Bayesian monotone metrics extend Petz metrics to prior-averaged states, yielding computable lower bounds on multiparameter Bayes risk that dominate van Trees bounds and can be optimized via a one-parameter subfamily.
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Quantum state evolution in variational algorithms is governed by geometric phase rather than dynamical phase, with entanglement decoupled from evolution in hardware-efficient ansatzes but acting as a dynamical resource in Hamiltonian variational ansatzes.
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Bayesian Monotone Metrics for Multiparameter Quantum Estimation
Bayesian monotone metrics extend Petz metrics to prior-averaged states, yielding computable lower bounds on multiparameter Bayes risk that dominate van Trees bounds and can be optimized via a one-parameter subfamily.
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Calibrating the Role of Entanglement in Variational Quantum Algorithms from a Geometric Perspective
Quantum state evolution in variational algorithms is governed by geometric phase rather than dynamical phase, with entanglement decoupled from evolution in hardware-efficient ansatzes but acting as a dynamical resource in Hamiltonian variational ansatzes.