User:J.E.Källman

On Bra-ket notation, hilbert space and associated terminology
http://en.wikipedia.org/wiki/Inner_product_space In mathematics, an inner product space is a vector space with an additional structure called an inner product. This additional structure associates each pair of vectors in the space with a scalar quantity known as the inner product of the vectors. Inner products allow the rigorous introduction of intuitive geometrical notions such as the length of a vector or the angle between two vectors. They also provide the means of defining orthogonality between vectors (zero inner product). Inner product spaces generalize Euclidean spaces (in which the inner product is the dot product, also known as the scalar product) to vector spaces of any (possibly infinite) dimension, and are studied in functional analysis.

In mathematics, particularly linear algebra, an orthonormal basis for an inner product space V with finite dimension is a basis for V whose vectors are orthonormal.[1][2][3] For example, the standard basis for a Euclidean space Rn is an orthonormal basis, where the relevant inner product is the dot product of vectors. The image of the standard basis under a rotation or reflection (or any orthogonal transformation) is also orthonormal, and every orthonormal basis for Rn arises in this fashion.

http://en.wikipedia.org/wiki/Cauchy_sequence In mathematics, a Cauchy sequence (pronounced [koˈʃi]), named after Augustin-Louis Cauchy, is a sequence whose elements become arbitrarily close to each other as the sequence progresses. More precisely, given any small positive distance, all but a finite number of elements of the sequence are less than that given distance from each other.

http://www.quantiki.org/wiki/Hilbert_space An inner product naturally induces an associated norm, thus an inner product space is also a normed vector space. A complete space with an inner product is called a Hilbert space.

Every inner product \langle.,.\rangle on a real or complex vector space H gives rise to a norm | |. | | as follows:

\|x\| = \sqrt{\langle x,x \rangle}

We call H a Hilbert space if it is complete with respect to this norm. Completeness in this context means that every Cauchy sequence of elements of the space converges to an element in the space, in the sense that the norm of differences approaches zero. Every Hilbert space is thus also a Banach space (but not vice versa).

http://www.quantiki.org/wiki/Bra-ket_notation

(F.1):From Riesz's representation theorem we know that a Hilbert space \; \mathcal{H} and its [dual space] (1.1) are [isometrically [conjugate](1.2) isomorphic](1.3).


 * (1.1):Any vector space, V, has a corresponding dual vector space (or just dual space for short) consisting of all linear functionals on V. Linear functional or linear form (also called a one-form or covector) is a linear map from a vector space to its [field of scalars](1.1.1).
 * (1.1.1):A field that associates a scalar value to every point in a space. The scalar may either be a mathematical number, or a physical quantity. Scalar fields are required to be coordinate-independent, meaning that any two observers using the same units will agree on the value of the scalar field at the same point in space (or spacetime). Examples used in physics include the temperature distribution throughout space or the pressure distribution in a fluid
 * (1.2): A group G is a finite or infinite set of elements together with a binary operation (called the group operation) that together satisfy the four fundamental properties of closure, associativity, the identity property, and the inverse property. The operation with respect to which a group is defined is often called the "group operation," and a set is said to be a group "under" this operation. Elements A, B, C, ... with binary operation between A and B denoted AB form a group if

1. Closure: If A and B are two elements in G, then the product AB is also in G.

2. Associativity: The defined multiplication is associative, i.e., for all A,B,C in G, (AB)C=A(BC).

3. Identity: There is an identity element I (a.k.a. 1, E, or e) such that IA=AI=A for every element A in G.

4. Inverse: There must be an inverse (a.k.a. reciprocal) of each element. Therefore, for each element A of G, the set contains an element B=A^(-1) such that AA^(-1)=A^(-1)A=I.

A group is a monoid each of whose elements is invertible.

A group must contain at least one element, with the unique (up to isomorphism) single-element group known as the trivial group.

A group G is said to act on a set X when there is a map phi:G×X->X such that the following conditions hold for all elements x in X.

1. phi(e,x)=x where e is the identity element of G.

2. phi(g,phi(h,x))=phi(gh,x) for all g,h in G.

In this case, G is called a transformation group, X is a called a G-set, and phi is called the group action. Group action of symmetric group

In a group action, a group permutes the elements of X. The identity does nothing, while a composition of actions corresponds to the action of the composition. For example, as illustrated above, the symmetric group S_(10) acts on the digits 0 to 9 by permutations.

Conjugation has a meaning in group theory. Let G be a group and let x in G. Then, x defines a homomorphism phi_x:G->G given by phi_x(g)=xgx^(-1).

This is a homomorphism because phi_x(g)phi_x(h)=xgx^(-1)xhx^(-1)=xghx^(-1)=phi_x(gh).

The operation on G given by phi_x is called conjugation by x.

Conjugation is an important construction in group theory. Conjugation defines a group action of a group on itself and this often yields useful information about the group.


 * (1.3):Isometry is a distance-preserving map between metric spaces. A morphism is an abstraction derived from structure-preserving mappings between two mathematical structures. The notion of morphism recurs in much of contemporary mathematics. In set theory, morphisms are functions; in linear algebra, linear transformations Two mathematical structures are said to be isomorphic if there is an isomorphism between them. In essence, two objects are isomorphic if they are indistinguishable given only a selection of their features, and the isomorphism is the mapping of the set elements and the selected operations between the objects. A named isomorphism indicates which features are selected for this purpose. Thus, for example, two objects may be group isomorphic without being ring isomorphic, since the latter isomorphism selects the additional structure of the multiplicative operator. An isomorphism can thus be considered a [bijectional](1.2.1) [homomorphism](1.2.2).
 * (1.2.1):A structure-preserving map between two algebraic structures (such as groups, rings, or vector spaces)
 * (1.2.2):A function giving an exact pairing of the elements of two sets. Every element of one set is paired with exactly one element of the other set, and every element of the other set is paired with exactly one element of the first set. There are no unpaired elements

(F.2):Thus we can associate to any vector \; \psi \in \mathcal{H} the [linear functional](2.1) \; \psi^\dagger, continuous in the [norm topology](2.2), defined on \; \mathcal{H} as follows:

\; \psi^\dagger[\phi] = \langle \psi,\phi \rangle. The space dual to the dual of \; \mathcal{H} can be identified with \; \mathcal{H} as it is [isomorphic](1.2) to \; \mathcal{H}. Thus we can associate to any vector \; \phi \in \mathcal{H} the [linear functional](2.1), indicated with the same symbol \; \phi, continuous in the [norm topology](2.2), defined on \; \mathcal{H}^\dagger (dual vector space of H of all linear functionals on H) as follows:

\; \phi[\psi^\dagger] = \langle \psi,\phi \rangle.

Definition: The vectors of \; \mathcal{H} are called kets and are denoted by \; | \; \rangle ; the vectors of \; \mathcal{H}^\dagger are called bras and are denoted by \; \langle \; |.


 * (2.1):A function that depends linearly on another function (mapping of a hilbert space vector to its dual space scalar).
 * (2.2):If (V, ‖·‖) is a normed vector space, the norm ‖·‖ induces a metric (a notion of distance) and therefore a topology on V. This metric is defined in the natural way: the distance between two vectors u and v is given by ‖u−v‖.

http://www.physics.unlv.edu/~bernard/phy721_99/tex_notes/node6.html

Question related to braket notation: down vote favorite

If I'm not wrong, a bra, ⟨ϕn|, can be thought as a linear functional that when applied to a ket vector, |ϕm⟩, returns a complex number; that is, the inner product it's a linear mapping that ξ→C; yet, exist a bra to each ket, and in discrete basis, the reverse it's valid too.

Well, thinking in the scope of discrete basis, my question, then, is: when we take a adjoint of a vector, we go from a vector space to a "linear functional" space? That is, when we want to calculate the inner product of |ϕn⟩ with itself, we are, as a matter of fact, applying the bra associated with the vector in the same vector?

1) Whenever one has a topological vector space (TVS) V over some field F, one can construct a dual vector space V∗ consisting of continuous linear functionals f:V→F.

2) Under relative mild conditions on the topology of V, it is possible to turn the dual vector space V∗ into a TVS. One may iterate the construct of dual vector spaces, so that more generally, one may consider the double-dual vector space V∗∗, the triple-dual vector space V∗∗∗, etc.

3) There is a natural/canonical injective linear map i:V→V∗∗. It is defined as

i(v)(f):=f(v),v∈V,f∈V∗.

4) If the map i is bijective V≅V∗∗, one says that V is a reflexive TVS.

5) If V is an inner product space (which is a particularly nice example of a TVS), then there is a natural/canonical injective conjugated linear map j:V→V∗. It is defined as

j(v)(w):=⟨v,w⟩,v,w∈V.

Here we follow the Dirac convention that the "bracket" ⟨⋅,⋅⟩ is conjugated linear in the first entry (as opposed to a lot of the math literature).

6) Riesz representation theorem (RRT) shows that j is a bijection if V is a Hilbert space. In other words, a Hilbert space is selfdual V≅V∗. If one identifies V with the set of kets, and V∗ with the set of bras, one may interpret RRT as saying that there is a natural/canonical one-to-one correspondence between bras and kets.

The expression \langle\phi|\psi\rangle is typically interpreted as the probability amplitude for the state ψ to collapse into the state ϕ.

In quantum mechanics, wave function collapse (also called collapse of the state vector or reduction of the wave packet) is the phenomenon in which a wave function—initially in a superposition of several different possible eigenstates—appears to reduce to a single one of those states after interaction with an observer. In simplified terms, it is the reduction of the physical possibilities into a single possibility as seen by an observer. It is one of two processes by which quantum systems evolve in time, according to the laws of quantum mechanics as presented by John von Neumann.[1