Alphabeta Math
TheoremStatement: Literature-sourcedProof: AI-generatedPipeline-generated
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

The finite Holevo measurement bound

Statement

Let X be a finite nonempty set, let (px) be a probability vector, let ρx be density operators on a nonzero finite-dimensional space HB, and let ρˉ:=∑xpxρx. For a finite POVM (Ey)y∈Y on HB, define w(y∣x):=tr⁡(ρxEy), qy:=∑xpxw(y∣x), and the classical mutual information

I(X:Y):=∑x,y:pxw(y∣x)>0pxw(y∣x)ln⁡w(y∣x)qy.

Then

I(X:Y)≤χ({px,ρx})=S(ρˉ)−∑x:px>0pxS(ρx).

Facts & Assumptions

Given: The finite ensemble, average state, POVM, and outcome probabilities in the Statement.

[F1]

Finite classical-quantum states and Holevo information: χ=S(ρˉ)−∑px>0pxS(ρx) for the associated cq state.

[F2]

Finite-dimensional quantum relative entropy: QRE is computed by the support-restricted spectral logarithm, with +∞ when supports fail.

[F3]

Channel data processing for finite quantum relative entropy: QRE cannot increase under a finite-dimensional quantum channel.

[F4]

A finite POVM defines a classical-output quantum channel: the POVM defines a channel whose output on a state has diagonal entries tr⁡(ρEy).

[F5]

POVM outcome weights are probabilities: each w(⋅∣x) is a probability vector.

[F7]

Trace is a linear functional on Mn(F): trace is linear over finite sums and scalar multiples.

Proof

1.1givenF2F6algebra

Fix x with px>0. The operator ρˉ−pxρx=∑z≠xpzρz is non-negative. If v∈ker⁡ρˉ, then 0=⟨ρˉv,v⟩≥px⟨ρxv,v⟩≥0, so [F6] gives ρxv=0. Thus ker⁡ρˉ⊆ker⁡ρx and supp⁡(ρx)⊆supp⁡(ρˉ); D(ρx∥ρˉ) is finite for every x with px>0.

2.1givenF2F3F4F5F7step 1.1algebra

The measurement channel [F4] sends ρx to the diagonal state with entries w(y∣x) and ρˉ to the diagonal state with entries qy by linearity [F7]. For px>0, if w(y∣x)>0 then qy≥pxw(y∣x)>0. The QRE formula [F2] on diagonal states and data processing [F3] therefore give D(ρx∥ρˉ)≥∑y:w(y∣x)>0w(y∣x)ln⁡w(y∣x)qy.

3.1givenF1F2F7step 1.1step 2.1algebra∎

Multiply step 2.1 by px and sum only over x with px>0. Extend the support logarithm of ρˉ by zero on its kernel; step 1.1 and [F2] ensure that this preserves every retained cross trace. Trace linearity [F7] and ρˉ=∑x:px>0pxρx give ∑x:px>0pxD(ρx∥ρˉ)=∑x:px>0pxtr⁡(ρxln⁡ρx)−tr⁡(ρˉln⁡ρˉ)=χ by [F1]. The summed measurement terms are exactly the displayed I(X:Y) by its definition. Hence I(X:Y)≤χ.

Depends on

Used by

Dependency tree · two levels

44 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources