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Finite-regulator entropy mathematics

1 · Prerequisites

2 · Summary

3 · Logical flowchart

4 · Definitions, postulates, theorems and proofs

DefinitionDefinition: AI-adaptedProof: AI-adaptedprecheck passverified 2026-10-22 (gpt-6.1-sol)Open item page →

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.

LemmaStatement: AI-adaptedProof: AI-adaptedprecheck passverified 2026-10-22 (gpt-6.1-sol)Open item page →

Finite mode cutoffs can have unbounded entropy

Statement

Fix a real number kB>0. For each integer N≥1, let Hin=C with its standard inner product and basis 1, and let Hout,N=(C2)⊗N with the standard product inner product and product basis. Identify the product basis of C⊗Hout,N with that of Hout,N. For the density operator ρN=I2N/2N, where I2N is the identity matrix on this 2N-dimensional space, the outside entropy is

Sout(ρN;C,Hout,N)=kBNlog⁡2,

which tends to +∞ as N→∞.

Facts & Assumptions

Given: A positive real kB, an integer N≥1, the one-dimensional input space C, and the N-factor output space (C2)⊗N.

[F1]

Finite-regulator outside density and entropy: the outside density is obtained by summing the diagonal blocks over an orthonormal basis of the input factor, and its entropy is −kB times the natural-log spectral sum.

[F2]

Density operator: a density operator on a nonzero finite-dimensional space is non-negative and has trace one.

[F3]

The natural logarithm as the inverse of the exponential function: log⁡ is the inverse of the real exponential on (0,∞).

[F4]

The exponential is positive and satisfies exp⁡(−x)=1/exp⁡(x): for every real x, exp⁡(x)>0 and exp⁡(−x)=1/exp⁡(x).

[F5]

The real exponential function and the number e by a power series: the real exponential is defined by its power series; at input 0, every positive-degree term vanishes and the zero-degree term is 1, so exp⁡(0)=1.

[F6]

The exponential addition formula exp⁡(x+y)=exp⁡(x)exp⁡(y): exp⁡(x+y)=exp⁡(x)exp⁡(y) for real x,y.

[F7]

The exponential function is strictly increasing: the real exponential is strictly increasing, hence injective.

[F8]

In every complete ordered field F, for each x∈F there is a natural number n≥1 with x<n⋅1F (Every complete ordered field is Archimedean).

[F9]

Ordered tensor registers, their inner product, and wire permutations: tensoring the standard basis in each C2 factor gives the ordered product basis of Hout,N, so its dimension is 2N.

[F10]

The library real numbers are the Cauchy-sequence quotient R=C/N (The real numbers), and this real field has the least-upper-bound property, so it is a complete ordered field (The Cauchy-sequence reals have the least-upper-bound property).

Proof

1.1givenF1F2F9algebra

The matrix ρN=I2N/2N is non-negative and has trace 2N/2N=1, so it is a density operator by [F2] and [F9]. Since the input factor is one-dimensional, the defining partial trace in [F1] has one summand and gives ρout,N=I2N/2N.

1.2givenF3F4F6F7algebra

The spectrum of ρout,N consists of 2N copies of 2−N. For N=1, exp⁡(Nlog⁡2)=2 by [F3]; if exp⁡(Nlog⁡2)=2N, [F6] gives exp⁡((N+1)log⁡2)=exp⁡(Nlog⁡2)exp⁡(log⁡2)=2N+1. Induction yields exp⁡(Nlog⁡2)=2N for every N≥1. By [F4], exp⁡(−Nlog⁡2)=1/2N=2−N, so [F3] and injectivity in [F7] give log⁡(2−N)=−Nlog⁡2.

2.1givenF1F5F7step 1.2algebra

Substitution into [F1] gives Sout=−kB 2N2−Nlog⁡(2−N)=kBNlog⁡2. Also log⁡2>0, because [F5] gives exp⁡(0)=1<2=exp⁡(log⁡2) and [F7] is strictly increasing.

3.1givenF8F10step 2.1algebra∎

Let c=kBlog⁡2>0. For any real M, [F10] makes R a complete ordered field, so [F8] applied to M/c supplies a natural number N0≥1 with N0>M/c. For every integer N≥N0, step 2.1 gives Sout=cN≥cN0>M, which is exactly convergence to +∞.

LemmaStatement: AI-adaptedProof: AI-generatedprecheck passverified 2026-10-22 (gpt-6.1-sol)Open item page →

Finite subtraction ambiguity

Statement

Let x=(xN)N≥1 and r=(rN)N≥1 be real sequences, and let L∈R. Here xN−rN→L means that for every ε>0 there is an integer N0 such that ∣xN−rN−L∣<ε for all N≥N0. For every a∈R, the equally valid subtraction rN′=rN−a satisfies

xN−rN′⟶L+a.

Thus existence of a finite subtracted limit alone does not select its additive finite part; no particular divergence expansion or physical renormalization scheme is assumed.

Facts & Assumptions

Given: Real sequences x=(xN)N≥1 and r=(rN)N≥1, a finite real number L, and xN−rN→L in the epsilon definition stated above.

[A1]

For every ε>0, some N0 satisfies ∣xN−rN−L∣<ε for all N≥N0.

Proof

1.1givenA1choose

Fix any real a and any ε>0. By [A1], choose N0 so that ∣xN−rN−L∣<ε for every N≥N0.

2.1givenstep 1.1algebra∎

For every N≥N0, ∣xN−rN′−(L+a)∣=∣xN−(rN−a)−(L+a)∣=∣xN−rN−L∣<ε. Since a and ε were arbitrary, the claimed limit is L+a for every a∈R.

5 · Examples, experiments and counterexamples

None yet.

Sources