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Quantum Neural Network Classification Benchmark

Quantum Convolutional Neural Network (QCNN) · Machine-learning · 10 qubits · Qiskit Machine Learning, PennyLane, TensorFlow Quantum

Benchmark comparing quantum neural network architectures for image classification tasks on standard datasets including MNIST and Fashion-MNIST. Fully parameterized QCNNs achieve excellent classification accuracy despite a small number of free parameters. Comparative studies evaluate noise robustness across different QCNN model structures, data encoding methods, and optimizers under realistic quantum noise channels.

QNNQCNNclassificationMNISTmachine-learninghybrid
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4 credible sources · last verified 6 months ago

Benchmark comparing quantum neural network architectures for image classification tasks on standard datasets including MNIST and Fashion-MNIST. Fully parameterized QCNNs achieve excellent classification accuracy despite a small number of free parameters. Comparative studies evaluate noise robustness across different QCNN model structures, data encoding methods, and optimizers under realistic quantum noise channels.

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Key Metrics
Qubits used
10
Datasets
MNIST, Fashion-MNIST
Why It Matters

Demonstrates that fully parameterized QCNNs achieve strong classification with far fewer parameters than classical counterparts, and evaluates noise robustness.

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Hardware

Simulator / hardware-agnostic

Framework

Qiskit Machine Learning, PennyLane, TensorFlow Quantum