Linear dependence among the diagonal components of Hamiltonian derivatives creates a slow parameter direction with O(t^0) scaling, obstructing simultaneous t^{-2} multiparameter quantum estimation.
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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.
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.
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.
citing papers explorer
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Geometric obstructions to quadratic time scaling in multiparameter quantum estimation
Linear dependence among the diagonal components of Hamiltonian derivatives creates a slow parameter direction with O(t^0) scaling, obstructing simultaneous t^{-2} multiparameter quantum estimation.
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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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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.
- Protecting Heisenberg scaling in quantum metrology via engineered dressed states