided Var‐Pool ) scheme. By enforcing continuous symmetry via Schur's Lemma, the Eq‐Conv kernel requires only five trainable parameters, significantly reducing circuit complexity compared to full‐rank operations while maintaining competitive expressivity. For dimensionality reduction, the Guided Var‐Pool introduces an Information‐Bottleneck‐inspired penalty that actively drives discarded qubits toward the ground state, ensuring task‐relevant features are concentrated within the retained subsystem prior to truncation. To maintain NISQ compatibility, the architecture strictly adheres to the Principle of Deferred Measurement, eliminating the need for mid‐circuit measurements. Numerical experiments on MNIST and Fashion‐MNIST datasets demonstrate that our approach achieves competitive classification accuracy while exhibiting superior robustness against depolarizing noise. Compared to over‐parameterized baselines, the proposed model establishes a favorable depth‐for‐robustness trade‐off, providing a resource‐efficient pathway for scalable quantum machine learning.
ABSTRACT While Quantum Convolutional Neural Networks (QCNNs) offer a structured framework for data processing in the noisy intermediate‐scale quantum (NISQ) regime, conventional architectures often rely on over‐parameterized ansätze that hinder trainability and increase sensitivity to hardware noise. Furthermore, standard quantum pooling strategies frequently discard useful coherent correlations. To address these limitations, we propose a parameter‐efficient QCNN architecture integrating a ‐equivariant convolutional ( Eq‐Conv ) kernel with a guided variational pooling ( Guided Var‐Pool ) scheme. By enforcing continuous symmetry via Schur's Lemma, the Eq‐Conv kernel requires only five trainable parameters, significantly reducing circuit complexity compared to full‐rank operations while maintaining competitive expressivity. For dimensionality reduction, the Guided Var‐Pool introduces an Information‐Bottleneck‐inspired penalty that actively drives discarded qubits toward the ground state, ensuring task‐relevant features are concentrated within the retained subsystem prior to truncation. To maintain NISQ compatibility, the architecture strictly adheres to the Principle of Deferred Measurement, eliminating the need for mid‐circuit measurements. Numerical experiments on MNIST and Fashion‐MNIST datasets demonstrate that our approach achieves competitive classification accuracy while exhibiting superior robustness against depolarizing noise. Compared to over‐parameterized baselines, the proposed model establishes a favorable depth‐for‐robustness trade‐off, providing a resource‐efficient pathway for scalable quantum machine learning.
This study presents an advanced Quantum Convolutional Neural Network (QCNN) architecture designed to improve classification performance and training stability within the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices. The proposed model incorporates a multi-dimensional quantum feature encoding stage, followed by entanglement-enriched convolutional layers and a refined quantum pooling mechanism. These design innovations increase the circuit’s expressive capacity while reducing susceptibility to barren plateau phenomena, thereby supporting more efficient optimization. Extensive numerical simulations indicate that the model achieves a classification accuracy of 95.2%, with a final loss of 0.24, a low output variance of 0.031, and improved expressibility characterized by a mean fidelity of 0.07. Collectively, these results demonstrate that the enhanced QCNN provides a robust and scalable framework for quantum machine learning tasks suitable for current and near-term quantum hardware.
As image datasets grow in scale and complexity, classical convolutional neural networks (CNNs) increasingly face limitations in computational efficiency, scalability, and generalisation in low-data regimes. Quantum machine learning (QML) offers a promising alternative, with quantum convolutional neural networks (QCNNs) exploiting quantum parallelism and entanglement for feature extraction. This paper introduces the Novel Quantum Convolutional Neural Network (No-QCNN), which differs from recent hybrid QCNN/QCQ-CNN architectures by employing an end-to-end quantum convolution-pooling pipeline without inserting an intermediate classical CNN block. No-QCNN is a hybrid quantum-classical model that employs a variational quantum classifier (VQC) optimised via the COBYLA algorithm and is designed for binary and multiclass classification of low-resolution colour images on near-term quantum devices. A key innovation is a problem-specific quantum feature map that pre-processes each image into a structured three-dimensional block-matrix representation, jointly encoding pixel colour (R, G, B) and spatial position before mapping this information into a hierarchical ZZFeatureMap. This encoding captures spatial-chromatic correlations at shallow circuit depth, making it compatible with noisy intermediate-scale quantum (NISQ) constraints. The model is implemented using IBM-Qiskit on a local quantum simulator and benchmarked against a classical CNN. For a six-class classification task on a 50-image dataset, No-QCNN achieves a validation accuracy of 82.05%, substantially outperforming the classical CNN's 40.00%, indicating improved generalisation and reduced overfitting in low-data, multiclass settings. Conversely, for a simpler binary classification task, the classical CNN achieves perfect validation accuracy of 100%, surpassing the No-QCNN's 89.7%. We further observe that No-QCNN performance declines with increasing dataset size and training time due to limited circuit expressibility, data-encoding overhead, and finite-sampling noise inherent to NISQ models. Overall, these results position No-QCNN as a complementary framework best suited to low-data, correlation-rich classification tasks, defining a realistic niche for quantum-enhanced artificial vision and quantum perception in the NISQ era.
Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis. This approach is particularly attractive due to the absence of the barren plateau problem, a fundamental challenge in training quantum neural networks (QNNs), and its feasibility. However, a limitation arises when applying QCNNs to classical data. The network architecture is most natural when the number of input qubits is a power of two, as this number is reduced by a factor of two in each pooling layer. The number of input qubits determines the dimensions (i.e., the number of features) of the input data that can be processed, restricting the applicability of QCNN algorithms to real-world data. To address this issue, we propose a QCNN architecture capable of handling arbitrary input data dimensions while optimizing the allocation of quantum resources such as ancillary qubits and quantum gates. This optimization is not only important for minimizing computational resources, but also essential in noisy intermediate-scale quantum (NISQ) computing, as the size of the quantum circuits that can be executed reliably is limited. Through numerical simulations, we benchmarked the classification performance of various QCNN architectures across multiple datasets with arbitrary input data dimensions, including MNIST, Landsat satellite, Fashion-MNIST, and Ionosphere. The results validate that the proposed QCNN architecture achieves excellent classification performance while utilizing a minimal resource overhead, providing an optimal solution when reliable quantum computation is constrained by noise and imperfections.
This study presents an advanced Quantum Convolutional Neural Network (QCNN) architecture tailored for improved classification accuracy and enhanced training stability within the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices. The proposed model incorporates a multi-dimensional quantum feature encoding stage, followed by entanglement-enriched convolutional layers and a refined quantum pooling mechanism. These design innovations substantially increase the circuit’s expressive capacity while reducing susceptibility to barren plateau phenomena, thereby supporting more efficient optimization. Extensive numerical simulations indicate that the model achieves a classification accuracy of 95.2%, alongside a final loss of 0.24, low output variance of 0.031, and improved expressibility characterized by a mean fidelity of 0.07. Collectively, these results demonstrate that the enhanced QCNN offers a robust and scalable framework for quantum machine learning tasks suitable for current and near-term quantum hardware. Also, modern applications from qunatum devices with the abovementioned result is met and leads to practical systems.
Quantum convolutional neural networks (QCNNs) typically consist of quantum encoding, convolution, and pooling layers. However, existing QCNNs for image classification still face two major limitations. Most encoding layers rely on single-parameter angle encoding, which limits their ability to represent complex image features. Many circuit architectures are relatively deep, increasing cir cuit complexity and noise accumulation and thus hindering implementation on noisy intermediate-scale quantum devices. To address these issues, this paper proposes a low-depth three-parameter encoding quantum convolutional neural network (LTP-QCNN) for image classification. The model employs a three parameter angle encoding layer that maps compressed image features onto the rotation angles of single-qubit gates. A first low-depth quantum convolutional layer is employed to extract local features, followed by a quantum pooling layer for quantum-state dimensionality reduction and information fusion. The pooled quantum states are then processed by a second quantum convolutional layer to extract discriminative features. Finally, the measured quantum feature vector is input into a classical classification layer to produce the final prediction. Simulation experiments were conducted on the Fashion-MNIST, MNIST, and KMNIST benchmark datasets. On Fashion-MNIST, LTP-QCNN achieved an accuracy of 99.85% on binary classification, while the accuracies for five-class and six class tasks reached 90.98% and 89.48%, respectively. The results demonstrate that LTP-QCNN achieves effective and stable image classification under limited quantum resources.
Abstract Equivariant quantum neural networks (QNNs) are promising variational models that exploit symmetries to improve machine learning capabilities. Despite theoretical developments in equivariant QNNs, their implementation on near-term quantum devices remains challenging due to limited computational resources. This study proposes a resource-efficient model of equivariant quantum convolutional networks (QCNNs) called equivariant split-parallelizing QCNN (sp-QCNN). Using a group-theoretical approach, we encode general symmetries into our model beyond the translational symmetry addressed by previous sp-QCNNs. We achieve this by splitting the circuit at the pooling layer while preserving symmetry. This splitting structure effectively parallelizes QCNNs to improve measurement efficiency in estimating the expectation value of an observable and its gradient by order of the number of qubits. Our model also exhibits high trainability and generalization performance, including the absence of barren plateaus. Numerical experiments demonstrate that the equivariant sp-QCNN can be trained and generalized with fewer measurement resources than a conventional equivariant QCNN in a noisy quantum data classification task. Our results contribute to the advancement of practical quantum machine learning algorithms.