Vectors are reviewed since they are extensively used in electric and magnetic field calculations and in quantum mechanics. A general definition of vectors is given followed by the more familiar Euclidean definition. The algebra of vectors is discussed including their addition, subtraction, multiplication and group characteristics. The matrix and Dirac notation for vectors is introduced and used to present vector transformations via operator matrices. A short description of Hilbert space and its fundamental definition is given. The merging of vectors spaces is discussed in preparation for the development of multiple-spin density matrices.
Vectors are reviewed since they are extensively used in electric and magnetic field calculations and in quantum mechanics. A general definition of vectors is given followed by the more familiar Euclidean definition. The algebra of vectors is discussed including their addition, subtraction, multiplication and group characteristics. The matrix and Dirac notation for vectors is introduced and used to present vector transformations via operator matrices. A short description of Hilbert space and its fundamental definition is given. The merging of vectors spaces is discussed in preparation for the development of multiple-spin density matrices.
Vectors are reviewed since they are extensively used in electric and magnetic field calculations and in quantum mechanics. A general definition of vectors is given followed by the more familiar Euclidean definition. The algebra of vectors is discussed including their addition, subtraction, multiplication and group characteristics. The matrix and Dirac notations for vectors are introduced and used to present vector transformations via operator matrices. A short description of Hilbert space and its fundamental definition is given. The merging of vector spaces is discussed in preparation for the development of multiple-spin density matrices.
We assume the reader has a strong background in elementary linear algebra. In this section we familiarize the reader with the algebraic notation used in quantum mechanics, remind the reader of some basic facts about complex vector spaces, and introduce some notions that might not have been covered in an elementary linear algebra course. The linear algebra notation used in quantum computing will likely be familiar to the student of physics, but may be alien to a student of mathematics or computer science. It is the Dirac notation, which was invented by Paul Dirac and which is used often in quantum mechanics. In mathematics and physics textbooks, vectors are often distinguished from scalars by writing an arrow over the identifying symbol: e.g a⃗. Sometimes boldface is used for this purpose: e.g. a. In the Dirac notation, the symbol identifying a vector is written inside a ‘ket’, and looks like |a⟩. We denote the dual vector for a (defined later) with a ‘bra’, written as ⟨a|. Then inner products will be written as ‘bra-kets’ (e.g. ⟨a|b⟩). We now carefully review the definitions of the main algebraic objects of interest, using the Dirac notation. The vector spaces we consider will be over the complex numbers, and are finite-dimensional, which significantly simplifies the mathematics we need. Such vector spaces are members of a class of vector spaces called Hilbert spaces. Nothing substantial is gained at this point by defining rigorously what a Hilbert space is, but virtually all the quantum computing literature refers to a finite-dimensional complex vector space by the name ‘Hilbert space’, and so we will follow this convention. We will use H to denote such a space. Since H is finite-dimensional, we can choose a basis and alternatively represent vectors (kets) in this basis as finite column vectors, and represent operators with finite matrices. As you see in Section 3, the Hilbert spaces of interest for quantum computing will typically have dimension 2n, for some positive integer n. This is because, as with classical information, we will construct larger state spaces by concatenating a string of smaller systems, usually of size two.
Heavy use of matrices is made in the discussions of density operators and rotation operators. Thus the general properties of matrices are presented and, since they represent a non-Abelian group, some of their peculiarities are included. Their addition, subtraction and various kinds of multiplications are discussed. Determinants are developed from matrices and used in the solution of simultaneous equations. Various matrix transformations are discussed with a view towards their later use in density matrix calculations and finally, the exponentials of matrices are discussed.
Heavy use of matrices is made in the discussions of density operators and rotation operators. Thus, the general properties of matrices are presented and, since they represent a non-Abelian group, some of their peculiarities are included. Their addition, subtraction and various kinds of multiplications are discussed. Determinants are developed from matrices and used in the solution of simultaneous equations. Various matrix transformations are discussed with a view towards their later use in density matrix calculations and finally, the exponentials of matrices are discussed.
Linear algebra is the natural mathematical language of quantum mechanics. For this reason, the present chapter starts with a review of Hilbert spaces for vectors and operators. We introduce vector and Hilbert spaces along with inner products and metrics. The Dirac notation is developed for the Euclidean vector spaces as a starting point for the concepts of complete orthonormal sets of vectors, closure, dual vector spaces, and adjoint operators. The Dirac delta function in various forms and the principal part is introduced in Appendix 5 as an essential tool. The chapter turns to the main use of Dirac notation for function spaces; the concepts of norm, inner product, and closure are discussed. Fourier, Cosine, and Sine series are discussed as examples of expansions in complete orthonormal sets of functions. Although Hilbert spaces are interesting mathematical objects with important physical applications, the study of linear algebra remains incomplete without a study of linear operators (i.e., linear transformations). In fact, the set of linear transformations itself forms a vector space and therefore has a basis set. The basis set for the operator is linked with the basis sets for the spaces that it operates between. The linear operator can be discussed as an abstract operator or through an isomorphism as a matrix or as a generalized expansion in operator space. A Hermitian (a.k.a., self-adjoint) operator produces a basis set within a Hilbert space. The basis set comes from the eigenvector equation for the particular operator. The fact that a Hermitian operator produces a complete set (of orthornomal vectors) has special importance for quantum mechanics. Observables such as energy or momentum correspond to Hermitian operators. Complete sets make it possible to represent every possible result of a measurement of the observable by an object (vector) in the theory. The Hermitian operators have real eigenvalues which represent the results of the measurement.
We have seen in the previous chapter how useful wave mechanics can be in solving physical problems. But wave mechanics has several limitations. It describes physical states by means of wave functions, which are functions of the positions of the particles of the system, but why should we single out position as the fundamental physical observable? For instance, we might want to describe states in terms of probability amplitudes for particles to have certain values of the momentum or energy rather than the position. A more fundamental limitation is that there are attributes of physical systems that cannot be described at all in terms of the positions and momenta of a set of particles. One of these attributes is spin , which will be a chief subject of Chapter 4. Another is the value of the electric or magnetic field at some point in space, treated in Chapter 11. This chapter will describe the principles of quantum mechanics in a formalism which is essentially the “transformation theory” of Dirac, mentioned briefly in Section 1.4. This formalism generalizes both the wave mechanics of Schrödinger and the matrix mechanics of Heisenberg, and is sufficiently comprehensive to apply to any sort of physical system. States The first postulate of quantum mechanics is that physical states can be represented as vectors in a sort of abstract space known as Hilbert space . Before getting into Hilbert space, I need to say a bit about vectors in general. In kindergarten we learn that vectors are quantities with both magnitude and direction. Later, when we study analytic geometry, we learn instead to describe a vector in d dimensions as a string of d numbers, the components of the vector. The latter approach lends itself well to calculation, but in some respects the kindergarten version is better, because it allows us to describe relations among vectors without specifying a coordinate system. For instance, a statement that one vector is parallel to a second vector, or perpendicular to a third, has nothing to do with how we choose our coordinate system. Here we will formulate what we mean by vector spaces in general, and Hilbert space in particular, in a way that is independent of the coordinates we use to describe directions in these spaces.