Fidelity-only lower bounds for arbitrary mixed states
OpenQAlgorithms.Papers.Independent.Intakethird100.P21.fidelity_lower_boundLet and let be an arbitrary density operator on . Set , where . For pure cost or , define to be the minimum of over finite normalized pure ensembles satisfying only . These minima are attained, and
The ensembles in this relaxation need not represent . Equivalently they may be parametrized by Schmidt probabilities and local unitaries; their component fidelities can range over all of . If , there are also the bounds
where is the minimum Schmidt tangle subject to and is the lower convex envelope. This reduces a state-dependent lower bound to its squared overlap.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P21
noncomputable section
namespace QAlgorithms.Papers.Independent.Intakethird100.P21
open scoped Matrix ComplexConjugate
open Filter
open QAlgorithms
open QAlgorithms.Papers.Independent.Information100.P03
(PureEnsemble convexRoof pureConcurrence concurrence SchmidtData schmidtVector)
attribute [local instance] Classical.propDecidable Classical.decEq
/-- PDF pp.6--7, (3.9)--(3.10): the arbitrary input is mixed.
The relaxed problem permits every component maximallyEntangledOverlap in [0,1]; only the
further raw-profile scalarConvexRoofAtFidelity bound requires the mean F>=1/d. -/
theorem fidelity_lower_bound (d : ℕ) (hd : 2 ≤ d)
(rho : Matrix (Fin d × Fin d) (Fin d × Fin d) ℂ) (hrho : IsDensityMatrix rho) :
let F := maximallyEntangledOverlap rho
IsLeast (relaxedValues d pureTangle F) (relaxedRoof d pureTangle F) ∧
IsLeast (relaxedValues d pureConcurrence F) (relaxedRoof d pureConcurrence F) ∧
relaxedRoof d pureTangle F ≤ tangle rho ∧
relaxedRoof d pureConcurrence F ≤ concurrence rho ∧
(1 / (d : ℝ) ≤ F →
scalarConvexRoofAtFidelity d (constrainedSchmidtTangle d) F ≤ tangle rho ∧ scalarConvexRoofAtFidelity d (rawC d) F ≤ concurrence rho) := by sorry
end QAlgorithms.Papers.Independent.Intakethird100.P21
end
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What the Lean code literally says, in plain math · gpt-6.1-sol
For every natural number and every positive semidefinite complex matrix of trace on , let , where , and set . For a unit vector , define by , and put and . Here has entries , and real square roots take value on negative arguments. For either cost or , let be the set of numbers arising from finite ensembles of unit vectors with , , and , and let ; these ensembles are constrained only by their mean overlap, with no requirement that their average matrix equal . Define and as the respective infima of and over such finite ensembles whose average matrix is exactly . Also define
and, for a real function , let be the infimum of over finite scalar mixtures with nonnegative weights summing to , points , and mean . The assertion is that belongs to and is no larger than any member of that set, and likewise belongs to and is least in ; moreover and , and, if , then and . All finite mixtures permit zero weights, while their weights summing to excludes an empty ensemble.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.