دسته بندی:

محاسبات کوانتومی - Quantum-Computing

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سال انتشار:

2022

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عنوان انگلیسی مقاله:

Parameterized Hamiltonian Learning With Quantum Circuit

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ترجمه فارسی عنوان مقاله:

یادگیری همیلتونی پارامتری شده با مدار کوانتومی

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منبع:

ieee - ieee Transactions on Pattern Analysis and Machine Intelligence;2022;PP;99;10:1109/TPAMI:2022:3203157

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نویسنده:

Jinjing Shi, Member, IEEE, Wenxuan Wang, Student Member, IEEE, Xiaoping Lou, Member, IEEE, Shichao Zhang, Senior Member, IEEE, and Xuelong Li, Fellow, IEEE

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چکیده انگلیسی:

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.
Index Terms: Quantum machine learning | Parameterized Hamiltonian learning (PHL) | parameterized quantum circuit | Hamiltonian learning algorithm | Image segmentation

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قیمت: رایگان

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توضیحات اضافی:

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