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Finite-regulator outside density and entropy
Definition
Let and be nonzero finite-dimensional complex inner-product spaces, and let be a density operator on . Choose orthonormal bases and , and use the ordered product basis , with the index first. The outside density is the linear map whose matrix entries in are
Fix as a real scale. If are the eigenvalues of counted with multiplicity, define
where is the natural logarithm and . The finite matrix argument below proves that is a density operator and that the displayed endomorphism is basis-independent, so is a finite real number independent of the chosen bases. In this mathematical definition is only a positive scalar; no physical units or model are asserted.
Definition references
Linear map between vector spaces over the same field
Coordinate columns and matrices of linear maps relative to ordered bases
Ordered tensor registers, their inner product, and wire permutations
Non-negative and positive operators
The basis-independent trace of an endomorphism of a finite-dimensional vector space
Finite-dimensional von Neumann entropy
Watrous, The Theory of Quantum Information, Chapter 2, section 2.1.3
Watrous, The Theory of Quantum Information, Chapter 5, section 5.2.1
Facts & Assumptions
Given: Nonzero finite-dimensional complex inner-product spaces ; a density operator on their ordered tensor product; orthonormal bases of each factor; and a fixed real .
Density operator: on a nonzero finite-dimensional complex inner-product space, a density operator is a non-negative endomorphism of trace one.
Non-negative and positive operators: a non-negative operator is self-adjoint and has non-negative quadratic form on every vector.
Ordered tensor registers, their inner product, and wire permutations: the factor order is retained, the ordered product of the chosen orthonormal factor bases is an orthonormal basis of , and the tensor-product inner product on elementary tensors is the product of the factor inner products; that convention is linear in the first argument.
Linear map between vector spaces over the same field: a linear map is a function between vector spaces over the same field that preserves linear combinations.
Coordinate columns and matrices of linear maps relative to ordered bases: the column for an input basis vector is the coordinate column of its image in the output basis.
A finite list is an ordered basis if and only if every equals for exactly one ; those scalars are the coordinates of in that ordered basis: coordinates in a finite ordered basis are unique.
The trace as the sum of the diagonal entries defines matrix trace as the sum of diagonal entries; The basis-independent trace of an endomorphism of a finite-dimensional vector space defines endomorphism trace by a matrix in any ordered basis and establishes basis-independence.
Finite-dimensional von Neumann entropy: for a finite density operator, entropy is the finite spectral sum , with zero-eigenvalue contribution and natural logarithms.
A non-negative operator is equivalently self-adjoint with nonnegative eigenvalues, a positive semidefinite matrix in an orthonormal basis, or an operator of the form S^*S: a non-negative endomorphism of a finite-dimensional complex inner-product space has an orthonormal eigenbasis with nonnegative real eigenvalues.
Proof
For each basis vector , prescribe . The product basis in [F3] supplies the matrix entries of , and [F4]–[F6] give a unique linear extension from the prescribed images; its matrix is exactly the displayed formula.
Since is self-adjoint by [F1]–[F2], its matrix satisfies , so is self-adjoint. For every , expansion in gives by [F2]–[F3]. Its trace is by [F1] and [F7]; hence is a density operator by [F1].
For every endomorphism of , define on the product basis by ; [F3]–[F6] give its unique linear extension. For arbitrary , expansion in the chosen bases shows this map sends to , so it is independent of those bases. Finite matrix multiplication gives . The right side is therefore basis-independent. If a second choice produced , then for all ; taking to be the matrix unit with sole nonzero entry in row , column gives , so every entry of the difference is zero. Thus the endomorphism is basis-independent.
By [F9], has an orthonormal eigenbasis with nonnegative real eigenvalues; [F7] and step 1.2 show their sum is , so each lies in . The finite spectral sum in [F8] is therefore real, with zero eigenvalues contributing by convention. Its eigenvalue multiset is intrinsic to the endomorphism, and step 1.3 proves that endomorphism is basis-independent; multiplying by the fixed real preserves finiteness and basis-independence.
Depends on
Linear map between vector spaces over the same field
Coordinate columns and matrices of linear maps relative to ordered bases
A finite list is an ordered basis if and only if every equals for exactly one ; those scalars are the coordinates of in that ordered basis
Ordered tensor registers, their inner product, and wire permutations
Non-negative and positive operators
Density operator
The trace as the sum of the diagonal entries
The basis-independent trace of an endomorphism of a finite-dimensional vector space
Finite-dimensional von Neumann entropy
A non-negative operator is equivalently self-adjoint with nonnegative eigenvalues, a positive semidefinite matrix in an orthonormal basis, or an operator of the form S^*S