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Schmidt truncation, transfer operators and parent Hamiltonians

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 →

Finite open MPS and injective translation invariant tensor

Definition

Let N≥1 and d1,…,dN,D0,…,DN≥1 be integers. Give each site j the Hilbert space Hj=Cdj with standard inner product ⟨x,y⟩:=∑i=1djxiyi‾, linear in its first argument, and its fixed ordered standard basis (∣ij⟩)ij=1dj. Tensor products use the ordered product basis and the product inner product. For every site and physical index choose a matrix Ajij∈MDj−1×Dj(C), a boundary column ℓ∈CD0, and a boundary column r∈CDN. Here Mm×n(C) is the space of m-by-n complex matrices and A∗ denotes conjugate transpose. The associated finite open matrix product state (MPS) is the vector

∣ψA⟩:=∑i1=1d1⋯∑iN=1dNℓ∗A1i1⋯ANiNr ∣i1⟩⊗⋯⊗∣iN⟩∈H1⊗⋯⊗HN.

The dimensions Dj are the bond dimensions; the virtual indices between adjacent matrices are matrix indices summed in their product. The zero vector is allowed. A nonzero vector is normalized by division by its norm, and normalization is defined only when ⟨ψA,ψA⟩>0. Every vector in H1⊗⋯⊗HN has an exact open MPS representation for some choice of bond dimensions, with D0=DN=1 and ℓ=r=(1); the internal dimensions are chosen in the construction, rather than prescribed arbitrarily.

For a translation invariant tensor, let d,D≥1 and use one family Ai∈MD(C) for every site. Its transfer map is the completely positive linear map

E:MD(C)→MD(C),E(X):=∑i=1dAiXAi∗.

Here complete positivity means that for every integer q≥1, the amplified map id⁡Mq⊗E sends positive semidefinite matrices to positive semidefinite matrices; a matrix is positive semidefinite when it is Hermitian and has nonnegative quadratic form on every vector.

The tensor is injective at length L≥1 when the matrices Ai1⋯AiL, over all i1,…,iL∈{1,…,d}, span the full vector space MD(C). No injectivity or transfer-spectrum condition is assumed by this definition.

For each block length L≥1, define the blocked boundary map

ΓL:MD(C)→(Cd)⊗L,ΓL(X):=∑i1,…,iL=1dtr⁡(XAi1⋯AiL) ∣i1⟩⊗⋯⊗∣iL⟩,

where tr⁡(Y):=∑a=1DYaa. Let PL be the orthogonal projection onto im⁡ΓL, set hL:=I−PL, and, for N≥L, define the open-chain parent Hamiltonian

HN,L:=∑j=1N−L+1I⊗(j−1)⊗hL⊗I⊗(N−j−L+1).

For a positive semidefinite operator K on a nonzero finite-dimensional inner-product space, define its ground energy by

E0(K):=inf⁡{⟨Kv,v⟩:∥v∥=1}.

A ground-state vector of K is a nonzero vector ψ with ⟨Kψ,ψ⟩/∥ψ∥2=E0(K). It is a zero-energy ground-state vector when Kψ=0; positivity then gives E0(K)=0.

Each local term is positive semidefinite with kernel im⁡ΓL, and HN,L annihilates every finite open MPS formed from the same translation invariant tensor. Every nonzero such vector is a zero-energy ground-state vector of HN,L. This definition asserts no uniqueness of that ground state and no spectral-gap bound.

Facts & Assumptions

Given: Finite positive integers N,dj,Dj, the standard ordered bases of Hj=Cdj, site matrices of the displayed dimensions, and boundary vectors of the displayed dimensions.

[F1]

Schmidt decomposition: every vector in a finite-dimensional complex tensor product has a Schmidt expansion with positive singular coefficients and orthonormal factor families; the squared norm is the sum of their squares.

[F2]

Every finite-dimensional real or complex inner product space has an orthonormal basis: every finite-dimensional inner-product space has an orthonormal basis, including the zero-dimensional case with the empty basis.

Proof

1.1givenF1construct

For a nonzero vector, reshape its coefficient array as a matrix with row index i1 and column index (i2,…,iN). By [F1] this gives an exact finite sum of products of a first-site coefficient and a residual coefficient array. Reshape the residual array at site 2 with row index (a1,i2) and remaining sites as column index, apply [F1] again, and store the resulting left coefficient array as the entries of A2i2 with virtual indices (a1,a2). Repeating this finite operation through site N−1 leaves a final array defining ANiN; every SVD factorization is exact, so the original coefficient of each ordered product-basis vector is the displayed matrix product. For N=1 take the coefficients as scalar matrices. For the zero vector take all bond dimensions equal to 1 and set the first site matrices to zero. For the nonzero construction choose each internal Dj as the corresponding positive Schmidt rank; in both constructions use D0=DN=1 with ℓ=r=(1). Thus all finite vectors admit a representation with suitably chosen bond dimensions.

1.2givenalgebra

For any integer q≥1 and positive semidefinite Y∈Mq(C)⊗MD(C), the amplification is (idMq⊗E)(Y)=∑i=1d(Iq⊗Ai)Y(Iq⊗Ai∗). Each summand is positive semidefinite, so every amplification is positive and E is completely positive. The displayed definition also fixes its exact input and output spaces and makes no trace-preserving or unitality assertion.

1.3givenF2constructalgebra

The map ΓL is linear and its image is a finite-dimensional subspace of (Cd)⊗L. By [F2], choose an orthonormal basis u1,…,us of that image and define PLv:=∑a=1s⟨v,ua⟩ua. Orthonormality gives PL∗=PL and PL2=PL, and v−PLv is orthogonal to every ua. Thus hL=I−PL is the orthogonal projection onto the complement, with ⟨hLv,v⟩=∥hLv∥2≥0 and kernel im⁡ΓL.

2.1givenstep 1.3algebra∎

Fix a finite open MPS made from the same tensor and a block of L consecutive sites. For each fixed physical-basis configuration outside that block, contraction of the outside tensors leaves a row α on the left and a column β on the right. With X=βα, direct coordinates give tr⁡(XAi1⋯AiL)=∑u,vβuαv(Ai1⋯AiL)vu=αAi1⋯AiLβ, so that block slice is ΓL(X). The outside basis configurations are orthonormal, so the full vector is a sum of outside basis vectors tensored with block vectors in im⁡ΓL; the corresponding hL therefore annihilates it. Each term in HN,L is positive semidefinite by step 1.3, and their sum annihilates the MPS. If that vector is nonzero, its normalized vector has quadratic form 0, while every unit vector has nonnegative quadratic form; hence E0(HN,L)=0, and it is a zero-energy ground-state vector by the Definition. The zero vector is annihilated but is excluded from ground-state vectors by definition. For example, if all tensor matrices vanish, then ΓL=0, hL=I and HN,L=(N−L+1)I, whose ground energy is positive. No uniqueness or positive-gap conclusion for a nonzero MPS follows from this argument.

Remarks

The diagram records the contracted virtual indices and the uncontracted physical basis legs in the finite open expression above. Horizontal edges carry bond spaces; each downward leg carries the indicated physical basis vector. The coefficient formula and proof are given algebraically above, so the diagram is explanatory only.

Ai11Ai22Ai33`¤rD1D2ji1iji2iji3i
DefinitionDefinition: AI-adaptedProof: AI-generatedprecheck passverified 2026-10-22 (gpt-6.1-sol)Open item page →

Transfer-generated infinite-chain state and correlations

Definition

Fix integers d,D≥1 and a translation invariant tensor A1,…,Ad∈MD(C) with transfer map E(X):=∑iAiXAi∗. A matrix is positive semidefinite when it is Hermitian with nonnegative quadratic form on every vector. Let ρ,ℓ∈MD(C) be positive semidefinite matrices satisfying

tr⁡ρ=1,E(ρ)=ρ,E∗(ℓ)=ℓ,tr⁡(ℓρ)=1,

where E∗(Y):=∑iAi∗YAi, equivalently the trace-pairing adjoint determined by tr⁡(YE(X))=tr⁡(E∗(Y)X). For a contiguous block of k≥1 sites, write I=(i1,…,ik) and J=(j1,…,jk) for physical multi-indices, let eI be its ordered standard product basis, put OI,J:=⟨OeJ,eI⟩ for O∈Mdk(C), and set AI:=Ai1⋯Aik. Define the transfer insertion map and local expectation by

EO(X):=∑I,JOI,JAJX(AI)∗,ωk(O):=tr⁡(ℓEO(ρ)).

For any finite nonempty Λ⊂Z and local observable O∈AΛ, embed O into the interval [min⁡Λ,max⁡Λ] by inserting identities at missing sites and evaluate the same formula. For the empty region set ω∅(cI):=c. These definitions give compatible positive normalized local functionals. Their unique density matrices ρΛ obey Tr⁡Λ′∖Λ(ρΛ′)=ρΛ whenever Λ⊆Λ′. Assume Countable Choice. The transfer-generated infinite-chain state is the unique infinite algebraic state ω on the norm-completed quasi-local spin algebra whose restriction to every finite region is the defined local functional. No infinite tensor-product Hilbert space is selected.

For observables OA∈Mda(C) and OB∈Mdb(C) on two fixed consecutive blocks, with g≥0 identity sites between the blocks, the connected correlation is

ω(OA⊗I⊗g⊗OB)−ω(OA)ω(OB).

Facts & Assumptions

Given: Integers d,D≥1, a translation invariant tensor Ai∈MD(C), positive semidefinite ρ,ℓ, and the four displayed fixed-point, normalization, and pairing equations.

[A1]

The Axiom of Countable Choice (ACω): every sequence of nonempty sets has a choice function. This is the only choice assumption used to complete the compatible local-state family.

[F1]

Finite open MPS and injective translation invariant tensor: E(X)=∑iAiXAi∗ is the completely positive transfer map of the tensor.

[F2]

Local spin matrix algebras and their factor inclusions: on the lattice Z with on-site dimension d, each finite-region algebra is the matrix algebra on its ordered configuration space, and inclusions insert identity factors.

[F3]

Norm-completed quasi-local quantum spin algebra: the quasi-local algebra is the norm completion of the directed union of finite-region observable algebras; it selects no infinite tensor-product Hilbert space.

[F4]

Finite matrix C-star algebra and density-state duality: a positive normalized functional on a finite matrix algebra has a unique trace-one positive density matrix, and its value on O is tr⁡(ρO).

[F5]

Infinite algebraic state and finite-region restrictions: partial trace satisfies Tr⁡Λ(ATr⁡Δ(T))=Tr⁡Λ′((A⊗IΔ)T) for A∈AΛ and T∈AΛ′, where Λ′=Λ∪Δ.

[F6]

Consistent local densities extend to an algebraic state: under Countable Choice, a consistent family of positive trace-one matrices on all finite regions extends uniquely to an infinite algebraic state on the quasi-local algebra.

[F7]

Finite matrix spectral and state calculus: a finite positive semidefinite matrix has an orthonormal eigenbasis with nonnegative eigenvalues, and the finite trace is basis independent.

Proof

1.1givenF1F7algebra

The finite coefficient sum defining EO(X) is complex-linear in both O and X. For O≥0, write its spectral decomposition as O=∑α∣vα⟩⟨vα∣, absorbing the square roots of the nonnegative eigenvalues into vα, and put vα,I=⟨vα,eI⟩. Then OI,J=∑αvα,Ivα,J‾, so with Kα:=∑Jvα,J‾AJ the insertion map satisfies EO(X)=∑αKαXKα∗≥0 whenever X≥0. Diagonalizing ℓ by [F7] and expanding tr⁡(ℓEO(ρ)) as a weighted sum of nonnegative diagonal expectations shows ωk(O)≥0. For O=Idk, OI,J=1 if I=J and 0 otherwise, so the insertion sums over all length-k words and equals Ek. The fixed-point equations and tr⁡(ℓρ)=1 give ωk(I)=tr⁡(ℓEk(ρ))=1. Thus each contiguous-block functional is positive and normalized.

2.1givenF1step 1.1algebra

Tensor-factor order in the definition gives EO⊗P=EO∘EP for consecutive blocks: expanding both sides gives the same ordered product AJOAJPX(AIP)∗(AIO)∗. Inserting an identity site on the right composes with E on the input, which leaves ρ fixed; inserting one on the left composes with E on the output, which leaves the trace pairing with ℓ fixed. Repeating these identities shows that the value is independent of the chosen containing interval and that restrictions to nested finite regions agree. A positive observable remains positive when identity factors are inserted, so the local functionals on arbitrary finite regions are positive and normalized as well. The empty-region convention is consistent because every local functional assigns one to the identity.

3.1givenF2F4F5step 2.1algebra

By [F2]–[F4] each local positive normalized functional has a unique trace-one density ρΛ. For nested regions, step 2.1 gives tr⁡Λ′((O⊗I)ρΛ′)=tr⁡Λ(OρΛ) for every O∈AΛ. Applying the defining partial-trace pairing [F5] shows tr⁡Λ(OTr⁡Λ′∖Λ(ρΛ′))=tr⁡Λ(OρΛ) for all O. Taking O to be each matrix unit makes every matrix entry of the two matrices equal, so the density family is consistent.

4.1givenA1F2F3F6step 3.1∎

Apply [F6] to the consistent finite-region density family from step 3.1. Its hypotheses hold by [A1], [F2], [F3], and the positive trace-one property just proved. It yields a unique infinite algebraic state on the specified quasi-local algebra, with exactly these local restrictions and no chosen global Hilbert-space representation. This establishes that the transfer contractions in the Definition are well defined.

AlgorithmAlgorithm: AI-adaptedProof: Not applicableverified 2026-10-22 (gpt-6.1-sol)Open item page →

Sequential Schmidt truncation

Definition

Input an integer N≥1, nonzero finite-dimensional complex inner-product spaces H1,…,HN with fixed ordered orthonormal bases, a normalized vector ψ∈H1⊗⋯⊗HN with exact coefficients in those bases, and integer rank caps Dk≥1 for 1≤k<N. For N=1 the cap list is empty. The algorithm performs the following exact finite procedure.

  1. Set the current remainder to ψ(0):=ψ, the retained left space before the first cut to S0:=C, and an empty list of site tensors and discarded squared norms.

  2. For each k=1,…,N−1, reshape the current remainder as a matrix whose row index is the retained left index in Sk−1 together with the physical basis index in Hk, and whose column index is the ordered product basis of Hk+1⊗⋯⊗HN. Compute an exact singular value decomposition. Order the positive singular values nonincreasingly. Within each repeated positive singular value, form a deterministic orthonormal basis of its left singular subspace by projecting the row-coordinate standard basis vectors in lexicographic order and applying Gram–Schmidt, discarding zero residuals; obtain the corresponding right singular vector as M∗u/s for singular value s>0. Retain the first mk:=min⁡(Dk,rk) positive singular directions, where rk is the number of positive singular values, and record δk:=∑j=mk+1rksk,j2 from this current unnormalized remainder. Reshape the retained left singular vectors into a site tensor Akik with left index in Sk−1 and right index in Sk:=Cmk; pass the retained singular values times the adjoints of the retained right singular vectors as the next current remainder ψ(k). Since the input has norm one and Dk≥1, every current remainder is nonzero and each mk≥1.

  3. After the last cut, reshape the remaining array as the final site tensor ANiN, use outer bond dimensions D0=DN=1 with scalar boundary columns ℓ=r=(1), and return the contracted unnormalized open MPS ϕ:=∑i1,…,iNℓ∗A1i1⋯ANiNr ∣i1⟩⊗⋯⊗∣iN⟩∈H1⊗⋯⊗HN, the list (δ1,…,δN−1), and the bond dimensions (m1,…,mN−1). Each ∣ij⟩ runs through the fixed ordered orthonormal basis of Hj. For N>1, the compressed remainder ψ(N−1)∈SN−1⊗HN supplies the entries of ANiN; its retained left index is contracted with the preceding site tensors to form ϕ. For N=1, return ϕ=ψ and empty lists with the same scalar boundaries. If ∥ϕ∥2=0, return a norm-zero flag and do not normalize. Otherwise an optional normalized copy is ϕ/∥ϕ∥; the returned ϕ and discarded norms always refer to the unnormalized sweep output.

All coefficients and SVDs are exact mathematical objects; no floating-point threshold is used. The tie rule fixes a choice even when a rank cap cuts through a degenerate singular-value subspace.

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

Finite sweep termination and computable norm error

Statement

Let N≥1, let Hj=Cdj be nonzero finite-dimensional complex inner-product spaces with fixed ordered orthonormal bases, and let ψ∈H1⊗⋯⊗HN have norm one. For integer caps Dk≥1 at cuts 1≤k<N, run the exact sequential Schmidt truncation algorithm on ψ and let ϕ be its unnormalized output and δk the discarded squared singular-value sum measured from the current unnormalized remainder at cut k. The sweep uses exactly N−1 SVDs and

∥ψ−ϕ∥2=∑k=1N−1δk.

In particular, ∥ψ−ϕ∥2≤∑kδk. This is a current-sweep error statement; the δk are not asserted to be the Schmidt tails of the original input at the same cuts. If ψ is represented exactly as an open MPS with bond dimension Dk at cut k, then its Schmidt rank at that cut is at most Dk, and the dimensionless von Neumann entropy in nats of the reduced density ρ1⋯k:=Tr⁡k+1,…,N∣ψ⟩⟨ψ∣ satisfies

S(ρ1⋯k)=−tr⁡(ρ1⋯klog⁡ρ1⋯k)≤log⁡Dk,

with zero eigenvalues contributing 0log⁡0:=0 and the natural logarithm convention.

Facts & Assumptions

Given: The normalized finite tensor, its ordered product basis, integer caps Dk≥1, and the exact truncation procedure of the Statement.

[F1]

Sequential Schmidt truncation: at cut k, the current coefficient matrix is decomposed exactly, its first mk positive singular directions are retained, and δk is the sum of squares of the discarded singular values of that current remainder.

[F2]

Finite open MPS and injective translation invariant tensor: an open MPS coefficient factors across each bond through its finite virtual space CDk; normalization is used only for a nonzero vector.

[F3]

Schmidt decomposition: a finite bipartite coefficient matrix has positive singular coefficients giving the Schmidt decomposition; these coefficients are the positive singular values, and the squared norm is their squared sum.

[F4]

Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition: the number of positive singular values of a finite-dimensional linear map equals its rank; the proof identifies the range as the span of the positive left singular vectors and annihilates the zero-singular-value domain basis.

[F5]

Dimensionless entropy, mutual and conditional information: for a finite density matrix ρ, S(ρ)=−∑j:λj>0λjlog⁡λj in nats, with 0log⁡0:=0; the partial trace contracts the stated tensor factors.

[F6]

Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm: log⁡ is continuous and increasing on (0,∞) and log⁡1=0, with log⁡(xy)=log⁡x+log⁡y for positive x,y.

[F8]

The mean value theorem, as the case g(x)=x of Cauchy's: for f continuous on [a,b] with a<b and differentiable on (a,b) there is c∈(a,b) with f(b)−f(a)=f′(c)(b−a): a function continuous on [a,b] and differentiable on (a,b) has c∈(a,b) with f(b)−f(a)=f′(c)(b−a).

Proof

1.1givenF1F2F3constructalgebra

Since ∥ψ∥=1 and every cap Dk≥1, each current remainder has a positive singular value and the algorithm retains at least one; hence all subsequent remainders are nonzero and all retained bond dimensions are positive. The loop makes one SVD for each k=1,…,N−1, exactly N−1 in total. Put H≤k:=H1⊗⋯⊗Hk and Rk:=Hk+1⊗⋯⊗HN, with H≤0=C. Let Uk:Sk→Sk−1⊗Hk have the retained left singular columns, so Uk∗Uk=ISk and ψ(k)=(Uk∗⊗IRk)ψ(k−1). Define W0=IC and Wk=(Wk−1⊗IHk)Uk:Sk→H≤k. Each Wk is an isometry by induction. Under the canonical C⊗H≅H identification, set x0=ψ, P0=I, and, for k≥1, xk=(Wk⊗IRk)ψ(k) and Pk=(WkWk∗)⊗IRk on the full chain. The range of Wk lies in ran⁡Wk−1⊗Hk, so ran⁡Pk⊆ran⁡Pk−1 and PkPk−1=Pk−1Pk=Pk. The remainder recurrence gives xk=Pkxk−1=Pkψ inductively. Expanding WN−1 in the stored site tensors shows that xN−1 is exactly their contraction with the final remainder, hence is the returned unnormalized ϕ in algorithm instruction 3. For N=1, x0=ϕ=ψ.

1.2givenF2F3F4F5

Across cut k, the coefficient matrix of an exact MPS factors as LkRk by summing over its Dk-dimensional virtual index. Its matrix rank is therefore at most Dk. By [F3]–[F4], the Schmidt rank is the number of positive singular values, so it is at most Dk. Writing the Schmidt expansion as ψ=∑j=1qsjuj⊗vj gives q≤Dk, pj:=sj2>0, and ∑jpj=1. Expanding the partial trace in [F5] gives ρ1⋯k=∑jpj∣uj⟩⟨uj∣, so these pj are exactly its positive eigenvalues.

2.1step 1.1algebra

Put εk:=xk−1−xk=(Pk−1−Pk)ψ in the full chain. This vector lies in ran⁡Pk−1 and is orthogonal to ran⁡Pk. For j>k, nesting gives εj∈ran⁡Pj−1⊆ran⁡Pk, so ⟨εk,εj⟩=0. Thus the embedded discarded components from different cuts are pairwise orthogonal.

3.1givenF1F3step 1.1step 2.1algebra

At cut k, εk=(Wk−1⊗IHk⊗Rk)(ψ(k−1)−(Uk⊗IRk)ψ(k)). The map outside the parentheses is an isometry by step 1.1, and the vector in parentheses is the discarded Schmidt part of the current compressed remainder. Hence [F3] and [F1] give ∥εk∥2=δk. Since x0=ψ and xN−1=ϕ, telescoping gives ψ−ϕ=∑k=1N−1εk; orthogonality from step 2.1 yields ∥ψ−ϕ∥2=∑k∥εk∥2=∑kδk. For N=1 both sides are zero, so the equality also covers the empty sweep.

4.1givenF5F6F7F8step 1.2algebra∎

For every x>1, apply [F8] to log⁡ on [1,x]; [F6]–[F7] give log⁡x=(x−1)/c≤x−1 for some c∈(1,x). For 0<x<1, apply [F8] on [x,1] to get −log⁡x=(1−x)/c for some c∈(x,1), hence log⁡x≤x−1; equality holds at x=1. Apply this inequality to x=(qpj)−1, multiply by pj>0, and sum: ∑jpjlog⁡((qpj)−1)≤∑j(1/q−pj)=0. The logarithm product law [F6] then gives log⁡q−S(ρ1⋯k)=∑jpjlog⁡(qpj)≥0. Since q≤Dk and log⁡ is increasing, S(ρ1⋯k)≤log⁡q≤log⁡Dk. The zero-eigenvalue convention in [F5] includes rank-deficient reductions.

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

Certified transfer gap gives exponential MPS clustering

Statement

Assume Countable Choice. Fix integers d,D≥1, matrices Ai∈MD(C), and the transfer map E(X)=∑i=1dAiXAi∗. Let ρ,ℓ∈MD(C) be Hermitian and satisfy ⟨ρv,v⟩>0 and ⟨ℓv,v⟩>0 for every nonzero v∈CD, together with tr⁡ρ=1, E(ρ)=ρ, E∗(ℓ)=ℓ, and tr⁡(ℓρ)=1. Assume that 1 is a root of multiplicity one of the characteristic polynomial of E, and define

q:=max⁡({∣λ∣:λ∈σC(E), λ≠1}∪{0})<1.

Let a,b≥1 and fix local observables OA∈Mda(C) and OB∈Mdb(C). For each integer g≥0, place OA on sites 1,…,a, place OB on sites a+g+1,…,a+g+b, and place identities on the g intervening sites. The transfer-generated infinite-chain state determined by Ai,ρ,ℓ has connected correlation

Cg:=tr⁡(ℓ EOAEgEOB(ρ))−tr⁡(ℓ EOA(ρ))tr⁡(ℓ EOB(ρ)).

For every real r with q<r<1, there is a finite constant C=C(A,ρ,ℓ,OA,OB,r)≥0, independent of g, such that ∣Cg∣≤Crg for every g≥0. The multiplicity-one hypothesis is algebraic simplicity; no diagonalizability assumption is made.

Statement references

Facts & Assumptions

Given: Countable Choice, the finite tensor, positive definite fixed matrices with the stated normalization, the algebraic simplicity of eigenvalue 1, and the strict spectral bound q<1.

[A1]

The Axiom of Countable Choice (ACω): every sequence of nonempty sets has a choice function; this is the exact set-theoretic hypothesis carried by the transfer-state construction.

[F1]

Finite open MPS and injective translation invariant tensor: E(X)=∑iAiXAi∗ is the completely positive transfer map associated with the translation invariant tensor.

[F2]

Transfer-generated infinite-chain state and correlations: E∗(Y)=∑iAi∗YAi is the trace-pairing adjoint of E, and for a block observable O, EO(X)=∑I,JOI,JAJX(AI)∗ with local expectation tr⁡(ℓEO(ρ)); consecutive block insertions compose in physical order.

[F3]

Eigenvalues, eigenvectors, eigenspaces Eλ(T)=ker⁡(T−λI), and the spectrum σF(T) of an endomorphism: σC(E) is the set of complex eigenvalues of the finite-dimensional complex-linear endomorphism E.

[F4]

Fundamental theorem of algebra: every nonconstant complex polynomial has a complex root: every nonconstant complex polynomial has at least one complex root.

[F5]

Jordan canonical form from the elementary divisors of VT: when the characteristic polynomial splits over the field, the finite-dimensional endomorphism has a Jordan basis with one block Js(λ)=λI+N for each elementary divisor, where Ns=0.

[F6]

Finite matrix spectral and state calculus: a finite Hermitian matrix has an orthonormal eigenbasis with real eigenvalues, and the finite trace is basis independent and cyclic.

[F7]

The basis-independent characteristic polynomial χT of an endomorphism of a finite-dimensional space, including χT=1 in dimension zero: for an endomorphism T:V→V of a finite-dimensional complex vector space, χT is the characteristic polynomial of any matrix of T in an ordered basis and is independent of that basis. If dim⁡CV=m≥1, then χT(x)=det⁡(xIm−[T]) has degree m and leading coefficient 1: in the determinant expansion the only degree-m term is xm.

[F8]

Algebraic multiplicity as the exponent of x−λ in χT, and geometric multiplicity as dim⁡Eλ(T): the algebraic multiplicity of an eigenvalue λ is the exponent of x−λ in χT, and its geometric multiplicity is dim⁡Eλ(T). Thus the Statement's multiplicity-one hypothesis is algebraic multiplicity one for E on the finite-dimensional complex vector space MD(C).

[F9]

The geometric multiplicity of an eigenvalue does not exceed its algebraic multiplicity: for a finite-dimensional endomorphism over a field and any eigenvalue λ, dim⁡Eλ(T)≤mult⁡χT(λ).

Proof

1.1givenF1F2F7F8F9algebra

Define the linear functional φ(X):=tr⁡(ℓX) and the rank-one map P(X):=ρ φ(X). Since tr⁡ρ=1, ρ≠0; the pairing normalization φ(ρ)=1 gives P2=P. The fixed-point equations give EP=P and PE=P, since φ(E(X))=tr⁡(E∗(ℓ)X)=φ(X). Hence MD(C)=Cρ⊕ker⁡φ is an E-invariant direct sum, E is the identity on Cρ, and P is zero on ker⁡φ. If the restriction of E to ker⁡φ had eigenvalue 1, it would have a nonzero eigenvector X there. Since φ(X)=0 and φ(ρ)=1, X and ρ would be linearly independent eigenvectors of E for 1, so dim⁡E1(E)≥2. But [F7]–[F8] identify the Statement's simple-root hypothesis with algebraic multiplicity one, and [F9] bounds this geometric multiplicity by the algebraic multiplicity. This contradiction shows that the restriction has no eigenvalue 1. Therefore Eg−P is zero on Cρ and equals (E∣ker⁡φ)g on the invariant complement.

2.1F3F4F5F7step 1.1algebra

Use ∥X∥op:=sup⁡∥v∥=1∥Xv∥ on MD(C) and the induced norm ∥Φ∥:=sup⁡∥X∥op=1∥Φ(X)∥op on its linear maps. If ker⁡φ={0}, then E=P=I and Eg−P=0 for every g≥0; for every allowed r, take Kr=0. Otherwise the restriction is a finite-dimensional complex endomorphism of positive dimension m. Its characteristic polynomial is monic of degree m by [F7]. By [F4] it has a root μ. Write this characteristic polynomial as p(x)=∑k=0mckxk. Since p(μ)=0, the identities xk−μk=(x−μ)∑t=0k−1xk−1−tμt give p(x)=(x−μ)h(x) with deg⁡h=m−1. Induction on degree therefore shows that this characteristic polynomial splits into linear factors over C. Now [F5] gives a similarity to finitely many Jordan blocks Js(λ)=λI+N with Ns=0. Every such λ belongs to σC(E)∖{1}, since an eigenvector of the restriction is also an eigenvector of E and step 1.1 excludes 1 from the restricted spectrum; hence ∣λ∣≤q. If q=0, all Jordan powers of the restriction vanish for sufficiently large g, so Eg−P=0 for those g. Since every allowed r>q=0 is positive, the finitely many remaining quantities ∥Eg−P∥r−g have a finite maximum; take this maximum as Kr to obtain the required bound for all g. If q>0, fix r>q. For a block with λ≠0 and g≥s, the binomial expansion gives Js(λ)g=∑j=0s−1(gj)λg−jNj. For every fixed j, gj(∣λ∣/r)g is bounded because the ratio of consecutive terms tends to ∣λ∣/r<1; hence each matrix entry is bounded by a constant times rg. Blocks with λ=0 vanish for g≥s, and all finitely many smaller g are absorbed into the constant. In the maximum-coordinate norm of the Jordan basis, the norm of the block-diagonal power is bounded by a constant times rg, since there are finitely many blocks and each has finitely many entries; the zero action of Eg−P on Cρ adds only a one-dimensional zero block. The fixed change-of-basis map and its inverse have finite induced norms, so submultiplicativity transfers this bound to the stated superoperator norm: ∥Eg−P∥≤Krrg for a finite Kr and every g≥0.

2.2givenA1F1F2step 1.1algebra

By [F1]–[F2], the transfer contraction for the two blocks with a gap of g identities is tr⁡(ℓEOAEgEOB(ρ)). The product of the separate expectations equals tr⁡(ℓEOAPEOB(ρ)), since P(X)=ρtr⁡(ℓX). Subtracting gives Cg=tr⁡(ℓEOA(Eg−P)EOB(ρ)).

3.1givenF6step 2.1step 2.2algebra∎

Diagonalize ℓ≥0 using [F6] as ℓ=∑jtj∣uj⟩⟨uj∣ with tj>0. For every matrix X, ∣tr⁡(ℓX)∣=∣∑jtj⟨Xuj,uj⟩∣≤tr⁡(ℓ)∥X∥op. The block insertion maps are finite sums of matrix multiplications, so their induced norms in step 2.1 are finite; submultiplicativity gives ∣Cg∣≤tr⁡(ℓ) ∥EOA∥ Kr ∥EOB(ρ)∥op rg. All factors before rg are finite and independent of g, so their product is an admissible C. This proves the claimed exponential clustering bound, including Jordan blocks and the case q=0.

5 · Examples, experiments and counterexamples

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