Optimally learning functions in interacting quantum sensor networks

• URL: http://arxiv.org/abs/2510.06360v1
• Date: Tue, 07 Oct 2025 18:25:27 GMT
• Title: Optimally learning functions in interacting quantum sensor networks
• Authors: Erfan Abbasgholinejad, Sean R. Muleady, Jacob Bringewatt, Anthony J. Brady, Yu-Xin Wang, Ali Fahimniya, Alexey V. Gorshkov,
• Abstract summary: We establish optimal bounds for estimating a linear combination of local parameters of Hamiltonians with arbitrary, unknown interactions.<n>Our results unify and extend existing bounds for non-interacting qubits and multimode interferometers.
• Score: 3.0446222918423627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating extensive combinations of local parameters in distributed quantum systems is a central problem in quantum sensing, with applications ranging from magnetometry to timekeeping. While optimal strategies are known for sensing non-interacting Hamiltonians in quantum sensor networks, fundamental limits in the presence of uncontrolled interactions remain unclear. Here, we establish optimal bounds and protocols for estimating a linear combination of local parameters of Hamiltonians with arbitrary, unknown interactions. In the process, we more generally establish bounds for learning any linear combination of Hamiltonian coefficients for arbitrary, commuting terms. Our results unify and extend existing bounds for non-interacting qubits and multimode interferometers, providing a general framework for distributed sensing and Hamiltonian learning in realistic many-body systems.

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