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<p>To address the limitations of the existing quantum convolution algorithm in feature extraction and scalability, this paper proposes a Quantum Independent Convolution-like Kernel Layer (QICKL), which mirrors classical convolution by encoding a kernel’s weighted sum into a qubit rotation angle. We evaluate the effectiveness of QICKL using a hybrid network constructed with QICKL and a classical fully connected layer. Result on the MNIST/FashionMNIST dataset shows that our network outperforms existing quantum-classical convolution networks while using same or fewer qubits, demonstrating its efficiency and scalability. Comparing other embedding methods, QICKL demonstrates advantage in convergence speed and accuracy. This work provides a new design perspective for resource-efficient quantum algorithms.</p>

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A qubit as a Kernel: an efficient quantum-classical network for image classification with Quantum Independent Convolution-like Kernel Layer

  • 期刊论文

  • 发表日期：

    2026年3月8日

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  • Wencong Cai,
  • Yuanfeng Wang,
  • Yongzhen Xu,
  • Sidan Du,
  • Qi Qin,
  • Yang Li

摘要

To address the limitations of the existing quantum convolution algorithm in feature extraction and scalability, this paper proposes a Quantum Independent Convolution-like Kernel Layer (QICKL), which mirrors classical convolution by encoding a kernel’s weighted sum into a qubit rotation angle. We evaluate the effectiveness of QICKL using a hybrid network constructed with QICKL and a classical fully connected layer. Result on the MNIST/FashionMNIST dataset shows that our network outperforms existing quantum-classical convolution networks while using same or fewer qubits, demonstrating its efficiency and scalability. Comparing other embedding methods, QICKL demonstrates advantage in convergence speed and accuracy. This work provides a new design perspective for resource-efficient quantum algorithms.