Needless to emphasize, the home of these quantum states and observables are the linear vector spaces or alternatively the Hilbert spaces. This chapter has crucial recipes relating to operations like inverse of matrices, tridiagonalization, QR decomposition, Gram-Schmidt orthogonalization, a wide spectrum of functions and powers of matrices, trace norm, Hilbert-Schmidt norm, singular value decomposition and so on. The recipies in this chapter can be not only used in quantum information theory but it can also be used as standalone recipies for any person working in linear algebra as well.
Needless to emphasize, the home of these quantum states and observables are the linear vector spaces or alternatively the Hilbert spaces. This chapter has crucial recipes relating to operations like inverse of matrices, tridiagonalization, QR decomposition, Gram-Schmidt orthogonalization, a wide spectrum of functions and powers of matrices, trace norm, Hilbert-Schmidt norm, singular value decomposition and so on. The recipies in this chapter can be not only used in quantum information theory but it can also be used as standalone recipies for any person working in linear algebra as well.
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.
There is a notational discrepancy in how mathematicians and physicists treat Hilbert spaces. In physics, one often works with finite-dimensional Hilbert spaces treated as ℂ n , and hence wants to write the Hermitian inner product as (x, y) = x ∗ y. This definition dictates the use of a Hermitian inner product that is antilinear in the first argument, the convention followed, for example, in Reed & Simon [246]. This is also the choice adopted in Dirac’s bracket notation, whose usage in quantum mechanics is very widespread. Our notation is chosen to extend-as closely as possible-the traditional notation of standard finite-dimensional matrix algebra to arbitrary complex inner product spaces and associated linear operators. In matrix algebra, column vectors and the corresponding matrices with one column are identical objects, row vectors are the linear functionals, and the adjoint is the conjugate transpose. For example, ℍ = ℍ × = ℂ n is the space of column vectors of size n, the dual space ℍ ∗ is the space of row vectors of size n, and the operator product ϕ ∗ ψ of a row vector ϕ ∗ and a column vector ψ is the standard Hermitian inner product of the column vectors ϕ and ψ. We use Greek lower case letters to write vectors, thus emphasizing their intended use as quantum state vectors in quantum mechanics. On the other hand, mathematicians working on reproducing kernel Hilbert spaces use an inner product (x, y) antilinear in the second argument, related to the physicist’s inner product (x, y) by (x, y) =( ). This is the convention followed, for example, in Rudin [252]. Although the two ways of defining the inner product lead to fully equivalent theories, all details look a bit different, a fact that has to be taken into account when reading the literature on the subject. For example, in the description based on the physical tradition, it is preferable to work with the antidual space in place of the dual space used in the mathematical tradition. In functional analysis, linear operators in Hilbert spaces are usually considered each with their own domain. But many computations in quantum mechanics require the consideration of algebras of operators with a common domain. The latter is a Euclidean space, a dense subspace of a Hilbert space. This space and its antidual play in many respects a more basic role in quantum physics than the Hilbert space itself. Therefore, and to avoid possible confusion caused by the different traditions, we give in the present chapter a self-contained introduction to Euclidean spaces and their associated spaces. All proofs are carried out in detail.
Abstract In this chapter, we present an overview of linear algebra with the purpose of outlining concepts relevant to quantum computing using the notation common to the field. The chapter consists of two parts. In the first part, we introduce vector spaces. We present the notions of linear dependence, dimensionality, inner product, and distance. We use these concepts to introduce finite-dimensional Hilbert spaces, a key ingredient for quantum information theory. In the second part of the chapter, we introduce linear operators. We describe the main classes of linear operators and show their mutual relations.
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).
This document explores the connections between singular value decomposition (SVD) and various tensor operations in the context of quantum state calculations and tensor networks. The relationships between SVD and quantities such as trace, trace distance, Frobenius norm, and fidelity are examined, highlighting their similarities and differences. The intuition behind these mathematical connections is explained, providing insights into the underlying principles and applications of SVD and tensor operations in quantum physics and computational mathematics
Abstract Decomposition of (finite-dimensional) operators in terms of orthogonal bases of matrices has been a standard method in quantum physics for decades. In recent years, it has become increasingly popular because of various methodologies applied in quantum information, such as the graph state formalism and the theory of quantum error correcting codes, but also due to the intensified research on the Bloch representation of quantum states. In this contribution we collect various interesting facts and identities that hold for finite-dimensional orthogonal matrix bases.