Covariance matrices for finite-dimensional DFT-related position-momentum pairs are fully characterized via unitary invariants, convex geometry, and SDP, yielding extremal states and application bounds.
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Detector distinguishability creates quantum geometry while a consistency functional built from deformation costs and symmetry principles yields the Einstein equations with matter from local detector changes.
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.
Leggett-Garg inequality violations yield lower bounds on quantum Fisher information in stationary pure and thermal states, serving as a witness for many-body quantum coherence.
A new framework establishes a trade-off between energy cost and complexity in quantum phase estimation, locating a sweet spot for co-optimization at desired precision.
citing papers explorer
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The uncertainty geometry of finite-dimensional position and momentum
Covariance matrices for finite-dimensional DFT-related position-momentum pairs are fully characterized via unitary invariants, convex geometry, and SDP, yielding extremal states and application bounds.
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A Detector-Based Inference Framework for Quantum Theory and Spacetime Geometry
Detector distinguishability creates quantum geometry while a consistency functional built from deformation costs and symmetry principles yields the Einstein equations with matter from local detector changes.
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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.
-
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.
-
Leggett-Garg Inequality Violations Bound Quantum Fisher Information
Leggett-Garg inequality violations yield lower bounds on quantum Fisher information in stationary pure and thermal states, serving as a witness for many-body quantum coherence.
-
Trade-off between complexity and energy in quantum phase estimation
A new framework establishes a trade-off between energy cost and complexity in quantum phase estimation, locating a sweet spot for co-optimization at desired precision.