Shi Jinjing, Wang Wenxuan, Lou Xiaoping, Zhang Shichao, Li Xuelong
IEEE Trans Pattern Anal Mach Intell. 2023 May;45(5):6086-6095. doi: 10.1109/TPAMI.2022.3203157. Epub 2023 Apr 3.
Hamiltonian learning, as an important quantum machine learning technique, provides a significant approach for determining an accurate quantum system. This paper establishes parameterized Hamiltonian learning (PHL) and explores its application and implementation on quantum computers. A parameterized quantum circuit for Hamiltonian learning is first created by decomposing unitary operators to excite the system evolution. Then, a PHL algorithm is developed to prepare a specific Hamiltonian system by iteratively updating the gradient of the loss function about circuit parameters. Finally, the experiments are conducted on Origin Pilot, and it demonstrates that the PHL algorithm can deal with the image segmentation problem and provide a segmentation solution accurately. Compared with the classical Grabcut algorithm, the PHL algorithm eliminates the requirement of early manual intervention. It provides a new possibility for solving practical application problems with quantum devices, which also assists in solving increasingly complicated problems and supports a much wider range of application possibilities in the future.
哈密顿学习作为一种重要的量子机器学习技术，为确定精确的量子系统提供了一种重要方法。本文建立了参数化哈密顿学习（PHL）并探索其在量子计算机上的应用与实现。首先通过分解酉算子来激发系统演化，创建用于哈密顿学习的参数化量子电路。然后，开发了一种PHL算法，通过迭代更新损失函数关于电路参数的梯度来制备特定的哈密顿系统。最后，在Origin Pilot上进行了实验，结果表明PHL算法能够处理图像分割问题并准确提供分割解决方案。与经典的Grabcut算法相比，PHL算法消除了早期人工干预的需求。它为用量子设备解决实际应用问题提供了新的可能性，这也有助于解决日益复杂的问题，并在未来支持更广泛的应用可能性。
