Alphabeta Math
LemmaStatement: AI-adaptedProof: 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.

Finite matrix spectral and state calculus

Statement

Use the following conventions on a complex inner-product space H of dimension D≥1, with inner product linear in its first argument. For an endomorphism A, define Tr⁡A:=∑j=1D⟨Aej,ej⟩ in an orthonormal basis; step 3.1 proves basis independence. For any Hermitian M, its positive spectral subspace (called its positive support here) is SM:=⨁λ>0ker⁡(M−λI); when M≥0 this equals (ker⁡M)⊥. Its support logarithm is log⁡(M∣SM):=∑λ>0(log⁡λ)Pλ∣SM, where Pλ is the orthogonal projection onto that eigenspace. No value log⁡0 is assigned; for M=0 this is the unique endomorphism of the zero space. For the set D(H) of positive semidefinite Hermitian endomorphisms with trace one, a point ρ is extreme if every equality ρ=tσ+(1−t)τ with σ,τ∈D(H) and 0<t<1 implies σ=τ=ρ.

Let H be a complex inner-product space of finite dimension D≥1, with inner product linear in its first argument. Every Hermitian endomorphism M has an orthonormal eigenbasis with real eigenvalues. For a real function f on its finite spectrum, define f(M) by its spectral projections; this definition is independent of the choice of eigenbasis. In particular positive square roots, exponentials, and logarithms on positive supports are well defined.

For a density matrix ρ on H, meaning a Hermitian positive semidefinite operator with Tr⁡ρ=1, Tr⁡(ρA)=∑jpj⟨Auj,uj⟩ for bounded A, where ρ=∑jpj∣uj⟩⟨uj∣. The weighted form (X,Y)ρ=Tr⁡(ρX∗Y) satisfies Cauchy–Schwarz, ∣(X,Y)ρ∣2≤(X,X)ρ(Y,Y)ρ. Finally, Tr⁡(ρ2)≤1, with equality exactly for rank-one densities, and those are exactly the extreme points of the finite density-matrix set.

Facts & Assumptions

Given: A complex inner-product space H of finite dimension D≥1, a Hermitian M, and an ordered orthonormal basis, whose coordinates identify H isometrically with R2D.

[F1]

For n≥1 every bounded sequence in Rn has a convergent subsequence: every bounded sequence in Rn, n≥1, has a convergent subsequence.

[F2]

Real and complex inner-product spaces and their induced length: the inner product is linear in the first argument, conjugate-linear in the second, and positive definite.

[F4]

Rational numbers are dense in the reals and countable; finite powers of a countable set are countable, and every nonempty countable set is a surjective image of the natural numbers (The rationals embed densely in the reals, Q is countably infinite, Every finite power of an at most countable set is at most countable, A nonempty set is at most countable iff it is a surjective image of N).

[F5]

The Cauchy-sequence reals have the least-upper-bound property: every nonempty bounded-above subset of the Cauchy-sequence real field used in [F4] has a real supremum.

Proof

1.1givenF1F2F3F4F5construct

Choose the ordered orthonormal basis of [F3]. In its coordinates q(u)=⟨Mu,u⟩ is a real continuous quadratic polynomial, bounded on the unit sphere by the finite sum of the absolute matrix entries. By [F4], enumerate all rational-coordinate vectors yk∈(Q+iQ)D, and put vk=yk/∥yk∥ when yk≠0 and vk=e1 otherwise. For any unit x and ε>0, coordinatewise rational approximation gives ∥y−x∥<min⁡(ε/2,1/2) for such a y. Then y≠0 and the reverse triangle inequality gives ∥y/∥y∥−x∥≤∣1−∥y∥∣+∥y−x∥≤2∥y−x∥<ε. Thus the vk are dense on the sphere; they are normalizations of rational-coordinate vectors, not necessarily rational-coordinate vectors themselves. The set S:={q(u):∥u∥=1} is nonempty because e1 is a unit vector and is bounded above as just shown; [F5] gives m:=sup⁡S∈R. For each n≥1 take the least index k(n) with q(vk(n))>m−1/n, which exists by continuity and density. This deterministic rule uses no countable choice. By [F1] the bounded sequence vk(n) has a convergent subsequence; its limit has norm one and attains m.

2.1step 1.1F2algebra

Let u be a maximizing unit vector and w⊥u. Differentiating q((u+tw)/∥u+tw∥) at t=0 for real t gives 2Re⁡⟨Mw,u⟩=0. Replacing w by iw shows that the imaginary part also vanishes. Hermiticity gives ⟨Mu,w⟩=0 for every w⊥u, so Mu=q(u)u with real eigenvalue. Moreover u⊥ is invariant, since ⟨Mw,u⟩=⟨w,Mu⟩=0. Induction on D, including the one-dimensional case, gives an orthonormal eigenbasis with real eigenvalues.

3.1step 2.1algebra

For M=∑λλPλ, define f(M)=∑λf(λ)Pλ. The full eigenspaces and their orthogonal projections are intrinsic, so this definition is independent of the basis within each eigenspace. It supplies positive square roots for nonnegative matrices, exponentials for every Hermitian matrix, and the stated logarithm on the positive spectral subspace SM for every Hermitian M: the restriction has exactly the strictly positive eigenvalues. If SM={0}, the empty spectral sum is the unique endomorphism of that zero space. Finite coordinate sums give Tr⁡(AB)=∑i,jAijBji=Tr⁡(BA), hence invariance under unitary changes of orthonormal basis. Diagonalizing ρ gives Tr⁡(ρA)=∑jpj⟨Auj,uj⟩ for every complex matrix A, and in particular Tr⁡(ρB∗B)=∑jpj∥Buj∥2≥0.

4.1step 3.1algebra

Put a=Tr⁡(ρX∗Y), b=Tr⁡(ρY∗Y) and c=Tr⁡(ρX∗X). Step 3.1 gives c+za+z‾ a‾+∣z∣2b≥0 for all complex z. If b>0, take z=−a‾/b to get ∣a∣2≤bc. If b=0, varying z with arbitrarily large magnitude forces a=0, giving the same inequality. This proves the stated weighted Cauchy–Schwarz formula (whose form is conjugate-linear in X).

5.1step 3.1step 4.1algebra∎

The density eigenvalues satisfy pj≥0 and ∑jpj=1, so Tr⁡(ρ2)=∑jpj2≤1, with equality exactly when one eigenvalue is one and all others vanish. If a rank-one density ρ=∣u⟩⟨u∣ is tσ+(1−t)τ with 0<t<1, then every v⊥u has 0=t⟨σv,v⟩+(1−t)⟨τv,v⟩. Positivity and the square roots from step 3.1 give σv=τv=0. Self-adjointness puts their ranges in Cu, and trace one gives σ=τ=ρ. Conversely, two positive eigenvalues allow a sufficiently small opposite transfer of weight, yielding distinct densities ρ+,ρ− with midpoint ρ. Thus precisely the rank-one densities are extreme.

Depends on

Dependency tree · 2 levels

Sources