دسته بندی:

محاسبات کوانتومی - Quantum-Computing

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سال انتشار:

2022

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عنوان انگلیسی مقاله:

Model-Predictive Quantum Control via Hamiltonian Learning

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ترجمه فارسی عنوان مقاله:

مدل-کنترل کوانتومی پیش‌بینی‌کننده از طریق یادگیری همیلتونی

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منبع:

ieee - ieee Transactions on Quantum Engineering; ;PP;99;10:1109/TQE:2022:3176870

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نویسنده:

MAISON CLOUÂTRÉ1,2, (Student Member, IEEE), MOHAMMAD JAVAD KHOJASTEH1, (Member, IEEE) and MOE Z. WIN1,3, (Fellow, IEEE)

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چکیده انگلیسی:

This work proposes an end-to-end framework for the learning-enabled control of closed quantum systems. The proposed learning technique is the first of its kind to utilize a hierarchical design which layers probing control, quantum state tomography, quantum process tomography, and Hamiltonian learning to identify both the internal and control Hamiltonians. Within this context, a novel quantum process tomography algorithm is presented which involves optimization on the unitary group, i.e., the space of unitary operators, to ensure physically meaningful predictions. Our scalable Hamiltonian learning algorithms have low memory requirements and tunable computational complexity. Once the Hamiltonians are learned, we formalize data-driven model-predictive quantum control (MPQC). This technique utilizes the learned model to compute quantum control parameters in a closed-loop simulation. Then, the optimized control input is given to a physical quantum system in an open-loop fashion. Simulations show modelpredictive quantum control to be more efficient than the current state-of-the-art, quantum optimal control, when sequential quadratic programming (SQP) is used to solve each control problem.
INDEX TERMS: Quantum Hamiltonian learning | quantum process tomography | quantum control | quantum consensus | quantum networks | quantum computing

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قیمت: رایگان

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توضیحات اضافی:

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