Alphabeta Math
Pipeline-generated
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.

Finite modular energy and entropy balance

1 · Prerequisites

2 · Summary

3 · Logical flowchart

4 · Definitions, postulates, theorems and proofs

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

Finite matrix entropy derivative at a faithful state

Statement

Let H be a nonzero finite-dimensional complex inner-product space with a fixed orthonormal basis, let J⊆R be an open interval, and let t0∈J. Suppose A:J→End⁡(H) has real and imaginary matrix entries in the fixed basis differentiable at t0 and is self-adjoint with strictly positive eigenvalues on an open neighborhood U⊆J of t0. Let tr⁡ be the finite-dimensional trace, and let ∥T∥op:=sup⁡∥v∥=1∥Tv∥ be the operator norm. For t∈U, define log⁡A(t) by applying the natural logarithm to the eigenvalues of A(t) in its spectral decomposition and set G:U→R by G(t):=tr⁡(A(t)log⁡A(t)). Then G is differentiable at t0 and

G′(t0)=tr⁡ ⁣(A′(t0)(log⁡A(t0)+IH)),

where IH is the identity endomorphism of H and A′(t0) is obtained by differentiating the matrix entries in the fixed basis.

Statement references

Facts & Assumptions

Given: A finite-dimensional complex inner-product space H, an open interval J with t0∈J, and an entrywise differentiable path A(t) that is self-adjoint with positive eigenvalues near t0.

[F1]

Finite matrix spectral and state calculus: every Hermitian endomorphism has an orthonormal eigenbasis with real eigenvalues; applying any real function to its finite spectrum defines a basis-independent endomorphism, including the logarithm on positive eigenvalues.

[F2]

The basis-independent trace of an endomorphism of a finite-dimensional vector space: in any ordered basis, the trace of an endomorphism is the sum of the diagonal entries of its matrix, and this value is basis-independent.

[F3]

The natural logarithm as the inverse of the exponential function: for x>0, log⁡x is the unique real y with exp⁡(y)=x; in particular log⁡1=0.

[F10]

Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm: for positive x,y, log⁡(xy)=log⁡x+log⁡y.

[F4]

The natural logarithm has derivative 1/x and equals the integral from 1 to x of 1/t: for x>0, log⁡ is differentiable and log⁡′(x)=1/x.

[F5]

For ∣r∣<1, ∑k≥0rk=1/(1−r), and for ∣r∣≥1 the series diverges: for real x with ∣x∣<1, ∑n=0∞xn=1/(1−x).

[F6]

Inside its radius a real power series may be differentiated term by term, and the differentiated series has the same radius: a real power series can be differentiated term by term at every point strictly inside its radius of convergence.

[F8]

The derivative f′(c)=lim⁡x→cf(x)−f(c)x−c of f:A→R at a point c∈A that is a limit point of A, and differentiability on a set: differentiability at t0 means the difference quotient has a finite limit, hence A(t0+h)=A(t0)+hA′(t0)+o(h) entrywise; at any real-valued differentiability point the difference quotient is locally bounded, so the function is continuous there.

[F9]

The operator norm as the least bound and as the unit-sphere or unit-ball supremum: ∥T∥op is the supremum of ∥Tv∥ over unit vectors, and therefore ∥Tv∥≤∥T∥op∥v∥ for every vector v by rescaling.

[F11]

The inner-product norm is definite, homogeneous, and satisfies the triangle inequality: the norm induced by an inner product satisfies ∥u+v∥≤∥u∥+∥v∥ for vectors u,v∈H.

[F12]

Cauchy–Schwarz: ∣⟨u,v⟩∣≤∥u∥∥v∥, with equality exactly for linearly dependent vectors: ∣⟨u,v⟩∣≤∥u∥∥v∥ for vectors u,v∈H.

Proof

1.1F3F4F5F6F7F8algebra

For ∣x∣<1, the power series P(x):=∑n=1∞xn/n converges absolutely because it is termwise bounded in absolute value by the geometric series in [F5]. Its radius is therefore at least 1, and [F6] gives P′(x)=∑n=1∞xn−1=1/(1−x). The difference quotient for log⁡ in [F4], with increment −h, gives ddx[−log⁡(1−x)]=1/(1−x). Both functions are differentiable on (−1,1) and hence continuous there by [F8]: at a differentiability point, the difference quotient is bounded near the point, so multiplying it by the increment shows that the function increment tends to zero. Since both functions vanish at 0 by [F3], [F7] applied to their difference yields ∑n=1∞xn/n=−log⁡(1−x) for every ∣x∣<1.

1.2F9F12algebra

Write d:=dim⁡H. For any endomorphism T, let M:=max⁡i,j∣Tij∣ in the fixed orthonormal basis. For x=∑jxjej, Cauchy–Schwarz [F12] applied in these coordinates to (Tij)j and (xj‾)j gives ∣(Tx)i∣=∣∑jTijxj∣≤(∑j∣Tij∣2)1/2(∑j∣xj∣2)1/2≤dM∥x∥ for each i, hence ∥Tx∥≤dM∥x∥. Thus T is bounded, so the operator norm [F9] is defined and finite, with ∥T∥op≤dM. For any endomorphisms S,T, [F9] then gives ∥STx∥≤∥S∥op∥Tx∥≤∥S∥op∥T∥op∥x∥ for every vector x. Taking the supremum over unit x proves ∥ST∥op≤∥S∥op∥T∥op.

2.1givenF1F8F9F10F11step 1.1step 1.2chooseconstruct

Put A0:=A(t0) and let λmax⁡ and λmin⁡ be its largest and smallest eigenvalues. They satisfy 0<λmin⁡≤λmax⁡ by [F1] and the hypothesis. Set c:=2λmax⁡, B:=IH−A0/c, and, for t∈U, Bt:=IH−A(t)/c. The eigenvalues of B lie in [1/2,1−λmin⁡/c]. In an orthonormal eigenbasis of B, writing its eigenvalues as bj, [F9] gives ∥B∥op=max⁡j∣bj∣: for x=∑jxjej, ∥Bx∥2=∑j∣bj∣2∣xj∣2≤(max⁡j∣bj∣)2∥x∥2, with equality on an eigenvector for a largest ∣bj∣. Hence q:=∥B∥op<1 by [F1]. Choose r with q<r<1. By [F8], every entry of A(t)−A0 tends to zero as t→t0. Since there are finitely many entries, M(t):=max⁡i,j∣(A(t)−A0)ij∣→0, and the matrix bound in step 1.2 gives ∥A(t)−A0∥op→0, so choose a neighborhood V⊆U of t0 where ∥A(t)−A0∥op/c<(r−q)/2. For every unit vector x, the inner-product norm triangle inequality [F11] and [F9] give ∥Btx∥≤∥Bx∥+∥(A(t)−A0)x∥/c≤q+∥A(t)−A0∥op/c<r for t∈V; taking the supremum proves ∥Bt∥op<r. Since Bt is self-adjoint, each eigenvalue β satisfies ∣β∣≤∥Bt∥op<r<1. For t∈V, the scalar identity in step 1.1 applied to these eigenvalues, the product law [F10], and spectral calculus [F1] give log⁡A(t)=(log⁡c)IH−∑n=1∞Btn/n, with convergence in operator norm because repeated application of step 1.2 gives ∥Btn∥op≤∥Bt∥opn<rn and ∑rn/n≤∑rn<∞.

3.1givenF9step 1.2step 2.1algebra

For t sufficiently near t0, put E:=A(t)−A0, Z:=−E/c, and z:=∥Z∥op. Then Bt=B+Z. The supremum definition gives z=∥E∥op/c, so z→0 as t→t0; shrink the neighborhood so that q+z≤r. Distributivity writes (B+Z)n=Bn+Sn+Rn, where Sn:=∑j=0n−1BjZBn−1−j contains the terms with exactly one Z, and Rn contains all terms with at least two Z factors. The submultiplicative bound in step 1.2 gives ∥Sn∥op≤nqn−1z. For Rn, each word with exactly k factors Z has norm at most qn−kzk, and there are (nk) such words, so ∥Rn∥op≤∑k=2n(nk)qn−kzk≤(n2)z2(q+z)n−2≤(n2)z2rn−2. The middle bound follows from (nk)≤(n2)(n−2k−2) for k≥2.

4.1F2F5F6F9F12step 2.1step 3.1algebra

Substituting the series of step 2.1 into G(A):=tr⁡(Alog⁡A), and using step 3.1, yields G(A0+E)−G(A0)=(log⁡c)tr⁡E−∑n=1∞tr⁡(EBn)+tr⁡(A0Sn)n+R(E), where ∣R(E)∣≤C∥E∥2 for a constant C independent of sufficiently small E. Indeed, the omitted terms are tr⁡(A0Rn), tr⁡(ESn), and tr⁡(ERn); for each orthonormal basis vector ei, the ith coordinate of Tei is ⟨Tei,ei⟩ and [F12] gives its magnitude at most ∥Tei∥∥ei∥=∥Tei∥≤∥T∥op by [F9], since ∥ei∥=1, so [F2] gives ∣tr⁡T∣≤d∥T∥op with d=dim⁡H, and the bounds in step 3.1 make their series after division by n bounded by constants times z2∑n≥2nrn−2, z2∑n≥1qn−1, and z3∑n≥2nrn−2, respectively. Each geometric derivative sum is finite by [F5]–[F6], and step 3.1 gives z=∥E∥op/c.

4.2F4F5F9F10step 1.1step 2.1step 3.1algebra

The linear matrix-log term read from the series in steps 2.1 and 3.1 is L(E):=1c∑n=1∞1n∑j=0n−1BjEBn−1−j. Its series converges in operator norm because the nth summand has norm at most qn−1∥E∥op/c. The remainder in the logarithm series is bounded by ∑n=2∞(n2)z2rn−2/n=(z2/2)∑n=2∞(n−1)rn−2=O(∥E∥op2), so L(E) is the full first-order term of log⁡(A0+E). In an orthonormal eigenbasis of A0, write A0ei=λiei and bi:=1−λi/c. Then L(E)ik=Eikc∑n=1∞1n∑j=0n−1bijbkn−1−j. If bi≠bk, the inner sum is (bin−bkn)/(bi−bk), so its coefficient is 1c∑n=1∞(bin−bkn)/nbi−bk=log⁡(1−bk)−log⁡(1−bi)c(bi−bk)=log⁡λi−log⁡λkλi−λk, where step 1.1 evaluates the series and [F10] gives log⁡λj=log⁡c+log⁡(1−bj), while λi−λk=c(bk−bi). If bi=bk, the coefficient is c−1∑n=1∞bin−1=1/(c(1−bi))=1/λi=log⁡′(λi) by [F4] and [F5]. Defining log⁡[1](x,y):=(log⁡x−log⁡y)/(x−y) for x≠y and log⁡[1](x,x):=1/x, we have L(E)ik=log⁡[1](λi,λk)Eik: this is the divided-difference derivative of the matrix logarithm.

5.1F2step 2.1step 4.1step 4.2algebra

In a fixed basis, [F2] gives tr⁡(XY)=∑i,jXijYji=∑i,jYjiXij=tr⁡(YX), so trace is cyclic. Since A0 commutes with B and Z=−E/c, cycling each term gives tr⁡(A0Sn)=−(n/c)tr⁡(A0Bn−1E). Thus the A0Sn terms in step 4.1 sum to tr⁡(A0L(E)). In the eigenbasis used in step 4.2, L(E)ii=Eii/λi, so tr⁡(A0L(E))=∑iλiL(E)ii=tr⁡E. The remaining linear terms in step 4.1 are tr⁡(Elog⁡A0) by the spectral series in step 2.1. Therefore G(A0+E)−G(A0)=tr⁡(E(log⁡A0+IH))+O(∥E∥op2).

6.1givenF8step 2.1step 5.1algebra∎

By [F8], for h→0 the path increment is E=A(t0+h)−A0=hA′(t0)+o(h) entrywise. The finite-coordinate bound in step 2.1 gives ∥E∥op=O(∣h∣). Dividing the expansion in step 5.1 by h makes its O(∥E∥op2) remainder tend to 0 and its linear term tend to tr⁡(A′(t0)(log⁡A(t0)+IH)). This is the stated derivative.

Remarks

The matrix logarithm derivative is proved here for finite-dimensional faithful matrices. It makes no assertion about singular reference states or infinite-dimensional operator algebras.

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

Finite relative entropy equals modular energy minus entropy

Statement

Let H be a nonzero finite-dimensional complex inner-product space. Let ρ,σ:H→H be self-adjoint linear maps with nonnegative eigenvalues and trace one, and suppose every eigenvalue of σ is strictly positive. Define Kσ:=−log⁡σ by spectral calculus, and for any self-adjoint τ:H→H with nonnegative eigenvalues and trace one define s(τ):=−tr⁡(τlog⁡τ)=−∑λj(τ)>0λj(τ)log⁡λj(τ), with the zero-eigenvalue term 0log⁡0:=0 and natural logarithms, so the entropy is in nats. Define the modular-energy difference relative to σ by ΔK(ρ,σ):=tr⁡((ρ−σ)Kσ). Then

D(ρ∥σ)=ΔK(ρ,σ)−(s(ρ)−s(σ)).

For any nonzero finite-dimensional complex inner-product spaces HA,HB with H=HA⊗HB, partial tracing both states over HB cannot increase relative entropy:

D(Tr⁡Bρ∥Tr⁡Bσ)≤D(ρ∥σ).

The identity and inequality are finite-dimensional mathematical statements; no Hamiltonian, physical energy, physical units, continuum limit, or gravitational entropy is assumed.

Facts & Assumptions

Given: A nonzero finite-dimensional complex inner-product space H and self-adjoint ρ,σ:H→H with nonnegative eigenvalues and trace one, with σ having strictly positive eigenvalues.

[F1]

Finite-dimensional quantum relative entropy: for nonnegative endomorphisms A,B, if supp⁡(A)⊆supp⁡(B) then D(A∥B)=tr⁡(Alog⁡A)−tr⁡(Alog⁡B) using spectral logarithms on positive supports; otherwise D(A∥B)=+∞.

[F2]

Finite-dimensional von Neumann entropy defines the von Neumann entropy S(τ)=−tr⁡(τlog⁡τ) for a finite-dimensional density endomorphism using the natural logarithm. The function s in the Statement is the same finite spectral sum, written with lowercase s and the convention 0log⁡0:=0.

[F3]

The basis-independent trace of an endomorphism of a finite-dimensional vector space: tr⁡(T) is the basis-independent trace of a finite-dimensional endomorphism, given by the sum of diagonal matrix entries in any basis; it is linear by this finite-sum definition.

[F4]

Partial trace over a finite tensor factor: for nonnegative R on HA⊗HB, the partial trace Tr⁡BR is the endomorphism of HA whose product-basis entries are (Tr⁡BR)ii′=∑jR(i,j),(i′,j).

[F5]

Partial-trace data processing for finite quantum relative entropy: for nonnegative A,B on a finite tensor product HA⊗HB, D(Tr⁡BA∥Tr⁡BB)≤D(A∥B).

Proof

1.1givenF1F2algebra

Since σ has only positive eigenvalues, supp⁡(σ)=H and hence supp⁡(ρ)⊆supp⁡(σ). By [F1], D(ρ∥σ) is finite and equals tr⁡(ρlog⁡ρ)−tr⁡(ρlog⁡σ)=−s(ρ)+tr⁡(ρKσ).

2.1F2F3step 1.1algebra

By [F2] and Kσ=−log⁡σ, s(σ)=tr⁡(σKσ). Linearity of trace [F3] then gives ΔK(ρ,σ)−(s(ρ)−s(σ))=tr⁡(ρKσ)−tr⁡(σKσ)−s(ρ)+s(σ)=tr⁡(ρKσ)−s(ρ)=D(ρ∥σ) by step 1.1. This proves the finite modular-energy identity.

3.1givenF4F5step 2.1algebra∎

With the finite identity established in step 2.1, apply [F5] for the separate partial-trace claim to the nonnegative endomorphisms ρ,σ on HA⊗HB, with the partial trace defined in [F4], to obtain the displayed inequality.

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

Finite-dimensional entropy first variation at a faithful state

Statement

Let H be a nonzero finite-dimensional complex inner-product space and let I⊆R be an open interval containing 0. Let ρ:I→End⁡(H) be a path whose real and imaginary matrix entries are continuously differentiable on I, such that each ρ(ε) is self-adjoint with nonnegative eigenvalues and trace one. Suppose ρ(0)=σ and every eigenvalue of σ is strictly positive. Define Kσ:=−log⁡σ by spectral calculus and let s(τ):=−tr⁡(τlog⁡τ) with the finite spectral convention 0log⁡0:=0. Then

ddεs(ρ(ε))∣ε=0=tr⁡(ρ′(0)Kσ).

If, in addition, the real and imaginary matrix entries of ρ are twice continuously differentiable on I, then the finite relative entropy D(ρ(ε)∥σ) has zero first derivative at ε=0. These are finite-dimensional mathematical statements and make no continuum or gravitational claim.

Facts & Assumptions

Given: A continuously differentiable finite-dimensional density path ρ(ε) on an open interval containing 0, with ρ(0)=σ strictly positive; for the relative-entropy derivative conclusion, additionally assume that its real and imaginary matrix entries are twice continuously differentiable on that interval.

[F1]

Finite matrix entropy derivative at a faithful state: for a differentiable path of self-adjoint positive-definite matrices, ddεtr⁡(ρlog⁡ρ)=tr⁡(ρ′(log⁡ρ+I)).

[F2]

Finite relative entropy equals modular energy minus entropy: for each density endomorphism τ and the fixed faithful reference σ, D(τ∥σ)=tr⁡((τ−σ)Kσ)−(s(τ)−s(σ)).

[F3]

Finite matrix spectral and state calculus: finite-dimensional Hermitian endomorphisms have orthonormal spectral decompositions, and the eigenvalues of a strictly positive one have a positive minimum.

[F4]

The basis-independent trace of an endomorphism of a finite-dimensional vector space: trace is the finite sum of diagonal entries and therefore is linear.

[F5]

The operator norm as the least bound and as the unit-sphere or unit-ball supremum: ∥Tv∥≤∥T∥op∥v∥ for every vector v.

[F6]

Cauchy–Schwarz: ∣⟨u,v⟩∣≤∥u∥∥v∥, with equality exactly for linearly dependent vectors: ∣⟨u,v⟩∣≤∥u∥∥v∥ for vectors in a complex inner-product space.

Proof

1.1givenF1F3F5F6choosealgebra

Let m>0 be the smallest eigenvalue of σ from [F3]. Since ρ is entrywise continuous at 0, its finite matrix difference T(ε):=ρ(ε)−σ tends to zero in operator norm: in a fixed orthonormal basis, if every entry of T has magnitude at most M, then for a unit vector x ∑j∣xj∣2=1 makes each coordinate of x have magnitude at most 1, and each coordinate of Tx has magnitude at most dM, and therefore ∥Tx∥≤d3/2M, where d=dim⁡H. Choose δ>0 so that ∥T(ε)∥op<m/2 for ∣ε∣<δ. For each v∈H, ⟨σv,v⟩≥m∥v∥2 by spectral decomposition, while [F5] and [F6] give ∣⟨T(ε)v,v⟩∣≤∥T(ε)∥op∥v∥2. Hence ⟨ρ(ε)v,v⟩>(m/2)∥v∥2 for nonzero v. Thus ρ(ε) is positive definite near 0 and [F1] applies.

2.1givenF1F4step 1.1algebra

Apply [F1] to ρ at 0: ddεs(ρ(ε))∣ε=0=−tr⁡(ρ′(0)(log⁡σ+I)). Because tr⁡(ρ(ε))=1 on the whole interval, in the fixed basis [F4] gives tr⁡(ρ′(0))=∑iρii′(0)=lim⁡h→0∑i(ρii(h)−ρii(0))/h=lim⁡h→0(1−1)/h=0; the finite sum commutes with this limit. Therefore ddεs(ρ(ε))∣0=−tr⁡(ρ′(0)log⁡σ)=tr⁡(ρ′(0)Kσ), as claimed.

3.1givenF2step 2.1algebra∎

The identity [F2] holds for every ρ(ε) in the path. Its right-hand side is differentiable at 0 by linearity of trace and step 2.1, so differentiating gives ddεD(ρ(ε)∥σ)∣0=tr⁡(ρ′(0)Kσ)−ddεs(ρ(ε))∣0=0. The additional twice continuously differentiable hypothesis ensures the differentiability required here.

5 · Examples, experiments and counterexamples

None yet.

Sources