Hilbert Book Model Project/Quaternionic Hilbert Spaces
Contents
Hilbert spaces[edit]
Around the turn of the nineteenth century into the twentieth century David Hilbert and others developed the type of vector space that later got Hilbert's name.
The Hilbert space is a particular vector space because it defines an inner product for every pair of its member vectors.
That inner product can take values of a number system for which every nonzero member owns a unique inverse. This requirement brands the number system as a division ring.
Bra's and ket's[edit]
Paul Dirac introduced a handy formulation for the inner product that applies a bra and a ket.
The bra is a covariant vector and the ket is a contravariant vector. The inner product acts as a metric.
For bra vectors hold

(

(
For ket vectors hold

(

(
Inner product[edit]
For the inner product holds that is a member of a division ring.

(
For quaternionic numbers and hold

(

(

(
Thus

(

(
We made a choice. We could instead define and .
Separable[edit]
In mathematics a topological space is called separable if it contains a countable dense subset; that is, there exists a sequence of elements of the space such that every nonempty open subset of the space contains at least one element of the sequence.
Its values on this countable dense subset determine every continuous function on the separable space .
The Hilbert space is separable. That means that a countable row of elements exists that spans the whole space.
If = [1 when ; 0 otherwise] then forms an orthonormal base of the Hilbert space.
A ket base of is a minimal set of ket vectors that together span the Hilbert space .
Any ket vector in can be written as a linear combination of elements of .

(
A bra base of is a minimal set of bra vectors that together span the Hilbert space .
Any bra vector in can be written as a linear combination of elements of .

(
Usually, a base selects vectors such that their norm equals 1. Such a base is called an orthonormal base.
Operators[edit]
Operators act on a subset of the elements of the Hilbert space.
Linear operators[edit]
An operator is linear when for all vectors and for which is defined and for all quaternionic numbers and

(
Operator is colinear when for all vectors for which is defined and for all quaternionic numbers there exists a quaternionic number such that

(
If is an eigenvector of operator with quaternionic eigenvalue

(
then is an eigenvector of with quaternionic eigenvalue .

(
is the adjoint of the normal operator .

(

(

(

(
If , then is a self adjoint operator.
 is a nil operator.
A linear transformation of Hilbert space changes the value of the inner product with the transformed vector.

(
The effect of the transpose transformation is then given by

(
A linear operator is normal if exists and .
For a normal transformation holds

(
Thus

(

(

(

(

(
is the Hermitian part of .
is the antiHermitian part of .
For two normal operators and holds

(
For a unitary transformation holds

(
Closure[edit]
The closure of means that converging rows of vectors converge to a vector of .
Operator construction[edit]
is a linear operator. is its adjoint operator.
For the orthonormal base
holds

(

(
This definition enables the shorthand

(
It is evident that

(
Reference operator holds

(
If consists of all rational values of a version of the quaternionic number system (marked by superscript ) then represents the parameter space of (discrete) function .

(
Omitting the superscript means that we take numbers from the version of the number system that serves the specification of the inner product.
We call the combination of equation 32 and equation 33 the reverse braket method.
Nonseparable Hilbert space[edit]
Every infinite dimensional separable Hilbert space owns a unique nonseparable companion Hilbert space . This is achieved by the closure of the eigenspaces of all reference operators. In this procedure, in many occasions, the notion of the dimension of subspaces looses its sense.
Gelfand triple and Rigged Hilbert space are other names for the general nonseparable Hilbert spaces.
In the nonseparable Hilbert space, for operators with continuum eigenspaces, the reverse braket method turns from a summation into an integration

(
Here, the subscripts of the countable base vanished.
The corresponding shorthand for operator is

(
For eigenvectors the function defines as

(
The reference operator that provides the background parameter space as its eigenspace follows from

(
with shorthand

(
Floating Platforms[edit]
The nonseparable Hilbert space can use the same versions of the quaternionic number system to define a series of parameter spaces that are eigenspaces of corresponding reference operators as its separable companion did. One of these parameter spaces is special because it applies the version of the quaternionic number system that serves the specification of the inner product of the companion Hilbert spaces. The corresponding eigenspace delivers the background parameter space of the Hilbert spaces. All other reference operators deliver eigenspaces that float with respect to the background parameter space and that possess a private ordering symmetry. A Cartesian coordinate system in combination with a polar coordinate system specifies the ordering symmetry. The difference between the ordering symmetry of the parameter space with the ordering symmetry of the background parameter space determines the symmetry flavor of the considered parameter space. The symmetry flavor delivers a symmetry related charge that locates at the geometric center of the floating parameter space. This symmetry related charge interacts with a symmetry related field.
The floating parameter spaces and their symmetryrelated charges belong to the furniture of the separable Hilbert space. The embedding of the separable Hilbert space into the nonseparable Hilbert space introduces an interaction with the symmetry related field.
Scanning subspace[edit]
The eigenvectors of the reference operator that defines the background parameter space can be split with respect to a selected progression value that specifies the real parts of the eigenvalues. The eigenvectors that correspond to the progression value span a scanning subspace that represents a static status quo. Lower values of the real part of the eigenvalue define the historical part of the Hilbert space. Higher values define the future part of the Hilbert space. This scanning subspace ᙎ turns a base model that consists of a separable Hilbert space and its companion nonseparable Hilbert space into a dynamic model.
The separable Hilbert space and its nonseparable companion Hilbert space combine into a base model ᙕ in which embeds via an embedding process that takes place in the scanning subspace ᙎ.
Progression steps with ᙎ in and flows in .
This base model ᙕ must extend with extra mechanisms that specify the dynamic locations of objects that hop around on the floating platforms. These extra mechanisms form the subject of the full Hilbert Book Model ᙢ.
Merging technologies[edit]
The combination of the two Hilbert spaces and together with the reverse braket method for the definition of operators via reference operators and quaternionic functions, creates a powerful base model ᙕ that merges quaternionic Hilbert space operator technology with quaternionic function theory and indirectly with quaternionic differential and integral calculus.
The combination enables to consider that the nonseparable Hilbert space embeds the separable Hilbert space in an ongoing process that acts as a function of the progress of a scanning subspace that defines the instant of the embedding.
As long as a differentiation or integration results in a sufficiently continuous function, then this function can help to define a new operator. This offers the opportunity to represent the solutions of differential and integral equations inside the base model ᙕ.
Also, the results of stochastic processes can be stored in the separable Hilbert space and if the result forms a coherent and dense location swarm, then the corresponding location density distribution can store embedded in a continuum in the nonseparable Hilbert space . This is applied in the Hilbert Book Model ᙢ.
Sets, coherent swarms, distributions and embedding continuums[edit]
The operators in the separable Hilbert space offer eigenspaces that can be
 Sets of spurious eigenvalues
 Stochastic coherent swarms of eigenvalues
 Stochastic coherent swarms of eigenvalues can be considered to be generated by a stochastic process that owns a characteristic function
 Stochastic coherent swarms of eigenvalues may be ordered with respect to their timestamp and become well ordered coherent swarms
 Stochastic well ordered coherent swarms of eigenvalues are ordered with respect to their timestamp
 Stochastic well ordered coherent swarms of eigenvalues may be spatially ordered and then become ordered distributions of eigenvalues
 Ordered distributions of eigenvalues
 Ordered distributions posses a location density distribution
If a continuum embeds an ordered distribution, then the defining function of the operator that has the continuum as eigenspace is the convolution of the location density distribution of the ordered distribution with the Green'sfunction of the continuum.
For example, an ordered distribution that is defined by an isotropic Gaussian function results in an embedding continuum that is definened by an function.