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Exponential averaged spectral projectors for a finite cq ensemble

Statement

For every fixed finite ensemble px,ρx and fixed η>0, there are constants C,c>0 (depending on this ensemble and η, not n) and projectors Pn,Qxn such that, writing ρˉ=∑pxρx and s=∑pxS(ρx), rank⁡Pn≤en(S(ρˉ)+η),Pnρˉ⊗nPn⪯e−n(S(ρˉ)−η)Pn, rank⁡Qxn≤en(s+η),QxnρxnQxn⪯e−n(s−η)Qxn, and Etr⁡(ρXn(I−Pn))≤Ce−cn,Etr⁡(ρXn(I−QXn))≤Ce−cn. The second pair of algebraic bounds holds for every word, including words with zero sampling probability; no per-word high-acceptance assertion is required.

Facts & Assumptions

Given: The finite-dimensional spaces, states, channels and explicit scalar hypotheses in the Statement.

[F1]

Finite-dimensional von Neumann entropy: Finite-dimensional von Neumann entropy.

[F2]

Complex spectral theorem: a normal endomorphism of a finite-dimensional complex inner product space has an orthonormal eigenbasis, and conversely: Complex spectral theorem: a normal endomorphism of a finite-dimensional complex inner product space has an orthonormal eigenbasis, and conversely.

[F3]

Finite classical-quantum states and Holevo information: Finite classical-quantum states and Holevo information.

[F4]

Taylor's Schlömilch–Roche remainder formula: Taylor's Schlömilch–Roche remainder formula.

[F5]

The exponential function is smooth and (exp⁡)′=exp⁡: The exponential function is smooth and (exp⁡)′=exp⁡.

[F6]

Markov's inequality for random variables: Markov's inequality for random variables.

[F7]

Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm: the natural logarithm product law converts product eigenvalues into sums of self-information.

Proof

1.1F1F2F3F4F5F6F7givenalgebraconstruct

For Pn, retain product eigenvectors of ρˉ⊗n whose positive eigenvalue has negative logarithm in n[S(ρˉ)−η,S(ρˉ)+η]. For Qxn, use product eigenvectors of ρxn=⨂iρxi whose eigenvalue has negative logarithm in n[s−η,s+η]. The upper eigenvalue bounds are immediate. The lower retained eigenvalue and trace at most one imply each rank bound. Under the joint law Pr⁡(X=x,J=j)=pxλxj, with zero-probability eigenvalues omitted, the random variable Z=−ln⁡λXJ has finite support and mean s. By [F7], the negative logarithm of each product eigenvalue is the sum of the coordinate self-information variables, so its IID sum controls the second failure probability exactly. The first failure is the same argument for the spectral law of ρˉ; averaging inputs gives ρˉ⊗n.

2.1F1F2F3F4F5F6F7step 1.1givenalgebraconstruct∎

For completeness, a finite supported centered variable Y with ∣Y∣≤b obeys EeuY≤1+u2b2e∣u∣b/2, by the Taylor remainder and EY=0. For small fixed u>0, ln⁡EeuY<uη/2. Markov's inequality and independence then bound Pr⁡(∑Yi≥nη)≤e−nuη/2; the logarithm product law [F7] gives the factor n when taking the log of the product moment generating function. Apply the same argument to −Y and add. A constant spectral variable has zero failure probability and needs no division by a range. All logarithms are evaluated only on positive-probability eigenvalues.

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