Finite-error metrological bounds on the multi-parameter Hamiltonian estimation

Abstract

Estimation of multiple parameters in an unknown Hamiltonian is investigated. We present upper and lower bounds on the time required to complete the estimation within a prescribed tolerance $\delta$. The lower bound is given on the basis of the Cramér-Rao inequality, where the quantum Fisher information is bounded by the squared evolution time. The upper bound is obtained by an explicit construction of estimation procedures. By comparing the cases with different numbers of Hamiltonian channels, we also find that the few-channel procedure with adaptive feedback and the many-channel procedure with entanglement are equivalent in that they require the same amount of time resource up to a constant factor.

21 pages, 4 figures

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• The advantage of quantum control in many-body Hamiltonian learning
• Active Learning of Quantum System Hamiltonians yields Query Advantage
• Quantum metrology in the finite-sample regime
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• Robust quantum metrology with explicit symmetric states
• Structure learning of Hamiltonians from real-time evolution
• Attainability of the Holevo-Cramér-Rao bound for two-qubit 3D magnetometry
• Learning the structure of any Hamiltonian from minimal assumptions
• Trans-dimensional Hamiltonian model selection and parameter estimation from sparse, noisy data