Surpassing Gaussian optimality in multiparameter estimation with indefinite causal order

• URL: http://arxiv.org/abs/2608.25870v1
• Date: Wed, 26 Aug 2026 14:42:54 GMT
• Title: Surpassing Gaussian optimality in multiparameter estimation with indefinite causal order
• Abstract summary: We identify single-mode Gaussian probes, generated by displacement and squeezing operations on the vacuum state.<n>We show that the best precision at a fixed energy is achieved not by an experimentally costly squeezing resource, but rather by redirecting some of the energy towards displacement.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We identify single-mode Gaussian probes, generated by displacement and squeezing operations on the vacuum state, which are optimal for the simultaneous estimation of displacement and squeezing operations in continuous-variable quantum systems. Importantly, our results reveal that the best precision at a fixed energy is achieved not by an experimentally costly squeezing resource, but rather by redirecting some of the energy towards displacement, thus allowing for more resource-effective operations. Furthermore, introducing indefinite causal order (ICO) in either the probe preparation or parameter encoding step can surpass the Gaussian precision bound, even though the optimal Gaussian probe state is agnostic to the ordering of the operations. Specifically, we observe that odd-parity superpositions of the two definite orders can enhance precision over optimal Gaussian probes in specific parameter regimes. Further, the observed advantage cannot be attributed solely to non-Gaussianity, as quantified by the relative entropy of non-Gaussianity, highlighting ICO as an independent resource for enhancing multiparameter estimation.

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