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Exact finite algebra entropy identities and holography boundaries
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Binary Operations, Monoids, Groups and Subgroups
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Cyclic Groups and Direct Products
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Finite modular energy and entropy balance
- Foundations of the Real Numbers for Analysis
- Free Modules, Exact Sequences, Projective and Injective Modules
- Fundamental Trigonometric Identities
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Hilbert Space Geometry and Riesz Representation
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Metric Spaces
- Modules, Submodules, Quotient Modules and the Isomorphism Theorems
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal density states and spectral moments
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Properties of the Integral and the Working FTC
- Pure and Mixed States, Ensembles, and Schmidt Decomposition
- Quantum Information Measures
- Registers, Tensor Products, and Partial Trace
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Simple Field Extensions and the Construction of the Complex Numbers
- Sine, Cosine, and the Definition of Pi
- Splitting Fields
- Subspaces, Products, and Quotients
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- Tensor Products of Modules
- The Complex Exponential and Euler's Formula
- The Derivative and the Mean Value Theorems
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Exponential Function
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Galois Correspondence
- The Logarithm and General Powers
- The Riemann Integral: Definition and Integrability
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The Topology of Euclidean Space
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Trust region and cubic regularization methods
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
3 · Logical flowchart
4 · Definitions, postulates, theorems and proofs
Direct-sum sector code entropy model
Definition
Let be a finite nonempty index set. For each , let and be nonzero finite-dimensional complex inner-product spaces, and give
the orthogonal direct-sum inner product: distinct summands are orthogonal and the inner product within a summand is the Hilbert tensor-product inner product. On elementary tensors that inner product is . A map acts by , and a direct-sum map acts blockwise with zero off-diagonal blocks.
A complex-linear endomorphism is a linear map from a vector space to itself. A density matrix on a finite-dimensional complex inner-product space is a self-adjoint endomorphism with nonnegative eigenvalues and trace one; it is faithful when all eigenvalues are strictly positive. These are the conventions used for the finite density matrices in the earlier modular-balance result. The summand indexed by is called the sector. For each , fix a faithful density matrix on . Let satisfy and , and let be density matrices on . Define the encoded endomorphism
When this encoded endomorphism is a density matrix, call it the encoded reduced state. The logical data are and ; the ancillary matrices are held fixed. For a second strictly positive probability vector and faithful density matrices on the same , write for the encoded reference endomorphism.
This is a finite-dimensional algebraic model. The definition makes no gravitational interpretation and does not identify a matrix entropy term with a geometric area.
Definition references
Finite relative entropy equals modular energy minus entropy
Ordered tensor registers, their inner product, and wire permutations
Tensor products of finitely many linear maps
Linear map between vector spaces over the same field
The basis-independent trace of an endomorphism of a finite-dimensional vector space
Daniel Harlow, The Ryu-Takayanagi Formula from Quantum Error Correction, Appendix A.7.2–A.7.3
John Watrous, The Theory of Quantum Information, §§5.2.1–5.2.2
Sector entropy and relative entropy identities
Statement
Let be a finite nonempty set. For each , let be nonzero finite-dimensional complex inner-product spaces. A density matrix is a self-adjoint endomorphism with nonnegative eigenvalues and trace one; it is faithful when all its eigenvalues are strictly positive. Let with , let be a density matrix on , let be a faithful density matrix on , and let be a fixed faithful density matrix on . Form the finite orthogonal direct sums
For a density matrix , set , with , so entropy is in nats. For density matrices with faithful reference , set using spectral logarithms on positive eigenvalues. Also define and . Then and are density matrices, is faithful, and
These are exact finite-dimensional matrix identities; no literal black-hole area, universal Ryu–Takayanagi formula, continuum limit, or gravitational entropy claim follows.
Statement references
Direct-sum sector code entropy model
Finite relative entropy equals modular energy minus entropy
Finite matrix spectral and state calculus
The basis-independent trace of an endomorphism of a finite-dimensional vector space
Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm
Daniel Harlow, The Ryu-Takayanagi Formula from Quantum Error Correction, Appendix A.7.2–A.7.3
John Watrous, The Theory of Quantum Information, §§5.2.1–5.2.2
Facts & Assumptions
Given: The finite sector data, encoded endomorphisms, entropy convention, and classical relative entropy convention in the Statement.
Finite relative entropy equals modular energy minus entropy: for finite-dimensional density matrices with faithful reference , , with natural logarithms and .
Finite matrix spectral and state calculus: every finite-dimensional Hermitian endomorphism has an orthonormal eigenbasis with real eigenvalues; for a nonnegative endomorphism the eigenvalues are nonnegative, and spectral functions on its positive spectrum are well defined.
The basis-independent trace of an endomorphism of a finite-dimensional vector space: trace is the finite sum of diagonal matrix entries in any basis and is independent of the basis.
Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm: for positive , , , and .
Direct-sum sector code entropy model: and are block-diagonal endomorphisms with blocks and on the orthogonal spaces ; tensor maps act on elementary tensors factorwise.
Proof
Choose orthonormal eigenbases for each , , and using [F2]. Write their eigenvalues as , , and , respectively. In each block, the tensor products of the factor eigenvectors form an orthonormal basis and have eigenvalues for and for . Thus every block is positive semidefinite, the finite orthogonal sum is positive semidefinite, and its trace is ; the same calculation with proves . Since are all strictly positive, every eigenvalue of is positive, so the reference is faithful.
The eigenvalues of are exactly over its orthogonal block eigenbases. Terms with contribute by the entropy convention; all other terms satisfy by [F4]. Substituting in and summing the finite products, using , gives , which is the asserted entropy identity.
Write for the chosen eigenbasis of and for that of , and put . By [F4], the block- eigenvalues of on are ; the diagonal entries of in that basis are . Its block trace against is therefore . Factoring the finite sums and using , , , and , summing over gives .
By [F1] and faithfulness from step 1.1, , since by the spectral entropy definition. Applying the same identity [F1] sectorwise gives . Subtract the entropy expression in step 2.1 from the trace expression in step 2.2; the ancillary terms cancel and . This is the claimed relative-entropy identity.
5 · Examples, experiments and counterexamples
None yet.