This chapter illustrates how one can obtain a matrix representation of a state vector, an operator, and an eigenvalue equation in a basis of orthonormal states. It helps to learn that matrix representations are very useful in calculations of the properties of operators, because algebra of matrices is very simple and completely developed. Readers familiar with the algebra of matrices agree that mathematical operations on matrices such as multiplication by a scalar, addition, subtraction, multiplication and diagonalization are simple and easy to perform. In fact, the application of the algebra of matrices to quantum physics results from the direct relations between operators and matrices that any mathematical relation that holds between operators also holds between the corresponding matrices. The eigenvalues and eigenvectors of the matrix representing a given operator  in the basis of orthonormal states are the same as the eigenvalues and eigenvectors of Â.
This chapter illustrates how one can obtain a matrix representation of a state vector, an operator, and an eigenvalue equation in a basis of orthonormal states. It helps to learn that matrix representations are very useful in calculations of the properties of operators, because algebra of matrices is very simple and completely developed. Readers familiar with the algebra of matrices agree that mathematical operations on matrices such as multiplication by a scalar, addition, subtraction, multiplication and diagonalization are simple and easy to perform. In fact, the application of the algebra of matrices to quantum physics results from the direct relations between operators and matrices that any mathematical relation that holds between operators also holds between the corresponding matrices. The eigenvalues and eigenvectors of the matrix representing a given operator  in the basis of orthonormal states are the same as the eigenvalues and eigenvectors of Â.
This chapter deals with the matrix and vector formulation of quantum mechanics. Topics covered under the chapter are: matrices and their basic algebra, bra and ket notations, vector space and the basis vectors, inner and outer products of vectors, the orthogonalization procedure for vectors, the Schwarz inequality, linear transformation of a vector, eigenvalues and eigenvectors of a matrix, numerical methods to find eigenvalue and eigenvector, different types of matrices used in quantum mechanics, diagonalization of a matrix, the Cayley-Hamilton theorem, change of basis in a vector space and the unitary transformations, infinite-dimensional and Hilbert spaces, representation of quantum states and operators in Hilbert space, the Schrödinger equation in matrix form, assumptions of quantum mechanics in matrix formulations, the general uncertainty principle, the one dimensional harmonic oscillator, solved examples and unsolved exercises. The objectives of this chapter are to introduce the important aspects of vector and matrix algebra that have been vital in the development and the description of modern quantum mechanics and in exhibiting that the Heisenberg and Schrödinger approaches to quantum mechanics are equivalent.
This chapter provides a simple general introduction to some basic mathematics necessary for a subsequent discussion of Lie algebras, operator methods, and their applications to theoretical physics and chemistry. It also provides some useful mathematical tools to derive important theoretical results and expressions in a simple and elegant way. In quantum mechanics the state of a system is represented by an element of a particular vector space, in which one defines an appropriate scalar or inner product, known in mathematics as Hilbert space. Hermitian operators with a complete set of eigenvectors belonging to that state space represent physical observables. Moreover, in certain cases it proves useful to work with vector spaces formed by linear operators. Hermitian operators are particular cases of linear operators and physical observables are particular cases of Hermitian operators. In many physical applications of operator methods one needs to reorder products of exponential operators or to rewrite one such operator as a product of simpler ones.
Matrices, vectors and determinants are presented in this chapter, mainly their applications to the quantum mechanics and quantum chemistry. A very important property of the matrices is that they do not commute and this leads to the commutator relations discussed in the end of this chapter and to most important features of the quantum matrix mechanics, for example, the uncertainty principle. In this chapter, the most important properties of the matrices (trace of matrix, matrix multiplication, transpose of a matrix, symmetric and orthogonal matrix) are shown along with the methods to obtain the determinant of the matrix (Gaussian elimination and Laplace expansion). The Gauss-Jordan method to obtain the inverse matrix is presented and antissymetric matrices and its importance to quantum mechanics. Matrix diagonalization, Jacobi method of diagonlization, eigenvectors in vector space, Hermitian matrix, unitary transformation and linear transformations, characteristic equations and system of secular homogeneous linear equations are shown in sequence which are vital for several operations in the quantum wave mechanics. Several Fortran codes of linear algebra are also displayed in this chapter (174 words).
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.
Dirac introduced a very useful notation of state vectors Ψ i in terms of "bra" vectors. This notation allows to make the formal expressions of quantum physics more transparent and easier to manipulate. The bra-ket notation extends to action of operators on state vectors. The projection operator and the completeness relation are very important quantities in quantum physics. They are very useful in calculations and are often employed to represent an arbitrary operator and a wave function (state) in terms of orthonormal states. To illustrate the procedure of representations, this chapter considers an arbitrary operator  and shows how to represent the operator in terms of projection operators of known orthonormal states. The example in the chapter illustrates the concept of orthonormal states. It presents the application of the completeness relation. Using the completeness relation, that matrix elements of the product operator in the same orthonormal basis can be found from the multiplication of the matrix elements.
This chapter introduces the essential topics and to show how eigenvalues and eigenvectors are calculated. In quantum computation it is relevant to consider cases in which the eigenvalues are all real and the eigenvectors are orthogonal. In the study of quantum computation, the matrices with which we usually work have properties that enable us to be quite specific about the nature of the eigenvalues and eigenvectors. Post-measurement the system is in a state represented by an eigenvector or eigenvectors. In quantum computation it is relevant to consider cases in which the eigenvalues are all real and the eigenvectors are orthogonal.