The rapid emergence of quantum technology has raised new challenges in distinguishing various quantum circuits of similar functions. In this work, we propose parallel quantum embedding neural network (ParaQuanNet) for the efficient identification of quantum generative circuits via classifications of the corresponding output data. Specifically, we generated W-like states with eight generative quantum circuits realizing the generative quantum denoising diffusion probabilistic models (QDDPM). Our ParaQuanNet can classify these eight classes of generated quantum data with an accuracy of { $99.5\%$ }, even though all of them are trained to generate the same types of quantum data. With a novel design of parallel quantum embedding unit (PQEU) in our neural networks, our ParaQuanNet enables the quantum kernel circuit parallelly process all the receptive fields of quantum data, which empowers the quantum data processing efficiency. We also integrate the mutually unbiased measurements into our ParaQuanNet and further improve its performance. We apply our ParaQuanNet on the classification of classical data sets and demonstrate a good performance of quantum neural networks on these tasks. Our approach demonstrates good robustness to noisy data and the circuit-level noise with a Python realization in a classical GPU. Our results highlight ParaQuanNet as a scalable and effective framework for quantum circuits identification, contributing to the broader development of quantum machine intelligence.The rapid emergence of quantum technology has raised new challenges in distinguishing various quantum circuits of similar functions. In this work, we propose parallel quantum embedding neural network (ParaQuanNet) for the efficient identification of quantum generative circuits via classifications of the corresponding output data. Specifically, we generated W-like states with eight generative quantum circuits realizing the generative quantum denoising diffusion probabilistic models (QDDPM). Our ParaQuanNet can classify these eight classes of generated quantum data with an accuracy of {$99.5\%$}, even though all of them are trained to generate the same types of quantum data. With a novel design of parallel quantum embedding unit (PQEU) in our neural networks, our ParaQuanNet enables the quantum kernel circuit parallelly process all the receptive fields of quantum data, which empowers the quantum data processing efficiency. We also integrate the mutually unbiased measurements into our ParaQuanNet and further improve its performance. We apply our ParaQuanNet on the classification of classical data sets and demonstrate a good performance of quantum neural networks on these tasks. Our approach demonstrates good robustness to noisy data and the circuit-level noise with a Python realization in a classical GPU. Our results highlight ParaQuanNet as a scalable and effective framework for quantum circuits identification, contributing to the broader development of quantum machine intelligence.

quant-pharXiv:2603.02834Frontiers of PhysicsMar 4, 20260 citations

Design of a quantum convolutional neural network on quantum circuits

Jin Yan Zheng, Qing Zhong Gao, Jinhu Lü +3 more

Journal of the Franklin InstituteAug 3, 202229 citations

Impact of Hybrid Neural Network Structure on Performance of Multiclass Classification

Yevhenii Trochun, Evgen Pavlov, Sergii Stirenko +1 more

This article describes hybrid convolutional neural network that uses one quantum circuit for image classification. The different configurations of the hybrid neural network with the quantum circuit are considered. Several different quantum circuits with different number of qubits are compared. These hybrid neural network configurations are evaluated on MNIST and MNIST Fashion datasets, which is radically different from MNIST dataset. Performance of hybrid neural network is compared for multiclass classification on MNIST and MNIST Fashion datasets for 4, 6, 8, 10 classes using quantum circuits with 2, 3, 4 qubits. The results of the experiments indicate the feasibility of using hybrid neural networks for multiclass classification.This article describes hybrid convolutional neural network that uses one quantum circuit for image classification. The different configurations of the hybrid neural network with the quantum circuit are considered. Several different quantum circuits with different number of qubits are compared. These hybrid neural network configurations are evaluated on MNIST and MNIST Fashion datasets, which is radically different from MNIST dataset. Performance of hybrid neural network is compared for multiclass classification on MNIST and MNIST Fashion datasets for 4, 6, 8, 10 classes using quantum circuits with 2, 3, 4 qubits. The results of the experiments indicate the feasibility of using hybrid neural networks for multiclass classification.

IEEE EUROCON - International Conference on Computer as a Tool (EUROCON)Jul 6, 20218 citations

Quantum Neural Networks: From Concept to Simulation

Nasrullah Masud, Abdus Sobhan, Md Tanvir Islam Sourav +1 more

Information systems engineering and managementJan 1, 20251 citation

Hybrid Quantum-Classical NN for Chest X-Ray Classification Using 2-Qubit Quantum Circuits

Firdaus, Infall Syafalni, Nur Ahmadi +3 more

This study investigates the integration of a 2-qubit quantum system into a classical neural network for chest X-ray classification. Three hybrid quantum-classical neural network circuits are evaluated: U-gate with CNOT, IBM QCNN, and RealAmplitudes. A classical convolutional neural network (CNN), specifically ResNet-18, serves as a benchmark. The results show accuracies of 0.9038 for ResNet-18, 0.7981 for U-gate with CNOT, 0.6250 for IBM QCNN, and 0.6250 for RealAmplitudes. The classical ResNet-18 model outperforms all hybrid models, but the U-gate with CNOT circuit demonstrates promising performance, suggesting potential for quantum-enhanced machine learning in medical image classification.This study investigates the integration of a 2-qubit quantum system into a classical neural network for chest X-ray classification. Three hybrid quantum-classical neural network circuits are evaluated: U-gate with CNOT, IBM QCNN, and RealAmplitudes. A classical convolutional neural network (CNN), specifically ResNet-18, serves as a benchmark. The results show accuracies of 0.9038 for ResNet-18, 0.7981 for U-gate with CNOT, 0.6250 for IBM QCNN, and 0.6250 for RealAmplitudes. The classical ResNet-18 model outperforms all hybrid models, but the U-gate with CNOT circuit demonstrates promising performance, suggesting potential for quantum-enhanced machine learning in medical image classification.

International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)Nov 4, 20250 citations

99.5\%

}, even though all of them are trained to generate the same types of quantum data. With a novel design of parallel quantum embedding unit (PQEU) in our neural networks, our ParaQuanNet enables the quantum kernel circuit parallelly process all the receptive fields of quantum data, which empowers the quantum data processing efficiency. We also integrate the mutually unbiased measurements into our ParaQuanNet and further improve its performance. We apply our ParaQuanNet on the classification of classical data sets and demonstrate a good performance of quantum neural networks on these tasks. Our approach demonstrates good robustness to noisy data and the circuit-level noise with a Python realization in a classical GPU. Our results highlight ParaQuanNet as a scalable and effective framework for quantum circuits identification, contributing to the broader development of quantum machine intelligence.