Alphabeta Math
TheoremStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedjudge pass (gpt-6-luna)
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.

Exact quantum, private, and entanglement-assisted erasure capacities

Statement

For the finite-dimensional flagged erasure channel Ed,ϵ with input dimension d≥1 and erasure probability ϵ∈[0,1],

Q(Ed,ϵ)=P(Ed,ϵ)=max⁡{1−2ϵ,0}ln⁡d,CE(Ed,ϵ)=2(1−ϵ)ln⁡d.

All capacities are in nats per channel use. The unassisted quantum and private formulas are zero in the antidegradable regime ϵ≥1/2.

Facts & Assumptions

Given: A data dimension d≥1, erasure probability ϵ∈[0,1], and the capacity conventions in the Statement.

[F1]

Coherent information of a qudit erasure channel: Ic(ρ,Ed,ϵ)=(1−2ϵ)S(ρ).

[F2]

Complement and degradation maps of the erasure channel: the channel is degradable for ϵ≤1/2 and antidegradable for ϵ≥1/2.

[F3]

Single-letter quantum and private capacities of degradable channels: for a degradable channel, Q=P=max⁡ρIc(ρ,N).

[F5]

Data processing for finite quantum mutual information: applying a channel to one register cannot increase finite quantum mutual information.

[F6]

Finite quantum mutual, conditional, and conditional mutual information: I(R;B)=S(R)+S(B)−S(RB) for finite bipartite states.

[F7]

Private information is an average coherent-information difference: private information is I(X;B)−I(X;E) for a finite input ensemble and its complementary output.

[F8]

The maximally mixed state maximizes finite quantum entropy: S(ρ)≤ln⁡d, with equality at I/d.

[F9]

Finite-dimensional channels compose and tensor: tensor products of finite-dimensional channels are channels and tensor products of complements implement the product dilation.

[F10]

Coherent information equals output entropy minus environment entropy: coherent information equals receiver-output entropy minus complementary-output entropy for every finite channel and fixed dilation.

Proof

1.1F1F8givenalgebra

By [F1] and [F8], the coherent information of an input state is (1−2ϵ)S(ρ), with S(ρ)≤ln⁡d and S(ρ)≥0. If ϵ≤1/2, its maximum is (1−2ϵ)ln⁡d, attained at I/d.

1.2F2F4F5F6F9F10givenalgebra

Now suppose ϵ≥1/2. By [F2], there is an antidegrading channel A:E→B with E=A∘Ec. Its tensor powers are channels by [F9]. For any n-use input state and purification reference R, [F5] gives I(R;Bn)≤I(R;En). The joint state RBnEn from a product Stinespring isometry is pure, so Schmidt decomposition gives S(RBn)=S(En) and S(REn)=S(Bn). By the mutual-information definition [F6], I(R;Bn)−I(R;En)=S(Bn)−S(En)−S(RBn)+S(REn)=2[S(Bn)−S(En)]=2Ic(ρ,E⊗n), where the last equality is [F10]. Hence the data-processing inequality forces Ic(ρ,E⊗n)≤0. A pure input attains zero, so the n-use coherent-information supremum is zero and [F4] gives Q=0.

2.1F2F3step 1.1algebra

For ϵ≤1/2, [F2] says the channel is degradable; applying [F3] to step 1.1 gives Q=P=(1−2ϵ)ln⁡d. This includes ϵ=1/2, where the value is zero.

2.2F4F5F6F7step 1.2algebra

For any finite n-use ensemble, [F7] and [F6] express private information as I(X;Bn)−I(X;En). The antidegrading channel on En and [F5] make this difference nonpositive. The singleton ensemble has private information zero, so each n-use private-information supremum is zero; [F4] gives P=0.

3.1F1F4F8algebra∎

By [F1] and [F4], the entanglement-assisted objective is S(ρ)+Ic(ρ,Ed,ϵ)=2(1−ϵ)S(ρ). Since 2(1−ϵ)≥0, [F8] makes its maximum 2(1−ϵ)ln⁡d, proving the final formula.

Depends on

Used by

Dependency tree · two levels

80 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