hamiltonianlearning, as an important quantum machine learning technique, provides a significant approach for determining an accurate quantum system. This paper establishes parameterized hamiltonianlearning (PHL) and...
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hamiltonianlearning, as an important quantum machine learning technique, provides a significant approach for determining an accurate quantum system. This paper establishes parameterized hamiltonianlearning (PHL) and explores its application and implementation on quantum computers. A parameterized quantum circuit for hamiltonianlearning 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.
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