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.
Local spectrum and noncommuting terms
Example
Fix and the two-site open chain . Let with and finite , and set . A classical diagonal replacement here means a density matrix diagonal in the computational basis . The exact finite Gibbs density of this matrix is not diagonal in that basis. Moreover are nontrivial projections with spectrum , satisfy , and do not commute.
Example references
Facts & Assumptions
Given: , , , , and .
These parameter and boundary hypotheses are exactly those in the Example.
Finite Hermitian Potts-clock chain: the inner product is linear in its first argument; , , , , and ; the spectral projections are self-adjoint idempotents summing to . A self-adjoint is positive definite if for every nonzero .
The finite Potts Gibbs matrix is well-defined at every finite inverse temperature: is self-adjoint, , and .
The exponential is a continuous bijection from onto : is bijective, so it is injective and has strictly positive values on real inputs.
Potts local algebra and braid quotient: the operators and are the adjacent local Potts Temperley-Lieb generators at loop weight .
Proof
By [F4], and are the odd and even adjacent local generators in the stated Potts Temperley-Lieb representation. Since , . Therefore Both and are orthogonal projections. Their ranks are in the -dimensional two-site space; for each has spectrum .
The matrix entry from to is because is diagonal in this basis. Hence is not diagonal.
Acting on gives and . These vectors differ for every , so .
Each eigenvalue of has a unit eigenvector . By [F2], is self-adjoint; using the linear-first inner product from [F1], , so . Since , multiplication is injective, and [F3] makes injective; [F3] also gives positive weights. The projections in [F1] sum to the identity, so is positive definite: for nonzero , . Now suppose were diagonal in the computational basis. By [F2], and , so would be diagonal. On the -eigenspace of , acts by ; these scalars are distinct for distinct real , hence the eigenspaces of are exactly those of . Every computational basis vector is an eigenvector of a diagonal , and therefore would be an -eigenvector. This contradicts the nonzero off-diagonal entry in step 1.2, so is not diagonal.
At , the spectral definition in [F1] gives , so [F2] gives , which is diagonal. If , [F1] gives , a diagonal matrix, so its spectral Gibbs density is diagonal as well. These boundary cases show why the nonzero assumptions are required.
Remarks
This is an exact finite-matrix example. It records no observation and establishes no claim about phase transitions, thermalization, or a material realization.