Heisenberg-Limited Continuous-Variable Distributed Quantum Metrology with Arbitrary Weights
Distributed quantum metrology (DQM) enables the estimation of global functions of d distributed parameters beyond the capability of separable sensors. Continuous-variable DQM involves using a linear network with at least one nonclassical input. Here, we fully elucidate the structure of linear networks with two nonvacuum inputs, which allows us to prove a number of fundamental properties of continuous-variable DQM. While measuring the sum of d parameters at the Heisenberg limit can be achieved with a single nonvacuum input, we show that two inputs, one of which can be classical, are required to measure an arbitrary linear combination of d parameters and an arbitrary global function of the parameters. We obtain a universal and tight upper bound on the sensitivity of DQM networks with two inputs, and completely characterize the properties of the nonclassical input required to obtain a quantum advantage. This reveals that a wide range of nonclassical states makes this possible, including a squeezed vacuum. We also show that, for a class of nonclassical inputs, local photon number detection will achieve the maximum sensitivity. Finally we show that a general DQM network has two distinct regimes. The first achieves Heisenberg scaling. In the second the nonclassical input is much weaker than the coherent input, nevertheless providing a multiplicative enhancement to the otherwise classical sensitivity.
Authors
- Kurt Jacobs (ORCID: https://orcid.org/0000-0003-0828-6421)
- Wenchao Ge (ORCID: https://orcid.org/0000-0003-1936-7710)
Institutions
- Southern Illinois University Carbondale (US)
- DEVCOM Army Research Laboratory (US)
- University of Rhode Island (US)
- University of Massachusetts Boston (US)
Publication Details
- Journal
- Physical Review Letters
- Published
- 2025-09-02
- DOI
- https://doi.org/10.1103/jkjj-3gvb
- Primary Topic
- Quantum Information and Cryptography
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation