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Entropy inequalities, recovery boundaries and thermal entanglement

1 · Prerequisites

2 · Summary

3 · Logical flowchart

4 · Definitions, postulates, theorems and proofs

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

Dimensionless entropy, mutual and conditional information

Definition

All Hilbert spaces in this item are nonzero and finite-dimensional over C. A density operator ρ on H is a Hermitian positive semidefinite endomorphism with Tr⁡ρ=1. Its dimensionless von Neumann entropy, in nats, is

S(ρ):=−Tr⁡(ρlog⁡ρ)=−∑j:λj>0λjlog⁡λj,

where λj are the eigenvalues of ρ, the logarithm is natural, and 0log⁡0:=0. For any positive semidefinite endomorphisms X,Y on one finite-dimensional H, let supp⁡X:=(ker⁡X)⊥. Define

D(X∥Y):=Tr⁡(Xlog⁡X)−Tr⁡(Xlog⁡Y)

when supp⁡X⊆supp⁡Y, taking each logarithm spectrally on its positive support and setting zero-eigenvalue contributions to zero; set D(X∥Y):=+∞ otherwise. For density operators this is the finite-dimensional quantum relative entropy in nats.

For a density operator ρAB on the ordered product HA⊗HB, define ρA:=Tr⁡BρAB and ρB:=Tr⁡AρAB, where in orthonormal product bases (Tr⁡BR)ii′=∑jR(i,j),(i′,j) and the first factor is retained. Define

I(A:B)ρ:=S(ρA)+S(ρB)−S(ρAB).

For a density operator ρABC on HA⊗HB⊗HC, define

ρAB:=Tr⁡CρABC,ρBC:=Tr⁡AρABC,ρB:=Tr⁡A(Tr⁡CρABC),

where each partial trace contracts the indicated tensor factor and retains the other factors in their displayed order.

I(A:C∣B)ρ:=S(ρAB)+S(ρBC)−S(ρB)−S(ρABC),

using the partial traces in the displayed factor order. For any fixed positive conversion factor kB with units J/K, the corresponding physical entropy is Sphys(ρ):=kBS(ρ); no numerical value of kB is specified here.

Facts & Assumptions

Given: Nonzero finite-dimensional complex inner-product spaces HA,HB,HC and density operators on the tensor products appearing in the Definition. Use the ordered tensor factors displayed there and natural logarithms.

[F1]

Density operator: a density operator is Hermitian positive semidefinite and has trace one.

[F2]

Finite-dimensional von Neumann entropy: for a finite density operator, S(ρ)=−Tr⁡(ρlog⁡ρ) with zero eigenvalues contributing 0log⁡0:=0.

[F3]

Finite-dimensional quantum relative entropy: D(X∥Y) uses support inclusion and support-restricted logarithms; it is +∞ when support inclusion fails.

[F4]

Partial trace over a finite tensor factor: the second tensor factor is contracted by summing its equal row and column indices in an ordered product basis.

[F5]

Partial traces of a finite bipartite state are states: both marginals of a finite bipartite density operator are density operators.

[F6]

Partial trace over the first tensor factor: in an ordered product basis, (Tr⁡AR)jj′=∑iR(i,j),(i,j′); for a grouped residual tensor product, its displayed factor order is retained.

Proof

1.1givenF1F4F5F6

By [F1], the input is a positive trace-one endomorphism on its finite tensor product. Apply [F5] to each bipartite grouping of a finite multipartite density operator; this makes every one-factor and two-factor marginal used in the Definition a density operator. Repeated partial traces use [F4] for the second factor and [F6] for the first factor, retaining the stated residual tensor-factor order.

2.1givenF2F3step 1.1algebra∎

Each such density operator has finitely many eigenvalues in [0,1], so the finite entropy sum in [F2] is a real number, including when some eigenvalues vanish. The relative entropy clause is defined on exactly the two support cases in [F3]. Therefore I(A:B) and I(A:C∣B) are finite real-valued functions on the stated density operators, and D has the extended-real domain specified in the Definition.

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

Conditional mutual information and finite channel monotonicity

Statement

Let HA,HB,HC be nonzero finite-dimensional complex inner-product spaces and let ρABC be a density operator on HA⊗HB⊗HC. With dimensionless entropy S and conditional mutual information I(A:C∣B) defined by the conventions below,

I(A:C∣B)ρ=S(ρAB)+S(ρBC)−S(ρB)−S(ρABC)≥0.

Also let d,e≥1 and let Φ:Md(C)→Me(C) be a finite completely positive trace-preserving map. For all positive semidefinite X,Y∈Md(C),

D(Φ(X)∥Φ(Y))≤D(X∥Y),

where D is the support-sensitive finite-dimensional quantum relative entropy: it equals Tr⁡(Xlog⁡X)−Tr⁡(Xlog⁡Y) when supp⁡X⊆supp⁡Y, and +∞ otherwise. These are finite-dimensional mathematical statements. The channel inequality is one-sided and asserts no equality condition, recovery map, or infinite-dimensional extension.

Stinespring factorization diagram

diagram failed to render:
\begin{tikzcd}
M_d(\mathbb C)_+ \arrow[r, "X\mapsto VXV^*"] \arrow[dr, "\Phi"'] & \operatorname{End}(\mathbb C^e\otimes\mathcal E)_+ \arrow[d, "\operatorname{Tr}_{\mathcal E}"] \\
& M_e(\mathbb C)_+
\end{tikzcd}

Here Md(C)+ denotes positive semidefinite matrices, E is the finite environment constructed in step 1.2, and V is its isometry. The triangle commutes because step 1.2 computes Tr⁡E(VXV∗)=Φ(X) entrywise. The proof uses that computation and the stated partial-trace inequality, not the diagram.

Facts & Assumptions

Given: The spaces and state in the first clause of the Statement; integers d,e≥1 and the finite channel Φ in the second clause. Use natural logarithms and the ordered finite tensor conventions of the cited definitions.

[A1]

Dimensionless entropy, mutual and conditional information: I(A:C∣B)ρ=S(ρAB)+S(ρBC)−S(ρB)−S(ρABC), with each marginal formed by partial trace.

[A2]

Finite completely positive trace-preserving maps: a finite channel is a complex-linear completely positive and trace-preserving map on matrix algebras.

[F1]

Strong subadditivity of finite-dimensional quantum entropy: for a density operator on any ordered finite triple HA⊗HB⊗HC, S(ρAB)+S(ρBC)≥S(ρB)+S(ρABC).

[F2]

Partial-trace data processing for finite quantum relative entropy: for positive semidefinite R,S on a finite tensor product, D(Tr⁡ER∥Tr⁡ES)≤D(R∥S), including the extended-real support-failure cases.

[F3]

Complete finite Choi-Kraus equivalence: a finite CPTP map has a finite Kraus representation Φ(X)=∑a=1qKaXKa∗, and trace preservation entails ∑aKa∗Ka=Id.

[F4]

Partial trace over a finite tensor factor: in product bases, the partial trace over the second factor sums diagonal blocks in that factor.

[F5]

Finite-dimensional quantum relative entropy: D(R∥S) is finite on support inclusion and +∞ otherwise; the finite branch uses support-restricted spectral logarithms.

[F6]

Finite matrix spectral and state calculus: positive finite matrices have finite spectral decompositions and spectral logarithms on their positive supports; the finite trace is basis-independent and satisfies Tr⁡(AB)=Tr⁡(BA).

Proof

1.1givenA1F1

By [A1], the left side of the first assertion is the displayed four-entropy difference. Apply [F1] to the given state with the same factor order; the resulting inequality is exactly I(A:C∣B)ρ≥0.

1.2givenA2F3F4constructalgebra

By [F3], choose a finite Kraus list K1,…,Kq with ∑aKa∗Ka=Id; because d≥1, this identity rules out q=0. Let E=Cq with orthonormal basis (fa) and define V:Cd→Ce⊗E by Vx=∑aKax⊗fa. Orthogonality of the fa gives ⟨Vx,Vy⟩=∑a⟨Kax,Kay⟩=⟨x,(∑aKa∗Ka)y⟩=⟨x,y⟩, so V∗V=Id. The coordinate formula [F4] then gives Tr⁡E(VXV∗)=∑aKaXKa∗=Φ(X) for every X∈Md(C).

2.1givenF5F6step 1.2algebra

The isometry V maps each positive-eigenvalue eigenspace of X≥0 onto the corresponding eigenspace of VXV∗, with the same eigenvalue; the orthogonal complement of ran⁡V is in the kernel. Hence supp⁡(VXV∗)=V(supp⁡X), and likewise for Y. Because V is injective, support inclusion holds for X,Y exactly when it holds for VXV∗,VYV∗. If it fails, both relative entropies are +∞ by [F5]. If it holds, the support-restricted spectral logarithms satisfy log⁡(VXV∗)=V(log⁡X)V∗ and log⁡(VYV∗)=V(log⁡Y)V∗ on the corresponding supports. The trace identity in [F6] therefore yields D(VXV∗∥VYV∗)=D(X∥Y).

3.1givenF2step 1.2step 2.1algebra∎

Apply [F2] to R=VXV∗ and S=VYV∗, which are positive semidefinite because V is linear and X,Y≥0. By step 1.2 their partial traces are Φ(X),Φ(Y), and by step 2.1 their relative entropy equals D(X∥Y). Thus D(Φ(X)∥Φ(Y))≤D(X∥Y), including when the right side is +∞.

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

Boundary energy bounds thermal mutual information

Statement

Assume the Axiom of Choice. Let HA,HB be nonzero finite-dimensional complex inner-product spaces. Let HA=HA∗ act on HA, let HB=HB∗ act on HB, and let H∂=H∂∗ act on HA⊗HB. Set

H:=HA⊗IB+IA⊗HB+H∂.

For β>0, define Zβ:=Tr⁡(e−βH) and the finite Gibbs density ρβ:=e−βH/Zβ. Then

I(A:B)ρβ≤2β∥H∂∥op,

where I(A:B) uses dimensionless entropy in nats and ∥K∥op:=sup⁡∥v∥=1∥Kv∥ is the operator norm.

For the optional lattice-family conclusion, fix m≥1. For each member s of a specified family, take a finite site set Λs⊆Zm, a partition Λs=As⊔Bs, and nonzero finite-dimensional complex inner-product spaces Ks,x for x∈Λs. Use the lexicographic order on Zm in Hs,X:=⨂x∈XKs,x, with the empty tensor product defined as C, and identify Hs,Λs with Hs,As⊗Hs,Bs in the displayed factor order. Let Js be a finite set of interaction labels with nonempty supports Xs,j⊆Λs and self-adjoint terms hs,j on Hs,Xs,j. For Y∈{Λs,As,Bs} containing Xs,j, write h^s,jY for the extension of hs,j to Hs,Y by the identity on every factor in Y∖Xs,j. The crossing-label set is

J∂,s:={j∈Js:Xs,j∩As≠∅ and Xs,j∩Bs≠∅},∣∂As∣:=∣J∂,s∣.

Let JA,s:={j∈Js:Xs,j⊆As} and JB,s:={j∈Js:Xs,j⊆Bs}. Set HAs:=∑j∈JA,sh^s,jAs, HBs:=∑j∈JB,sh^s,jBs, and H∂,s:=∑j∈J∂,sh^s,jΛs. Under the stated identification these give the displayed Hamiltonian decomposition. For each s, assume βs>0 and define ρβs by the Gibbs formula above. If one specified C≥0 satisfies ∥H∂,s∥op≤C∣∂As∣ for every family member, the bound gives I(As:Bs)ρβs≤2βsC∣∂As∣ memberwise. No finite-range condition or additional geometric scaling assumption is used. When H∂,s and each interaction term are measured in joules, βs has units J−1 and C has units J, so the bound is dimensionless.

Facts & Assumptions

Given: The spaces and Hermitian matrices in the Statement, β>0, natural logarithms, and a fixed kB>0 with units J/K when physical units are assigned. Put T=(kBβ)−1.

[A1]

The Axiom of Choice: every family of nonempty sets has a choice function. This is inherited through the Gibbs variational supplier in [F1] and its [F2] relative-entropy-domain dependency Quantum spectral entropy and relative-entropy domains; the finite matrix calculation in this item makes no infinite-dimensional selection.

[F1]

Finite Gibbs free-energy and entropy optimization: for a finite Hermitian Hamiltonian, its Gibbs density minimizes Tr⁡(Hω)−TSphys(ω) over all density matrices, including rank-deficient competitors, with Sphys=kBS and T=(kBβ)−1. Its relative-entropy-domain dependency, recorded as [F2] in that supplier, carries Choice through the declared supplier chain; its own [A1] records energy and units.

[F2]

Partial traces of a finite bipartite state are states: both marginals of a finite bipartite density operator are density operators.

[F3]

Finite partial trace, Schmidt decomposition and finite no-signalling: for every finite bipartite endomorphism R and local endomorphism A, Tr⁡((Tr⁡BR)A)=Tr⁡(R(A⊗IB)), with the corresponding identity for tracing the first factor.

[F4]

Dimensionless entropy, mutual and conditional information: S(ρ)=−Tr⁡(ρlog⁡ρ) and I(A:B)ρ=S(ρA)+S(ρB)−S(ρAB).

[F5]

Finite matrix spectral and state calculus: finite Hermitian matrices have orthonormal eigenbases with real eigenvalues; positive matrices have nonnegative spectral weights, and the finite trace is cyclic.

[F6]

Finite mixing, product entropy, and bipartite subadditivity: for finite-dimensional density matrices, the physical entropy of a tensor product is the sum of the physical entropies of its factors, including rank-deficient factors. Its declared supplier chain carries the same Choice hypothesis in [A1].

Proof

1.1givenF1F2F5algebra

By [F5], H has finitely many real eigenvalues, so e−βH is positive definite and Zβ>0. Thus ρβ is a finite Gibbs density to which [F1] applies. Its marginals ρA:=Tr⁡Bρβ and ρB:=Tr⁡Aρβ are density operators by [F2].

2.1givenF4F5F6step 1.1algebra

Set τ:=ρA⊗ρB. Tensor products of orthonormal eigenbases show that τ is positive semidefinite with eigenvalues piqj, whose sum is (∑ipi)(∑jqj)=1; hence τ is a density operator. By [F6], Sphys(τ)=Sphys(ρA)+Sphys(ρB). Since kB>0 and Sphys=kBS, division by kB gives S(τ)=S(ρA)+S(ρB). Therefore [F4] gives S(τ)−S(ρβ)=I(A:B)ρβ.

3.1A1givenF1step 2.1algebra

Define Fβ(ω):=Tr⁡(Hω)−β−1S(ω) for finite density matrices. Since TSphys=TkBS=β−1S, [F1] applies to this functional and yields Fβ(ρβ)≤Fβ(τ). Rearranging and using step 2.1 gives β−1I(A:B)ρβ≤Tr⁡(H(τ−ρβ)).

4.1step 1.1step 2.1step 3.1F3F5algebra

The coordinate formula in [F3] gives Tr⁡B(ρA⊗ρB)=ρA and Tr⁡A(ρA⊗ρB)=ρB, since each factor has trace one. The defining trace-pairing identities in [F3], with cyclicity from [F5], then show that the expectations of HA⊗IB in τ and ρβ agree, and likewise for IA⊗HB. Thus both local terms cancel in the trace on the right of step 3.1, leaving Tr⁡(H(τ−ρβ))=Tr⁡(H∂(τ−ρβ)).

5.1givenF5step 3.1step 4.1algebra∎

Diagonalize H∂=∑jλjPj by [F5]. For any density matrix ω, the numbers wj:=Tr⁡(Pjω) are nonnegative (each is a sum of nonnegative diagonal expectations of ω on an orthonormal basis of ran⁡Pj) and sum to one. Thus ∣Tr⁡(H∂ω)∣=∣∑jλjwj∣≤∑j∣λj∣wj≤max⁡j∣λj∣=∥H∂∥op. Applying this absolute bound to τ and ρβ gives Tr⁡(H∂(τ−ρβ))≤2∥H∂∥op. Combine this with steps 3.1 and 4.1 and multiply by β>0 to obtain the claimed inequality. Substituting the additional family bound ∥H∂∥op≤C∣∂A∣ yields its stated conditional area scaling.

5 · Examples, experiments and counterexamples

None yet.

Sources