用于学习平移对称数据的量子卷积神经网络的分割与并行化
Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
AI总结：
本文提出分割并行化量子卷积神经网络（sp-QCNN），利用平移对称性分割电路，在不增加量子比特数的情况下提高测量效率并加速学习，在量子相位识别任务中达到与传统 QCNN 相当的准确率。
摘要：
量子卷积神经网络（QCNN）是一种很有前景的量子机器学习（QML）模型，有望在经典难以处理的问题中实现量子优势。然而，QCNN 在数据学习过程中需要大量测量，这限制了其在大规模问题中的实际应用。为缓解这一需求，我们提出了一种名为分割并行化 QCNN（sp-QCNN）的新架构，该架构利用量子数据的先验知识来设计高效模型。该架构受几何量子机器学习的启发，面向物理学和量子计算科学中常见的平移对称量子数据。通过基于平移对称性分割量子电路，sp-QCNN 能够在不增加量子比特数量的情况下大幅并行化传统 QCNN，并将测量效率提高一个量子比特数量级。为证明其有效性，我们将 sp-QCNN 应用于量子相位识别任务，结果表明它可以在显著减少所需测量资源的同时，达到与传统 QCNN 相当的分类准确率。由于其高测量效率，sp-QCNN 能够减轻估计损失函数梯度时的统计误差，从而加速学习过程。这些结果为将先验数据知识融入 QML 模型的高效设计开辟了新可能，从而带来实际的量子优势。
英文摘要：
The quantum convolutional neural network (QCNN) is a promising quantum machine learning (QML) model that is expected to achieve quantum advantages in classically intractable problems. However, the QCNN requires a large number of measurements for data learning, limiting its practical applications in large-scale problems. To alleviate this requirement, we propose a novel architecture called split-parallelizing QCNN (sp-QCNN), which exploits the prior knowledge of quantum data to design an efficient model. This architecture draws inspiration from geometric quantum machine learning and targets translationally symmetric quantum data commonly encountered in physics and quantum computing science. By splitting the quantum circuit based on translational symmetry, the sp-QCNN can substantially parallelize the conventional QCNN without increasing the number of qubits and improve the measurement efficiency by an order of the number of qubits. To demonstrate its effectiveness, we apply the sp-QCNN to a quantum phase recognition task and show that it can achieve comparable classification accuracy to the conventional QCNN while considerably reducing the measurement resources required. Due to its high measurement efficiency, the sp-QCNN can mitigate statistical errors in estimating the gradient of the loss function, thereby accelerating the learning process. These results open up new possibilities for incorporating the prior data knowledge into the efficient design of QML models, leading to practical quantum advantages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
