Zhou Zeng-Rong, Li Hang, Long Gui-Lu
Research Center for Quantum Sensing, Zhejiang Lab, Hangzhou 311121, China.
Beijing Academy of Quantum Information Sciences, Beijing 100193, China.
Fundam Res. 2023 Oct 14;4(4):845-850. doi: 10.1016/j.fmre.2023.10.001. eCollection 2024 Jul.
Quantum machine learning has made remarkable progress in many important tasks. However, the gate complexity of the initial state preparation is seldom considered in lots of quantum machine learning algorithms, making them non-end-to-end. Herein, we propose a quantum algorithm for the node embedding problem that maps a node graph's topological structure to embedding vectors. The resulting quantum embedding state can be used as an input for other quantum machine learning algorithms. With qubits to store the information of nodes, our algorithm will not lose quantum advantage for the subsequent quantum information processing. Moreover, owing to the use of a parameterized quantum circuit with depth, the resulting state can serve as an efficient quantum database. In addition, we explored the measurement complexity of the quantum node embedding algorithm, which is the main issue in training parameters, and extended the algorithm to capture high-order neighborhood information between nodes. Finally, we experimentally demonstrated our algorithm on an nuclear magnetic resonance quantum processor to solve a graph model.
量子机器学习在许多重要任务中取得了显著进展。然而，许多量子机器学习算法很少考虑初始态制备的门复杂度，这使得它们不是端到端的。在此，我们提出一种用于节点嵌入问题的量子算法，该算法将节点图的拓扑结构映射到嵌入向量。所得的量子嵌入态可作为其他量子机器学习算法的输入。利用 个量子比特来存储节点信息，我们的算法在后续量子信息处理中不会失去量子优势。此外，由于使用了具有 深度的参数化量子电路，所得状态可作为一个高效的量子数据库。另外，我们探讨了量子节点嵌入算法的测量复杂度，这是训练参数中的主要问题，并扩展了该算法以捕获节点之间的高阶邻域信息。最后，我们在核磁共振量子处理器上通过实验证明了我们的算法可用于求解一个图模型。
