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.

Every finite-dimensional channel has a finite isometric dilation

Statement

Let H,K be finite-dimensional complex inner-product spaces and let Φ:L(H)→L(K) be a quantum channel. It has a finite Kraus representation, and for any chosen such representation with N operators Ka:H→K there are an N-dimensional environment E and a linear isometry V:H→K⊗E such that Φ(X)=Tr⁡E(VXV†)for every X∈L(H).

Facts & Assumptions

Given: Finite-dimensional complex inner-product spaces H,K and a channel Φ:L(H)→L(K).

[F1]

Finite-dimensional quantum channel: a channel is completely positive and trace-preserving.

[F2]

Kraus and Choi characterizations of complete positivity: complete positivity gives a finite Kraus representation.

[F3]

Trace preservation is Kraus normalization: for any such Kraus representation, trace preservation gives ∑aKa†Ka=IH.

[F4]

Finite Kraus representation: the chosen family satisfies Φ(X)=∑aKaXKa† for every X.

[F6]

Ordered tensor registers, their inner product, and wire permutations and The product basis is orthonormal and tensor dimensions multiply: the tensor pairing is the factorwise pairing and the ordered product basis is orthonormal.

[F7]

Tensor products of finitely many linear maps: tensor maps act linearly on elementary tensors.

[F10]

Partial trace over a finite tensor factor and The partial trace is characterized by the trace pairing and is basis-independent: in the product basis of K⊗E, partial trace over E sums the diagonal environment blocks.

[F11]

Finite-dimensional linear isometries satisfy the adjoint identity: a linear isometry V satisfies V†V=IH.

[L1]

Isometric Stinespring representation: a Stinespring representation has the form X↦Tr⁡E(VXV†).

Proof

1.1givenF1F2F3F5F6F7F8F11algebrachooseconstruct

By [F1] and [F2], a finite Kraus family exists; take any such family (Ka)a<N for Φ. Since Φ is trace-preserving, [F3] gives ∑a<NKa†Ka=IH. Take E=CN with its standard orthonormal basis (ua)a<N and define Vx=∑a<NKax⊗ua. Each summand is linear in x by [F7], so V is linear. Orthogonality of (ua), the product pairing [F6], the adjoint identity [F8], and the Kraus normalization give ∥Vx∥2=∑a∥Kax∥2=⟨x,(∑aKa†Ka)x⟩H=∥x∥2 for every x; thus V is a linear isometry. By [F11], V†V=IH.

2.1givenF3F4F5F6F8F9F10L1step 1.1algebra∎

For each b<N, y∈K, and x∈H, the product pairing and [F8] give ⟨Vx,y⊗ub⟩=∑a⟨Kax,y⟩⟨ua,ub⟩=⟨Kbx,y⟩=⟨x,Kb†y⟩. By uniqueness of the adjoint, V†(y⊗ub)=Kb†y. Therefore, on each product vector y⊗ub, VXV†(y⊗ub)=∑a<NKaXKb†y⊗ua. By [F10], taking the partial trace sums exactly the blocks with a=b, so Tr⁡E(VXV†)=∑a<NKaXKa†=Φ(X) by [F4] for every X. This is the representation in [L1], with dim⁡E=N. If N=0, [F3] forces IH=0, hence H=0; the empty sums and unique map give the same identities.

Depends on

Used by

Dependency tree · two levels

65 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