An approximate quantum Hamiltonian identification algorithm using a Taylor expansion of the matrix exponential function
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Wang, Yuanlong
Dong, Daoyi
Petersen, Ian
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IEEE
Abstract
An approximate quantum Hamiltonian identification algorithm is presented with the assumption that the system initial state and observation matrix can be set appropriately. We sample the system with a fixed period and using the sampled data we estimate the Hamiltonian based on a Taylor expansion of the matrix exponential function. We prove the estimation error is linear in the variance of the additive Gaussian noise. We also propose a heuristic formula to find the order of magnitude of the optimal sampling period. Two numerical examples are presented to validate the theoretical results.
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2017 IEEE 56th Annual Conference on Decision and Control, (CDC) 2017
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