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Fadel, M., Yadin, B., Mao, Y. P., Byrnes, T., & Gessner, M. (2023). Multiparameter quantum metrology and mode entanglement with spatially split nonclassical spin ensembles. New J. Phys., 25(7), 073006–25pp.
Abstract: We identify the multiparameter sensitivity of entangled spin states, such as spin-squeezed and Dicke states that are spatially distributed into several addressable spatial modes. Analytical expressions for the spin-squeezing matrix of families of states that are accessible by current atomic experiments reveal the quantum gain in multiparameter metrology, as well as the optimal strategies to maximize the sensitivity gain for the estimation of any linear combination of parameters. We further study the mode entanglement of these states by deriving a witness for genuine k-partite mode entanglement from the spin-squeezing matrix. Our results highlight the advantage of mode entanglement for distributed sensing, and outline optimal protocols for multiparameter estimation with nonclassical spatially-distributed spin ensembles. We illustrate our findings with the design of a protocol for gradient sensing with a Bose-Einstein condensate in an entangled spin state in two modes.
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Gomez-Lurbe, R. (2026). Efficient protocol to estimate the Quantum Fisher Information Matrix for commuting-block circuits. Quantum Inf. Process., 25(9), 278–16pp.
Abstract: The Quantum Fisher Information Matrix (QFIM) quantifies how sensitive parameterized quantum states are to changes in their parameters. Recently, it has been used to improve variational quantum algorithm optimization through geometry-aware techniques. However, estimating the QFIM-particularly its off-block-diagonal elements-requires substantial resources. To address this, we introduce a novel protocol that efficiently computes these elements for commuting-block variational circuits. Our approach reduces the number of quantum state preparations from O(m2) to O(L2) , where m is the number of parameters and L the number of circuit layers. This also lowers classical measurement and post-processing requirements, improving computational efficiency.
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