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Finite modular energy and entropy balance
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
- Bounded Linear Operators and Quotient Spaces
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Complex Norms and Realification
- 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
- 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
Finite matrix entropy derivative at a faithful state
Statement
Let be a nonzero finite-dimensional complex inner-product space with a fixed orthonormal basis, let be an open interval, and let . Suppose has real and imaginary matrix entries in the fixed basis differentiable at and is self-adjoint with strictly positive eigenvalues on an open neighborhood of . Let be the finite-dimensional trace, and let be the operator norm. For , define by applying the natural logarithm to the eigenvalues of in its spectral decomposition and set by . Then is differentiable at and
where is the identity endomorphism of and is obtained by differentiating the matrix entries in the fixed basis.
Statement references
Finite matrix spectral and state calculus
The basis-independent trace of an endomorphism of a finite-dimensional vector space
The operator norm as the least bound and as the unit-sphere or unit-ball supremum
The inner-product norm is definite, homogeneous, and satisfies the triangle inequality
Cauchy–Schwarz: , with equality exactly for linearly dependent vectors
The natural logarithm as the inverse of the exponential function
Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm
The natural logarithm has derivative 1/x and equals the integral from 1 to x of 1/t
For , , and for the series diverges
The derivative of at a point that is a limit point of , and differentiability on a set
John Watrous, The Theory of Quantum Information, §5.2.1
Mark M. Wilde, From Classical to Quantum Shannon Theory, §11.1
Facts & Assumptions
Given: A finite-dimensional complex inner-product space , an open interval with , and an entrywise differentiable path that is self-adjoint with positive eigenvalues near .
Finite matrix spectral and state calculus: every Hermitian endomorphism has an orthonormal eigenbasis with real eigenvalues; applying any real function to its finite spectrum defines a basis-independent endomorphism, including the logarithm on positive eigenvalues.
The basis-independent trace of an endomorphism of a finite-dimensional vector space: in any ordered basis, the trace of an endomorphism is the sum of the diagonal entries of its matrix, and this value is basis-independent.
The natural logarithm as the inverse of the exponential function: for , is the unique real with ; in particular .
The natural logarithm has derivative 1/x and equals the integral from 1 to x of 1/t: for , is differentiable and .
For , , and for the series diverges: for real with , .
Inside its radius a real power series may be differentiated term by term, and the differentiated series has the same radius: a real power series can be differentiated term by term at every point strictly inside its radius of convergence.
A function continuous on an interval whose derivative vanishes at every interior point of is constant on ; consequently two such functions with the same derivative differ by a constant: a continuous function on an interval with zero derivative at every interior point is constant on that interval.
The derivative of at a point that is a limit point of , and differentiability on a set: differentiability at means the difference quotient has a finite limit, hence entrywise; at any real-valued differentiability point the difference quotient is locally bounded, so the function is continuous there.
The operator norm as the least bound and as the unit-sphere or unit-ball supremum: is the supremum of over unit vectors, and therefore for every vector by rescaling.
The inner-product norm is definite, homogeneous, and satisfies the triangle inequality: the norm induced by an inner product satisfies for vectors .
Proof
For , the power series converges absolutely because it is termwise bounded in absolute value by the geometric series in [F5]. Its radius is therefore at least , and [F6] gives . The difference quotient for in [F4], with increment , gives . Both functions are differentiable on and hence continuous there by [F8]: at a differentiability point, the difference quotient is bounded near the point, so multiplying it by the increment shows that the function increment tends to zero. Since both functions vanish at by [F3], [F7] applied to their difference yields for every .
Write . For any endomorphism , let in the fixed orthonormal basis. For , Cauchy–Schwarz [F12] applied in these coordinates to and gives for each , hence . Thus is bounded, so the operator norm [F9] is defined and finite, with . For any endomorphisms , [F9] then gives for every vector . Taking the supremum over unit proves .
Put and let and be its largest and smallest eigenvalues. They satisfy by [F1] and the hypothesis. Set , , and, for , . The eigenvalues of lie in . In an orthonormal eigenbasis of , writing its eigenvalues as , [F9] gives : for , , with equality on an eigenvector for a largest . Hence by [F1]. Choose with . By [F8], every entry of tends to zero as . Since there are finitely many entries, , and the matrix bound in step 1.2 gives , so choose a neighborhood of where . For every unit vector , the inner-product norm triangle inequality [F11] and [F9] give for ; taking the supremum proves . Since is self-adjoint, each eigenvalue satisfies . For , the scalar identity in step 1.1 applied to these eigenvalues, the product law [F10], and spectral calculus [F1] give , with convergence in operator norm because repeated application of step 1.2 gives and .
For sufficiently near , put , , and . Then . The supremum definition gives , so as ; shrink the neighborhood so that . Distributivity writes , where contains the terms with exactly one , and contains all terms with at least two factors. The submultiplicative bound in step 1.2 gives . For , each word with exactly factors has norm at most , and there are such words, so . The middle bound follows from for .
Substituting the series of step 2.1 into , and using step 3.1, yields where for a constant independent of sufficiently small . Indeed, the omitted terms are , , and ; for each orthonormal basis vector , the th coordinate of is and [F12] gives its magnitude at most by [F9], since , so [F2] gives with , and the bounds in step 3.1 make their series after division by bounded by constants times , , and , respectively. Each geometric derivative sum is finite by [F5]–[F6], and step 3.1 gives .
The linear matrix-log term read from the series in steps 2.1 and 3.1 is Its series converges in operator norm because the th summand has norm at most . The remainder in the logarithm series is bounded by , so is the full first-order term of . In an orthonormal eigenbasis of , write and . Then If , the inner sum is , so its coefficient is where step 1.1 evaluates the series and [F10] gives , while . If , the coefficient is by [F4] and [F5]. Defining for and , we have : this is the divided-difference derivative of the matrix logarithm.
In a fixed basis, [F2] gives , so trace is cyclic. Since commutes with and , cycling each term gives . Thus the terms in step 4.1 sum to . In the eigenbasis used in step 4.2, , so . The remaining linear terms in step 4.1 are by the spectral series in step 2.1. Therefore .
By [F8], for the path increment is entrywise. The finite-coordinate bound in step 2.1 gives . Dividing the expansion in step 5.1 by makes its remainder tend to and its linear term tend to . This is the stated derivative.
Remarks
The matrix logarithm derivative is proved here for finite-dimensional faithful matrices. It makes no assertion about singular reference states or infinite-dimensional operator algebras.
Finite relative entropy equals modular energy minus entropy
Statement
Let be a nonzero finite-dimensional complex inner-product space. Let be self-adjoint linear maps with nonnegative eigenvalues and trace one, and suppose every eigenvalue of is strictly positive. Define by spectral calculus, and for any self-adjoint with nonnegative eigenvalues and trace one define , with the zero-eigenvalue term and natural logarithms, so the entropy is in nats. Define the modular-energy difference relative to by . Then
For any nonzero finite-dimensional complex inner-product spaces with , partial tracing both states over cannot increase relative entropy:
The identity and inequality are finite-dimensional mathematical statements; no Hamiltonian, physical energy, physical units, continuum limit, or gravitational entropy is assumed.
Statement references
Finite-dimensional quantum relative entropy
Finite-dimensional von Neumann entropy
The basis-independent trace of an endomorphism of a finite-dimensional vector space
Partial trace over a finite tensor factor
Partial-trace data processing for finite quantum relative entropy
John Watrous, The Theory of Quantum Information, §§5.2.1–5.2.3
Mark M. Wilde, From Classical to Quantum Shannon Theory, §§11.1 and 11.8
Facts & Assumptions
Given: A nonzero finite-dimensional complex inner-product space and self-adjoint with nonnegative eigenvalues and trace one, with having strictly positive eigenvalues.
Finite-dimensional quantum relative entropy: for nonnegative endomorphisms , if then using spectral logarithms on positive supports; otherwise .
Finite-dimensional von Neumann entropy defines the von Neumann entropy for a finite-dimensional density endomorphism using the natural logarithm. The function in the Statement is the same finite spectral sum, written with lowercase and the convention .
The basis-independent trace of an endomorphism of a finite-dimensional vector space: is the basis-independent trace of a finite-dimensional endomorphism, given by the sum of diagonal matrix entries in any basis; it is linear by this finite-sum definition.
Partial trace over a finite tensor factor: for nonnegative on , the partial trace is the endomorphism of whose product-basis entries are .
Partial-trace data processing for finite quantum relative entropy: for nonnegative on a finite tensor product , .
Proof
Since has only positive eigenvalues, and hence . By [F1], is finite and equals .
By [F2] and , . Linearity of trace [F3] then gives by step 1.1. This proves the finite modular-energy identity.
With the finite identity established in step 2.1, apply [F5] for the separate partial-trace claim to the nonnegative endomorphisms on , with the partial trace defined in [F4], to obtain the displayed inequality.
Finite-dimensional entropy first variation at a faithful state
Statement
Let be a nonzero finite-dimensional complex inner-product space and let be an open interval containing . Let be a path whose real and imaginary matrix entries are continuously differentiable on , such that each is self-adjoint with nonnegative eigenvalues and trace one. Suppose and every eigenvalue of is strictly positive. Define by spectral calculus and let with the finite spectral convention . Then
If, in addition, the real and imaginary matrix entries of are twice continuously differentiable on , then the finite relative entropy has zero first derivative at . These are finite-dimensional mathematical statements and make no continuum or gravitational claim.
Statement references
Finite matrix entropy derivative at a faithful state
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
The operator norm as the least bound and as the unit-sphere or unit-ball supremum
Cauchy–Schwarz: , with equality exactly for linearly dependent vectors
John Watrous, The Theory of Quantum Information, §5.2.1
Mark M. Wilde, From Classical to Quantum Shannon Theory, §§11.1 and 11.8
Facts & Assumptions
Given: A continuously differentiable finite-dimensional density path on an open interval containing , with strictly positive; for the relative-entropy derivative conclusion, additionally assume that its real and imaginary matrix entries are twice continuously differentiable on that interval.
Finite matrix entropy derivative at a faithful state: for a differentiable path of self-adjoint positive-definite matrices, .
Finite relative entropy equals modular energy minus entropy: for each density endomorphism and the fixed faithful reference , .
Finite matrix spectral and state calculus: finite-dimensional Hermitian endomorphisms have orthonormal spectral decompositions, and the eigenvalues of a strictly positive one have a positive minimum.
The basis-independent trace of an endomorphism of a finite-dimensional vector space: trace is the finite sum of diagonal entries and therefore is linear.
Cauchy–Schwarz: , with equality exactly for linearly dependent vectors: for vectors in a complex inner-product space.
Proof
Let be the smallest eigenvalue of from [F3]. Since is entrywise continuous at , its finite matrix difference tends to zero in operator norm: in a fixed orthonormal basis, if every entry of has magnitude at most , then for a unit vector makes each coordinate of have magnitude at most , and each coordinate of has magnitude at most , and therefore , where . Choose so that for . For each , by spectral decomposition, while [F5] and [F6] give . Hence for nonzero . Thus is positive definite near and [F1] applies.
Apply [F1] to at : . Because on the whole interval, in the fixed basis [F4] gives ; the finite sum commutes with this limit. Therefore , as claimed.
The identity [F2] holds for every in the path. Its right-hand side is differentiable at by linearity of trace and step 2.1, so differentiating gives . The additional twice continuously differentiable hypothesis ensures the differentiability required here.
5 · Examples, experiments and counterexamples
None yet.
Sources
- John Watrous, The Theory of Quantum Information, author-hosted complete book manuscript, §5.2.1
- Mark M. Wilde, From Classical to Quantum Shannon Theory, complete book manuscript, §11.1
- John Watrous, The Theory of Quantum Information, author-hosted complete book manuscript, §§5.2.1–5.2.3
- Aron C. Wall, A proof of the generalized second law for rapidly changing fields and arbitrary horizon slices, §2.5
- Mark M. Wilde, From Classical to Quantum Shannon Theory, complete book manuscript, §§11.1 and 11.8