Fundamental Limits of Quantum Metrology Beyond Fixed Causal Order

• URL: http://arxiv.org/abs/2609.05355v1
• Date: Fri, 04 Sep 2026 17:01:27 GMT
• Title: Fundamental Limits of Quantum Metrology Beyond Fixed Causal Order
• Abstract summary: We bridge the gap for the estimation of a single parameter encoded in $N$ identical uses of a finite-dimensional quantum channel.<n>Our results clarify the ultimate role of indefinite causality as a metrological resource for quantum channel estimation.
• Score: 10.155534042258097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum metrology with indefinite causal order (ICO) has attracted intense interest due to its potential to surpass the limitations of conventional fixed-order strategies. A key open question is whether ICO can fundamentally enhance asymptotic precision scaling. In this work, we bridge this gap for the estimation of a single parameter encoded in $N$ identical uses of a finite-dimensional quantum channel. We first establish a universal Heisenberg-scaling upper bound for the full general ICO process-matrix class and show that for unitary channels its optimal quantum Fisher information (QFI) coincides exactly with that of parallel strategies. For noisy channels, a structurally refined bound shows that channels restricted to the standard quantum limit (SQL) under parallel strategies remain SQL-limited under general ICO strategies. Most significantly, an asymptotically tight (AT) bound is derived to close the remaining possibility of an asymptotic ICO advantage by showing that general ICO and optimal parallel strategies have exactly the same leading QFI coefficient in both the SQL and Heisenberg regimes. For the operationally motivated class of quantum circuits with quantum control of causal order, we further obtain an iterative constraint on finite-query precision whose asymptotic limit agrees with that of the AT bound. Our results clarify the ultimate role of indefinite causality as a metrological resource for quantum channel estimation.

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