配备反向传播的量子神经网络在量子数字处理器上的实现
Quantum neural network equipped with backpropagation on a qudit processor
- University of Science and Technology of China（中国科学技术大学）
- Hefei National Laboratory, University of Science and Technology of China（合肥国家实验室，中国科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文实验演示了基于囚禁40Ca+离子qudit处理器的量子神经网络，采用混合量子-经典反向传播训练，在测试图像集上达到95.7%分类准确率，展示了qudit在构建高表达力QNN方面的潜力。
AI中文摘要：
量子神经网络（QNNs）作为量子机器学习中的基础算法之一，已被广泛应用于分类和识别任务。然而，QNN的能力受限于其规模，而规模由底层量子处理器的希尔伯特空间维度决定。与两能级量子比特相比，多能级量子数字（qudits）能够访问更高维的希尔伯特空间，从而构建更具表达力的QNN。在本工作中，我们报告了使用囚禁$\ m ^{40}Ca^+$离子实现基于qudit的QNN的实验演示。我们采用反向传播的混合量子-经典实现来训练QNN，并在测试图像集上实现了$95.7\%$的实验分类准确率。该演示突显了基于qudit的处理器在QNN架构中的潜力，并为在各种量子设备上实现基于qudit的QNN提供了框架。
英文摘要：
Quantum neural networks (QNNs), one of the fundamental algorithms in quantum machine learning, have been widely used in classification and identification tasks. However, the capabilities of QNNs are constrained by their size, which is determined by the dimension of the Hilbert space of the underlying quantum processor. Multi-level quantum digits (qudits) offer access to a higher-dimensional Hilbert space compared to two-level qubits, enabling the construction of more expressive QNNs. In this work, we report an experimental demonstration of qudit-based QNN using a trapped $\rm ^{40}Ca^+$ ion. We train the QNN using a hybrid quantum-classical implementation of backpropagation and achieve an experimental classification accuracy of $95.7\%$ on a test image set. This demonstration highlights the potential of qudit-based processors to QNN architectures and provides a framework for implementing qudit-based QNNs across various quantum devices.
