The paper introduces a novel quantum machine learning model called Quantum Convolutional Neural Networks (QCNNs), which is inspired by convolutional neural networks (CNNs) but adapted for quantum systems. QCNNs use only $O(\log(N))$ variational parameters for input sizes of $N$ qubits, making them efficient for training and implementation on near-term quantum devices. The architecture combines multi-scale entanglement renormalization ansatz (MERA) and quantum error correction (QEC). The authors demonstrate the potential of QCNNs in two applications: recognizing quantum states associated with 1D symmetry-protected topological phases and optimizing quantum error correction schemes for given error models. They show that QCNNs can accurately reproduce phase diagrams and outperform known quantum codes in terms of error correction performance. The paper also discusses the experimental realization of QCNNs and potential future directions, including generalizations to higher dimensions and fault-tolerant operations.The paper introduces a novel quantum machine learning model called Quantum Convolutional Neural Networks (QCNNs), which is inspired by convolutional neural networks (CNNs) but adapted for quantum systems. QCNNs use only $O(\log(N))$ variational parameters for input sizes of $N$ qubits, making them efficient for training and implementation on near-term quantum devices. The architecture combines multi-scale entanglement renormalization ansatz (MERA) and quantum error correction (QEC). The authors demonstrate the potential of QCNNs in two applications: recognizing quantum states associated with 1D symmetry-protected topological phases and optimizing quantum error correction schemes for given error models. They show that QCNNs can accurately reproduce phase diagrams and outperform known quantum codes in terms of error correction performance. The paper also discusses the experimental realization of QCNNs and potential future directions, including generalizations to higher dimensions and fault-tolerant operations.