Power-law kinetic grading in a 1D lattice drives a localization transition at alpha equals zero with diverging length, enabling critical enhancement of quantum Fisher information for parameter estimation.
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A fluctuation-guided adaptive random compiler for Hamiltonian simulation dynamically adjusts term sampling probabilities according to state sensitivity to improve fidelity over fixed randomized methods.
Dressed states generated by static fields protect Heisenberg scaling in quantum metrology for low-temperature noise precisely when the signal generator lies outside the linear span of the system-environment coupling operators.
Integrating quantum catalysis, entanglement, and squeezing in a distributed quantum network yields better multiphase sensing precision than any two alone, approaching the Heisenberg limit, with partial catalysis outperforming global catalysis in both ideal and lossy cases.
Long-range non-Hermitian XX spin chains show enhanced time and size scaling of dynamical quantum Fisher information for parameter estimation compared to short-range and Hermitian cases, with identical scaling at criticality for ground-state probes.
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Localization from Infinitesimal Kinetic Grading: Finite-size Scaling, Kibble-Zurek Dynamics and Applications in Sensing
Power-law kinetic grading in a 1D lattice drives a localization transition at alpha equals zero with diverging length, enabling critical enhancement of quantum Fisher information for parameter estimation.
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Fluctuation-guided adaptive random compiler for Hamiltonian simulation
A fluctuation-guided adaptive random compiler for Hamiltonian simulation dynamically adjusts term sampling probabilities according to state sensitivity to improve fidelity over fixed randomized methods.
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Protecting Heisenberg scaling in quantum metrology via engineered dressed states
Dressed states generated by static fields protect Heisenberg scaling in quantum metrology for low-temperature noise precisely when the signal generator lies outside the linear span of the system-environment coupling operators.
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Quantum-enhanced distributed network sensing using multiple quantum resources
Integrating quantum catalysis, entanglement, and squeezing in a distributed quantum network yields better multiphase sensing precision than any two alone, approaching the Heisenberg limit, with partial catalysis outperforming global catalysis in both ideal and lossy cases.
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Quantum-enhanced sensing from the interplay of long-range interactions and non-Hermiticity
Long-range non-Hermitian XX spin chains show enhanced time and size scaling of dynamical quantum Fisher information for parameter estimation compared to short-range and Hermitian cases, with identical scaling at criticality for ground-state probes.