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DefinitionDefinition: AI-adaptedProof: AI-adaptedPipeline-generatedprecheck passverified 2026-10-22 (gpt-6.1-sol)
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Finite-regulator outside density and entropy

Definition

Let Hin and Hout be nonzero finite-dimensional complex inner-product spaces, and let ρ be a density operator on Hin⊗Hout. Choose orthonormal bases (ei)i=1din and (fa)a=1dout, and use the ordered product basis (ei⊗fa), with the Hin index first. The outside density ρout:Hout→Hout is the linear map whose matrix entries in (fa) are

(ρout)ab:=∑i=1dinρ(i,a),(i,b).

Fix kB>0 as a real scale. If λ1,…,λdout are the eigenvalues of ρout counted with multiplicity, define

Sout(ρ;Hin,Hout):=−kB∑j=1doutλjlog⁡λj,

where log⁡ is the natural logarithm and 0log⁡0:=0. The finite matrix argument below proves that ρout is a density operator and that the displayed endomorphism is basis-independent, so Sout is a finite real number independent of the chosen bases. In this mathematical definition kB is only a positive scalar; no physical units or model are asserted.

Definition references

Facts & Assumptions

Given: Nonzero finite-dimensional complex inner-product spaces Hin,Hout; a density operator ρ on their ordered tensor product; orthonormal bases of each factor; and a fixed real kB>0.

[F1]

Density operator: on a nonzero finite-dimensional complex inner-product space, a density operator is a non-negative endomorphism of trace one.

[F2]

Non-negative and positive operators: a non-negative operator is self-adjoint and has non-negative quadratic form on every vector.

[F3]

Ordered tensor registers, their inner product, and wire permutations: the factor order is retained, the ordered product of the chosen orthonormal factor bases is an orthonormal basis of Hin⊗Hout, and the tensor-product inner product on elementary tensors is the product of the factor inner products; that convention is linear in the first argument.

[F4]

Linear map between vector spaces over the same field: a linear map is a function between vector spaces over the same field that preserves linear combinations.

[F5]

Coordinate columns [v]B and matrices [T]BC of linear maps relative to ordered bases: the column for an input basis vector is the coordinate column of its image in the output basis.

[F7]

The trace tr⁡(A) as the sum of the diagonal entries defines matrix trace as the sum of diagonal entries; The basis-independent trace of an endomorphism of a finite-dimensional vector space defines endomorphism trace by a matrix in any ordered basis and establishes basis-independence.

[F8]

Finite-dimensional von Neumann entropy: for a finite density operator, entropy is the finite spectral sum −∑λj>0λjln⁡λj, with zero-eigenvalue contribution 0 and natural logarithms.

[F9]

A non-negative operator is equivalently self-adjoint with nonnegative eigenvalues, a positive semidefinite matrix in an orthonormal basis, or an operator of the form S^*S: a non-negative endomorphism of a finite-dimensional complex inner-product space has an orthonormal eigenbasis with nonnegative real eigenvalues.

Proof

1.1givenF3F4F5F6constructalgebra

For each basis vector fb, prescribe ρoutfb:=∑a=1dout(∑i=1dinρ(i,a),(i,b))fa. The product basis in [F3] supplies the matrix entries of ρ, and [F4]–[F6] give a unique linear extension from the prescribed images; its matrix is exactly the displayed formula.

1.2givenF1F2F3F7algebra

Since ρ is self-adjoint by [F1]–[F2], its matrix satisfies ρ(i,b),(i,a)‾=ρ(i,a),(i,b), so ρout is self-adjoint. For every y∈Hout, expansion in (fa) gives ⟨ρouty,y⟩=∑i⟨ρ(ei⊗y),ei⊗y⟩≥0 by [F2]–[F3]. Its trace is ∑a(ρout)aa=∑i,aρ(i,a),(i,a)=tr⁡ρ=1 by [F1] and [F7]; hence ρout is a density operator by [F1].

1.3givenF3F4F5F6F7algebra

For every endomorphism B of Hout, define Iin⊗B on the product basis by (Iin⊗B)(ei⊗fb):=ei⊗Bfb; [F3]–[F6] give its unique linear extension. For arbitrary x,y, expansion in the chosen bases shows this map sends x⊗y to x⊗By, so it is independent of those bases. Finite matrix multiplication gives tr⁡(ρoutB)=∑i,a,bρ(i,a),(i,b)Bba=tr⁡(ρ(Iin⊗B)). The right side is therefore basis-independent. If a second choice produced ρ~out, then tr⁡((ρ~out−ρout)B)=0 for all B; taking B to be the matrix unit with sole nonzero entry in row b, column a gives tr⁡((ρ~out−ρout)B)=(ρ~out−ρout)ab, so every entry of the difference is zero. Thus the endomorphism is basis-independent.

2.1givenF7F8F9step 1.2step 1.3algebra∎

By [F9], ρout has an orthonormal eigenbasis with nonnegative real eigenvalues; [F7] and step 1.2 show their sum is tr⁡(ρout)=1, so each lies in [0,1]. The finite spectral sum in [F8] is therefore real, with zero eigenvalues contributing 0 by convention. Its eigenvalue multiset is intrinsic to the endomorphism, and step 1.3 proves that endomorphism is basis-independent; multiplying by the fixed real kB preserves finiteness and basis-independence.

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