Linear Algebra and Dirac Notation, Pt. 3

Similar documents
Principles of Quantum Mechanics Pt. 2

Linear Algebra using Dirac Notation: Pt. 2

Linear Algebra and Dirac Notation, Pt. 2

Linear Algebra and Dirac Notation, Pt. 1

The Principles of Quantum Mechanics: Pt. 1

Lecture 3: Hilbert spaces, tensor products

Quantum Computing Lecture 2. Review of Linear Algebra

2. Review of Linear Algebra

1. The Polar Decomposition

Hilbert Spaces: Infinite-Dimensional Vector Spaces

Algebraic Theory of Entanglement

Linear Algebra Review. Vectors

MP 472 Quantum Information and Computation

Lecture notes on Quantum Computing. Chapter 1 Mathematical Background

Quantum Nonlocality Pt. 4: More on the CHSH Inequality

Foundations of Computer Vision

Linear Algebra (Review) Volker Tresp 2018

Multilinear Singular Value Decomposition for Two Qubits

Lecture 3: Review of Linear Algebra

Lecture 3: Review of Linear Algebra

The following definition is fundamental.

Ph 219/CS 219. Exercises Due: Friday 20 October 2006

Review of similarity transformation and Singular Value Decomposition

5. Orthogonal matrices

Introduction to Quantum Information Hermann Kampermann

Quantum Information & Quantum Computing

Linear Algebra- Final Exam Review

Linear Algebra and the Basic Mathematical Tools for Finite-Dimensional Entanglement Theory

Entanglement Measures and Monotones

Quantum Computation. Alessandra Di Pierro Computational models (Circuits, QTM) Algorithms (QFT, Quantum search)

MATRICES ARE SIMILAR TO TRIANGULAR MATRICES

Quantum Physics II (8.05) Fall 2004 Assignment 3

Quantum Entanglement and Error Correction

1 Readings. 2 Unitary Operators. C/CS/Phys C191 Unitaries and Quantum Gates 9/22/09 Fall 2009 Lecture 8

Spring, 2012 CIS 515. Fundamentals of Linear Algebra and Optimization Jean Gallier

Mathematical Methods for Quantum Information Theory. Part I: Matrix Analysis. Koenraad Audenaert (RHUL, UK)

Introduction to quantum information processing

Math 113 Final Exam: Solutions

Linear Algebra (Review) Volker Tresp 2017

Decomposition of Quantum Gates

Decay of the Singlet Conversion Probability in One Dimensional Quantum Networks

linearly indepedent eigenvectors as the multiplicity of the root, but in general there may be no more than one. For further discussion, assume matrice

Hilbert Space, Entanglement, Quantum Gates, Bell States, Superdense Coding.

Mathematical Methods wk 2: Linear Operators

Entanglement Measures and Monotones Pt. 2

LINEAR ALGEBRA BOOT CAMP WEEK 4: THE SPECTRAL THEOREM

MA 1B ANALYTIC - HOMEWORK SET 7 SOLUTIONS

CS 143 Linear Algebra Review

Clifford Algebras and Spin Groups

5.6. PSEUDOINVERSES 101. A H w.


AMS526: Numerical Analysis I (Numerical Linear Algebra)

Reflections in Hilbert Space III: Eigen-decomposition of Szegedy s operator

Math Bootcamp An p-dimensional vector is p numbers put together. Written as. x 1 x =. x p

Singular Value Decomposition (SVD)

CS286.2 Lecture 8: A variant of QPCP for multiplayer entangled games

Mathematical foundations - linear algebra

C/CS/Phys 191 Quantum Gates and Universality 9/22/05 Fall 2005 Lecture 8. a b b d. w. Therefore, U preserves norms and angles (up to sign).

Categories and Quantum Informatics: Hilbert spaces

. Here we are using the standard inner-product over C k to define orthogonality. Recall that the inner-product of two vectors φ = i α i.

Introduction to Matrix Algebra

Ir O D = D = ( ) Section 2.6 Example 1. (Bottom of page 119) dim(v ) = dim(l(v, W )) = dim(v ) dim(f ) = dim(v )

Spin foam vertex and loop gravity

Finite-dimensional spaces. C n is the space of n-tuples x = (x 1,..., x n ) of complex numbers. It is a Hilbert space with the inner product

The Framework of Quantum Mechanics

Lecture 10: Vector Algebra: Orthogonal Basis

Quantum Simulation. 9 December Scott Crawford Justin Bonnar

Review problems for MA 54, Fall 2004.

2. Introduction to quantum mechanics

Quantum Mechanics and Quantum Computing: an Introduction. Des Johnston, Notes by Bernd J. Schroers Heriot-Watt University

Quantum Computing 1. Multi-Qubit System. Goutam Biswas. Lect 2

MATH 20F: LINEAR ALGEBRA LECTURE B00 (T. KEMP)

Quantum Physics II (8.05) Fall 2002 Assignment 3

Trace Class Operators and Lidskii s Theorem

(a) If A is a 3 by 4 matrix, what does this tell us about its nullspace? Solution: dim N(A) 1, since rank(a) 3. Ax =

COMP 558 lecture 18 Nov. 15, 2010

Glossary of Linear Algebra Terms. Prepared by Vince Zaccone For Campus Learning Assistance Services at UCSB

Operator-Schmidt Decompositions And the Fourier Transform:

AMS526: Numerical Analysis I (Numerical Linear Algebra)

Pseudoinverse & Moore-Penrose Conditions

MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators.

ALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA

Basic Elements of Linear Algebra

Applied Linear Algebra in Geoscience Using MATLAB

Conceptual Questions for Review

Quantum Computing Lecture 3. Principles of Quantum Mechanics. Anuj Dawar

PHY305: Notes on Entanglement and the Density Matrix

Vector spaces and operators

Lecture 19: Polar and singular value decompositions; generalized eigenspaces; the decomposition theorem (1)

LINEAR ALGEBRA SUMMARY SHEET.

Matrix Factorization and Analysis

4 Matrix product states

Consistent Histories. Chapter Chain Operators and Weights

NOTES ON PRODUCT SYSTEMS

SYLLABUS. 1 Linear maps and matrices

DECAY OF SINGLET CONVERSION PROBABILITY IN ONE DIMENSIONAL QUANTUM NETWORKS

MATRIX ALGEBRA. or x = (x 1,..., x n ) R n. y 1 y 2. x 2. x m. y m. y = cos θ 1 = x 1 L x. sin θ 1 = x 2. cos θ 2 = y 1 L y.

Diagonalizing Matrices

On positive maps in quantum information.

Lecture 4 Orthonormal vectors and QR factorization

Transcription:

Linear Algebra and Dirac Notation, Pt. 3 PHYS 500 - Southern Illinois University February 1, 2017 PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 1 / 16

The tensor product is a construction that combines different vector spaces to form one large vector space. Definition (Tensor Product Space) If H 1 is a Hilbert space with orthonormal basis { a i } d 1 and H 2 is a Hilbert space with orthonormal basis { b i } d 2, then their tensor product space is a vector space H 1 H 2 with orthonormal basis given by { a i b j } d 1,d 2,j=1, called a tensor product basis. Every vector Ψ H 1 H 2 can be written as a linear combination of the basis vectors: d 1 d 2 Ψ = c ij a i b j. j=1 PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 2 / 16

Definition (Tensor Product of Vectors) The tensor product is a map : H 1 H 2 H 1 H 2 that generates the tensor product vector ψ φ H 1 H 2 for ψ H 1 and φ H 2, such that 1 (c ψ ) φ = ψ (c φ ) = c( ψ φ ) ψ H 1, φ H 2, c C, 2 ( ψ + ω ) φ = ψ φ + ω φ ψ, ω H 1, φ H 2, 3 ψ ( φ + ω ) = ψ φ + ψ ω ψ H 1, φ, ω H 2. These three properties are known as bilinearity. Hence, the tensor product is a bilinear map from H 1 H 2 to H 1 H 2. PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 3 / 16

For notation simplicity, the tensor product of two vectors ψ φ is often written as ψ φ, or even just ψφ. Example Suppose ψ = a 0 + b 1 and φ = c 0 + d 1. (a) Express ψ φ in the tensor product basis { 00, 01, 10, 11 }. (b) Express φ ψ in the tensor product basis { 00, 01, 10, 11 }. In this example H 1 = H 2 = C 2. The tensor product space is C 2 C 2, which is also written denoted by C 2 := C 2 C 2. PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 4 / 16

Remark The tensor product space H 1 H 2 is much larger than the set of tensor product vectors! S = { α β : α H 1, β H 2 } H 1 H 2 = span S Not every Ψ H 1 H 2 is the tensor product of two vectors: Ψ α β. As we will see, such vectors Ψ correspond to entangled states in quantum mechanics. PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 5 / 16

Definition (Tensor Product of Operators) If A is an operator on H 1 and B is an operator on H 2, then their tensor product A B is a linear operator on H 1 H 2 with action defined by d 1 d 2 d 2 A B c ij a i b j = c ij A a i B b j. j=1 j=1 If A and C are operators on H 1 and B and D are operators on H 2, then 1 A B + C D is an operator on H 1 H 2 with action (A B + C D) Ψ = A B Ψ + C D Ψ Ψ H1 H 2, 2 (A B)(C D) = AC BD. PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 6 / 16

Recall that any operator A on H 1 and any operator B on H 2 can be expressed as A = d 1 i,j=1 α ij a i a j, B = d 2 i,j=1 β ij b i b j, where { a i } d 1 is a basis for H 1 and { b j } d 2 j=1 is a basis for H 2. Likewise, any operator K on H 1 H 2 can be expressed as K = d 1 d 2 i,k=1 j,l=1 since { a i b j } d 1,d 2 i,j=1 is a basis for H 1 H 2. γ ij,kl a i a k b j b l PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 7 / 16

Example Let { 0, 1 } be the computational basis for C 2. Define the operator σ x to have action σ x 0 = 1 and σ x 1 = 0. What is the action of σ x σ x on an arbitrary Ψ C 2? Example Consider the operator F = 00 00 + 10 01 + 01 10 + 11 11 defined on C 2. What is its action on an arbitrary Ψ C 2? The operator F is called the SWAP operator. Remark The space of linear operators acting on H 1 H 2 is much larger than the set of tensor product operators! Not every operator on H 1 H 2 is the tensor product of two operators. For example F A B. PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 8 / 16

The matrix representation of tensor products is given in terms of the Kronecker matrix product. a 11 a 12 a 1d1 b 11 b 12 b 1d2 A =. a 21 a 22 a 2d1., B. b 21 b 22 b 2d2 =., a d1 1 a d1 d 1 b d2 1 b d2 d 2 a 11 b 11 a 11 b 12 a 12 b 11 a 12 b 12 a 1d1 b 1d2 A B =. a 11 b 21 a 11 b 22 a 12 b 11 a 12 b 12 a 1d1 b 2d2 a 21 b 11 a 21 b 12 a 22 b 11 a 22 b 12 a 2d1 b 1d2 a d1 1b d2 1 a d1 1b d2 2 a d1 2b d2 1 a d1 2b d2 2 a d1 d 1 b d2 d 2 PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 9 / 16

Example For ψ = a 0 + b 1 and φ = c 0 + d 1, what is the matrix representation of ψ φ? What is the matrix representation of ψ φ? Example What is the matrix representation of σ x σ x? Example What is the matrix representation of F? PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 10 / 16

The Schmidt Decomposition Theorem For any Ψ H 1 H 2, there exists an orthonormal basis { α i } d 1 H 1 and orthonormal basis { β j } d 2 j=1 for H 2 such that for Ψ = r λi α i β i. The numbers λ i > 0 are called the Schmidt coefficients of Ψ and the number r min{d 1, d 2 } is called the Schmidt rank of Ψ. The Schmidt coefficients are unique in the following sense: If Ψ = r λi α i β i = r λ i α i β i for two sets of orthonormal product bases { α i β j } i,j, { α i β j } i,j, and λ i, λ i > 0, then r = r and {λ i } r = {λ i }r. PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 11 / 16

The Schmidt Decomposition Any vector Ψ H 1 H 2 can be written as d 1 d 2 Ψ = c ij a i b j j=1 for any tensor product basis { a i b j } i,j. The Schmidt decomposition says there exists a special tensor product basis { α i β j } i,j such that Ψ = r λi α i β i. Example Give a Schmidt decomposition of the vector Ψ = 1/2[cos θ 00 + cos θ 01 + sin θe iφ 10 sin θe iφ 11 ]. PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 12 / 16

The Schmidt Decomposition - A Deeper Look So far we have only considered linear operators T that map a given vector space H onto itself; T : H H. In general, linear operators map one vector space H 2 onto another H 1 ; T : H 2 H 1. The set of all linear operators mapping H 2 into H 1 forms a vector space itself, denoted by L(H 1, H 2 ). The space L(H 1, H 2 ) can be made into an inner product space (in fact a Hilbert space) by defining the so-called Hilbert-Schmidt inner product: A, B := Tr[A B] for all A, B L(H 1, H 2 ). If { a i } d 1 is an orthonormal basis for H 1 and { b i } d 2 is an orthonormal basis for H 2, then { a i b j } d 1,d 2 i,j=1 is an orthonormal basis for L(H 1, H 2 ). PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 13 / 16

The Schmidt Decomposition - A Deeper Look Recall that one vector space V is isomorphic to another W, denoted by V = W, if there exists an invertible mapping f : V W such that f (aψ + bφ) = af (ψ) + bf (φ) ψ, φ V ; a, b C. Theorem (The First Isomorphism Theorem) L(H 1, H 2 ) = H 1 H 2. The isomorphic mapping f : L(H 1, H 2 ) H 1 H 2 is defined by d 1,d 2 d 1,d 2 f ( a i b j ) = a i b j f c ij a i b j = c ij a i b j i,j=1 for any orthonormal product basis { a i b j } i,j. i,j=1 PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 14 / 16

The Schmidt Decomposition - A Deeper Look Theorem (The Singular Value Decomposition (SVD)) For a given orthonormal basis { a i b j } i,j of L(H 1, H 2 ), any operator Ψ L(H 1, H 2 ) can be decomposed as Ψ = UΛV, where U is a unitary operator on H 1, V is a unitary operator on H 2, and Λ L(H 1, H 2 ) is diagonal in the basis { a i b j } i,j with unique non-negative diagonal elements λ i called the singular values of T. d 1 d 2 U = α i a i, V = β j b j, Λ = j=1 min{d 1,d 2 } λi a i b i. PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 15 / 16

The Schmidt Decomposition - A Deeper Look Ψ = UΛV = min{d 1,d 2 } λi α i β i = Apply isomorphic mapping f : r f (Ψ) = f λi α i β i = (λ i >0) r (λ i >0) r (λ i >0) λi α i β i. λi α i β i. The Schmidt Decomposition of Ψ H 1 H 2 is equivalent to the SVD of its corresponding operator Ψ L(H 1, H 2 ). The Schmidt coefficients of Ψ are the singular values of Ψ, and the Schmidt rank of Ψ is the rank of Ψ. PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 16 / 16