Notes on Quantum Computing

Size: px
Start display at page:

Download "Notes on Quantum Computing"

Transcription

1 Notes o Quatum Computig Maris Ozols May 0, 01 Cotets 1 Mathematics of quatum iformatio 1.1 Basics Bell basis, teleportatoi ad superdese codig Measuremets Decompositios ad ormal forms Pauli matrices ad Bloch sphere Elemetary circuit idetities Trace distace Fidelity ad Uhlma s theorem Quatum operatios Quatum gates, circuit model, ad uiversality Gives rotatios Elemetary quatum algorithms 7.1 Phase kickback Deutsch s algorithm Deutsch Jozsa algorithm Berstei Vazirai problem Simo s algorithm Quatum Fourier trasform ad phase estimatio 9 4 Shor s algorithm for factorig Period fidig Grover s quatum search algorithm 10 6 Computatioal complexity 10 7 Quatum error correctio ad fault tolerace Quatum error correctio The Shor code

2 8 Quatum iformatio theory ad basic commuicatio protocols Resource tradeoffs Nayak s boud Mathematics of quatum iformatio 1.1 Basics Bell basis, teleportatoi ad superdese codig Bell basis states: β xy = 0, y + ( 1x 1, ȳ (1 Preparatio of a Bell basis state (H o the first qubit, followed by CNOT: x H y (!!! p. 5 i NC!!! 1.1. Measuremets Geeral measuremet: p m = Tr(M m ρm m ρ m = M mρm m p m (3!!! POVM:!!! Fact (Priciple of deferred measuremet. Measuremets ca always be moved from a itermediate stage of a quatum circuit to the ed of the circuit. Ay classically cotrolled operatios that use the measuremet results ca be replaced by coditioal quatum operatios Decompositios ad ormal forms Fact. (A I φ = (I A T φ where φ = i i i is the maximally etagled state ad A M (C. This follows by projectig both sides o j k. Theorem (Schmidt decompositio. If ψ H A H B, the there exist orthoormal bases { i A } i ad { i B } i for H A ad H B, respectively, such that ψ = i λ i i A i B. (4 Note: oe ca take the first basis ad the coefficiets to be the eigevectors ad square roots of the eigevalues of the reduced state Tr B ( ψ ψ, respectively.

3 Theorem (Purificatio. If ρ is a mixed state o system A, the there is a system B ad a pure state ψ o AB such that ρ = Tr B ( ψ ψ. (5 Lemma (Geeral purificatio. If ψ is a purificatio of ρ, the it ca be writte i the form ψ = (ρ 1/ U φ (6 for some uitary U. Theorem (Polar decompositio. Ay A M (C ca be writte i the form A = UP = QU, (7 where P, Q 0, U U(. I particular, P = A A ad Q = AA Pauli matrices ad Bloch sphere Pauli matrices are: X = ( Y = ( 0 i i 0 Z = ( (8 Ay sigle qubit desity matrix ca be writte as ρ = 1 (I + r σ (9 Fact. If A = I, the e iθa = cos(θa + i si(θa = I cos θ + ia si θ. Fact. ( r σ = r I so r σ has eigevalues ± r. Fact. Rotatio aroud a uit vector r by agle α is give by e i α ( r σ = I cos α i( r σ si α. ( Elemetary circuit idetities = SWAP (11 = Z (1 Z = V U V V UV (13 H H = (14 H H 3

4 1. Trace distace Trace distace: For qubits: Tricks: D(p, q := 1 D(ρ, σ := 1 x X If P, Q 0, the Tr(P Q 0, p x q x = max(p(s q(s. (15 S X Tr ρ σ = max I P 0 Tr( P (ρ σ. (16 F (r 1, r = 1 r 1 r. (17 If I P 0 ad Q 0, the Tr(Q Tr(P Q, ρ σ = Q S, where Q, S 0 have orthogoal supports. Theorem. Let {E m } be a POVM. The where p m := Tr(ρE m, ad q m := Tr(ρE m. D(ρ, σ = max {E m} D({p m}, {q m }, ( Fidelity ad Uhlma s theorem Fidelity: F (p, q := x X px q x = p q (19 F (ρ, σ := Tr ρ 1/ σρ 1/ (0 A ad A have the same sigular values, therefore Tr A = Tr A. Fidelity is symmetric, sice For qubits: F (r 1, r := 1 F (ρ, σ = Tr ρ 1/ σρ 1/ (1 = Tr (σ 1/ ρ 1/ σ 1/ ρ 1/ ( = Tr σ 1/ ρ 1/ (3 = Tr (ρ 1/ σ 1/ (4 = Tr ρ 1/ σ 1/ (5 = F (σ, ρ. (6 ( 1 + r 1 r + (1 r 1 (1 r (7 4

5 Lemma. If A is ay operator ad U is uitary, the Tr(AU Tr A. Theorem (Uhlma. 1.4 Quatum operatios F (ρ, σ = max ψ ϕ. (8 ψ, ϕ Differet represetatios of a geeral quatum operatio E: 1. Stiesprig represetatio: E(ρ = Tr B ( U(ρ 0 0 U.. Kraus represetatio: E(ρ = k E kρe k, where k E k E k = I. 3. Choi-Jamio lkowski represetatio: E(ρ = Tr B ( JE (I ρ T, where J E := (E I( φ φ = ij E( i j i j. 4. Completely positive ad trace preservig: (E I(ρ 0 ad Tr E(ρ = Tr ρ for all ρ. How to covert betwee these represetatios: Stiesprig Kraus: E k := (I k U(I 0 Kraus Stiesprig: U(I 0 := k E k k Physical iterpretatio of Kraus represetatio: E(ρ = k p kρ k, where p k := Tr(E k ρe k ad ρ k := E kρe k Tr(E k ρe (9 k. 1.5 Quatum gates, circuit model, ad uiversality Theorem (Z-Y decompositio. For ay U U( there exist α, β, γ, δ R such that U = e iα R z (βr y (γr z (δ, where R k (θ = e i θ σ k. Theorem. For ay U U( there exist A, B, C U( such that ABC = I ad U = e iα AXBXC for some α R. Give such decompositio for U, we ca implemet c-u as follows: = ( e iα (30 U C B A Note that ( e iα = e iα I (31 5

6 If V = U the we ca implemet cc-u as follows: (3 = U V V V Note: the marked regio performs V cotrolled o XOR of the first two bits. More cotrols ca be added usig workspace qubits iitialized i 0 : = U U (33 We ca add more cotrols to Toffoli gate without imposig ay restrictios o the iitial state of the workspace: = w 1 w (34 Note: the marked gates act as follows: ivert w 1 if ad oly if the first bits are set to 1, ivert w if ad oly if the first 3 bits are set to Gives rotatios If α 0 ad β 0 the G(α,β {}}( { ( ( 1 α β α = α + β α + β β α β 0 (35 6

7 Note that G(α, β SU(. I geeral a two-level uitary α β G ij (α, β =... α + β β α... 1 (36 is called Gives rotatio. If U U( the for appropriately chose parameters α ad β we have ( 1 0 G 1... G 13 G 1 U = 0 U (37 where U U( 1. By recursively applyig this procedure we obtai a diagoal matrix of the form diag(1,..., 1, det U.!!! implemetig a -level uitary (Gray code!!! Elemetary quatum algorithms.1 Phase kickback If a {0, 1} the X a = ( 1 a. (38 If f : {0, 1} {0, 1} ad U f x y = x y f(x the. Deutsch s algorithm U f x = ( 1 f(x x. (39 Problem. Determie whether f : {0, 1} {0, 1} is costat or balaced. Equivaletly, compute f(0 f(1. Circuit. Aalysis. 0 H U f H 1 H H 1 + U f 1 (( 1 f(0 0 + ( 1 f(1 1 (40 (41 =( 1 f(0 1 ( 0 + ( 1 f(0 f(1 1 }{{} (4 + or depedig o f(0 f(1 7

8 .3 Deutsch Jozsa algorithm Problem. Determie whether f : {0, 1} {0, 1} is costat or balaced. Circuit. Aalysis. Recall the formula: 0 H U f H 1 H H 1 (43 H x = 1 1 ( 1 x y y (44 y=0 We have + = 1 U f 1 H (+1 1 x (45 x {0,1} ( 1 f(x x (46 x {0,1} x,y {0,1} ( 1 x y+f(x y 1 (47 The amplitude for y = 0 is give by 1 ( 1 f(x = x {0,1} { ±1 if f is costat 0 if f is balaced (48.4 Berstei Vazirai problem Problem. Determie s {0, 1} by queryig f : {0, 1} {0, 1} give by f( x = s x = s 1 x 1 s x s x (49 Circuit. 0 H U f H (50 1 H H 1 Aalysis. Apply the Hadamard trasform formula i the backward directio: + U f 1 H (+1 1 x {0,1} ( 1 x s x (51 x,y {0,1} ( 1 x ( y+ s y 1 (5 The amplitude for y = s is clearly 1, so the other amplitudes must be 0. 8

9 .5 Simo s algorithm 3 Quatum Fourier trasform ad phase estimatio Let y/ = l=1 y l l = 0.y 1 y... y. The QFT x = 1 1 e πixy/ y (53 = 1 = y=0 y {0,1} e πix ( 0 + e πix l 1 l=1 4 Shor s algorithm for factorig 4.1 Period fidig l=1 y l l y (54 f : {0, 1,..., 1} {0, 1} m. Promise: f(x = f(y x y (mod b. ( m QFT 1 1 x 0 (56 x=0 U f 1 1 x f(x (57 If we get outcome f(x 0 after measurig the d register we get, the state that is left over is k 1 1 x 0 + jr, (58 k j=0 x=0 where k { r, r }. Applyig QFT 1 we get 1 k 1 k 1 y=0 j=0 e πi (x0+jr y. (59 If r, the Pr(y = 1 si πyrk k si πyr (60 9

10 5 Grover s quatum search algorithm 6 Computatioal complexity I what follows DTM stads for a Determiistic Turig Machie. Defiitio. A promise problem is a pair A = (A yes, A o, where A yes, A o {0, 1} ad A yes A o =. Compute Verify Determiistic P, PSPACE NP Probabilistic BPP, PP MA Quatum BQP, BPP QMA Table 1: The success probabilities of umerical QRACs.!!! PICTURE!!! Most of the followig defiitios start with A promise problem A = (A yes, A o is i [complexity class] if ad oly if.... Defiitio (P.... there exists a DTM M that rus i polyomial time such that x A yes : M (x = 1, x A o : M (x = 0. Defiitio (PSPACE. Similar to P, except M rus i polyomial space. Defiitio (NP.... there exists a DTM M that rus i polyomial time ad a polyomial p such that x A yes y {0, 1} p( x : M (x, y = 1 (completeess, x A o y {0, 1} p( x : M (x, y = 0 (soudess. Defiitio (PP.... there exists a DTM M that rus i polyomial time ad a polyomial p such that x A yes : {r {0, 1} p( x : M (x, r = 1} / p( x > 1, x A o : {r {0, 1} p( x : M (x, r = 1} / p( x 1. Defiitio (BPP. Similar to PP, except the probabilities are... 3 ad 1 3, respectively. Defiitio (MA.... there exists a DTM M that rus i polyomial time ad polyomials p ad q such that x A yes y {0, 1} p( x : {r {0, 1} q( x : M (x, y, r = 1} 3 (completeess, 10

11 x A o y {0, 1} p( x : {r {0, 1} q( x : M (x, y, r = 1} 1 3 (soudess. Defiitio (BQP.... there exists a polyomial-time geerated family of quatum circuits Q = {Q : N}, where each circuit Q takes iput qubits ad produces oe output qubit, such that x A yes : Pr[Q x accepts x] 3, x A o : Pr[Q x accepts x] 1 3. Defiitio (QMA.... there exists a polyomial-time geerated family of quatum circuits Q = {Q : N}, where each circuit Q takes + p( iput qubits ad produces oe output qubit, such that x A yes ρ D( p( x : Pr[Q x accepts (x, ρ] 3 (completeess, x A o ρ D( p( x : Pr[Q x accepts (x, ρ] 1 3 (soudess, where D(d stads for the set of all d d desity matrices. 7 Quatum error correctio ad fault tolerace Actio via cojugatio: H : X Z H : Z X H : Y Y (61 S : X Y S : Y X S : Z Z (6 7.1 Quatum error correctio CNOT : X I X X (63 CNOT : I X I X (64 CNOT : Z I Z I (65 CNOT : I Z Z Z (66 Theorem (Quatum error-correctio coditio. Let C be a quatum code ad Π C the projector oto the code subspace, ad E = {E i } a quatum operatio. A operatio for correctig E o C exists if ad oly if for some Hermitia matrix α. Π C E i E jπ C = α ij Π C (67 Theorem (Discretizatio of errors. Let C be a quatum code ad R be the error-correctio operatio to recover from E = {E i } costructed i the proof of the previous theorem. If F = {F j } where F j = i m jie i for some complex matrix m, the R also corrects for F o the code C. 11

12 7.1.1 The Shor code ( = 0 L ( ( = 1 L (69 8 Quatum iformatio theory ad basic commuicatio protocols 8.1 Resource tradeoffs Let x {0, 1}. The ability to perform the correspodig trasformatio for ay basis vector is a resource: qubit: x A x B, cbit: x A x B x E, cobit: x A x A x B. Trivial iequalities: 1 qubit 1 cobit 1 cbit. (70 (Alice ca perform a CNOT to create a coheret copy of the state i stadard basis, ad sed oe half to Bob. Alice ca discard her half of the coheret bit to get a classical bit. Ability to trasmit coheret bits ca be used to geerate etaglemet by usig + as iput: Irreversible trasformatios: 1 qubit + 1 ebit cbits (superdese codig, cbits + 1 ebit 1 qubit (teleportatio. Reversible trasformatios give a catalyst ebit: (1 qubit + 1 ebit ( cobits, 1 cobit 1 ebit. (71 ( cobits + 1 ebit (1 qubit + 1 ebit + 1 ebit (sed over a coheret copy without measurig it. (Before ruig the superdese codig protocol, Alice makes local copies of her two classical bits; this does ot require the catalyst ebit. Alice performs a uitary that maps Bell basis to stadard basis (see circuit i Eq. ( o the qubit i a ukow state ad her half of the ebit; istead of measurig ad trasmittig two classical bits, she uses coheret commuicatio; coditioal o the two received coheret bits, Bob corrects his half of the ebit; they ed up 1

13 geeratig two ebits from the coheret commuicatio, ad Bob also eds up havig the ukow state. Coclusio: ( cobits + 1 ebit = (1 qubit + 1 ebit + 1 ebit (7 where the ebit is used as a catalyst. 8. Nayak s boud Theorem. If X {0, 1} m is draw uiformly at radom, ecoded i qubits, ad recovered to Y, the probability that X = Y is at most / m. Proof. Let {Π x : x {0, 1} m } be a orthoormal measuremet i (C, i.e., a set of projectors that sum to idetity, ad φ x (C the ecodig of x. The Pr[X = Y ] = 1 m Π x φ x x {0,1} m (73 = 1 ( m Tr Πx φ x φ x x {0,1} m (74 1 m Tr(Π x Π C x {0,1} m (75 = 1 m Tr Π C (76 = m. (77 13

Quantum Information & Quantum Computation

Quantum Information & Quantum Computation CS9A, Sprig 5: Quatum Iformatio & Quatum Computatio Wim va Dam Egieerig, Room 59 vadam@cs http://www.cs.ucsb.edu/~vadam/teachig/cs9/ Admiistrivia Do the exercises. Aswers will be posted at the ed of the

More information

Quantum Computing Lecture 7. Quantum Factoring

Quantum Computing Lecture 7. Quantum Factoring Quatum Computig Lecture 7 Quatum Factorig Maris Ozols Quatum factorig A polyomial time quatum algorithm for factorig umbers was published by Peter Shor i 1994. Polyomial time meas that the umber of gates

More information

Last time, we talked about how Equation (1) can simulate Equation (2). We asserted that Equation (2) can also simulate Equation (1).

Last time, we talked about how Equation (1) can simulate Equation (2). We asserted that Equation (2) can also simulate Equation (1). 6896 Quatum Complexity Theory Sept 23, 2008 Lecturer: Scott Aaroso Lecture 6 Last Time: Quatum Error-Correctio Quatum Query Model Deutsch-Jozsa Algorithm (Computes x y i oe query) Today: Berstei-Vazirii

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION DOI: 10.1038/NPHYS309 O the reality of the quatum state Matthew F. Pusey, 1, Joatha Barrett, ad Terry Rudolph 1 1 Departmet of Physics, Imperial College Lodo, Price Cosort Road, Lodo SW7 AZ, Uited Kigdom

More information

Problem Set # 5 Solutions

Problem Set # 5 Solutions MIT./8.4/6.898/8.435 Quatum Iformatio Sciece I Fall, 00 Sam Ocko October 5, 00 Problem Set # 5 Solutios. Most uitar trasforms are hard to approimate. (a) We are dealig with boolea fuctios that take bits

More information

Analysis of Deutsch-Jozsa Quantum Algorithm

Analysis of Deutsch-Jozsa Quantum Algorithm Aalysis of Deutsch-Jozsa Quatum Algorithm Zhegju Cao Jeffrey Uhlma Lihua Liu 3 Abstract. Deutsch-Jozsa quatum algorithm is of great importace to quatum computatio. It directly ispired Shor s factorig algorithm.

More information

Quantum Simulation: Solving Schrödinger Equation on a Quantum Computer

Quantum Simulation: Solving Schrödinger Equation on a Quantum Computer Purdue Uiversity Purdue e-pubs Birc Poster Sessios Birc Naotechology Ceter 4-14-008 Quatum Simulatio: Solvig Schrödiger Equatio o a Quatum Computer Hefeg Wag Purdue Uiversity, wag10@purdue.edu Sabre Kais

More information

Notes for Lecture 5. 1 Grover Search. 1.1 The Setting. 1.2 Motivation. Lecture 5 (September 26, 2018)

Notes for Lecture 5. 1 Grover Search. 1.1 The Setting. 1.2 Motivation. Lecture 5 (September 26, 2018) COS 597A: Quatum Cryptography Lecture 5 (September 6, 08) Lecturer: Mark Zhadry Priceto Uiversity Scribe: Fermi Ma Notes for Lecture 5 Today we ll move o from the slightly cotrived applicatios of quatum

More information

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j.

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j. Eigevalue-Eigevector Istructor: Nam Su Wag eigemcd Ay vector i real Euclidea space of dimesio ca be uiquely epressed as a liear combiatio of liearly idepedet vectors (ie, basis) g j, j,,, α g α g α g α

More information

C191 - Lecture 2 - Quantum states and observables

C191 - Lecture 2 - Quantum states and observables C191 - Lecture - Quatum states ad observables I ENTANGLED STATES We saw last time that quatum mechaics allows for systems to be i superpositios of basis states May of these superpositios possess a uiquely

More information

C/CS/Phys C191 Deutsch and Deutsch-Josza algorithms 10/20/07 Fall 2007 Lecture 17

C/CS/Phys C191 Deutsch and Deutsch-Josza algorithms 10/20/07 Fall 2007 Lecture 17 C/CS/Phs C9 Deutsch ad Deutsch-Josza algorithms 0/0/07 Fall 007 Lecture 7 Readigs Beeti et al., Ch. 3.9-3.9. Stolze ad Suter, Quatum Computig, Ch. 8. - 8..5) Nielse ad Chuag, Quatum Computatio ad Quatum

More information

Lecture 11: Pseudorandom functions

Lecture 11: Pseudorandom functions COM S 6830 Cryptography Oct 1, 2009 Istructor: Rafael Pass 1 Recap Lecture 11: Pseudoradom fuctios Scribe: Stefao Ermo Defiitio 1 (Ge, Ec, Dec) is a sigle message secure ecryptio scheme if for all uppt

More information

24 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS

24 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS 24 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS Corollary 2.30. Suppose that the semisimple decompositio of the G- module V is V = i S i. The i = χ V,χ i Proof. Sice χ V W = χ V + χ W, we have:

More information

Quantum Computing - A new Implementation of Simon Algorithm for 3-Dimensional Registers

Quantum Computing - A new Implementation of Simon Algorithm for 3-Dimensional Registers Joural of Applied Computer Sciece & Mathematics, o. 9 (9) /5, Suceava Quatum Computig - A ew Implemetatio of Simo Algorithm for -Dimesioal Registers Adia BĂRÎLĂ Ștefa cel Mare Uiversity of Suceava, Romaia

More information

Linear regression. Daniel Hsu (COMS 4771) (y i x T i β)2 2πσ. 2 2σ 2. 1 n. (x T i β y i ) 2. 1 ˆβ arg min. β R n d

Linear regression. Daniel Hsu (COMS 4771) (y i x T i β)2 2πσ. 2 2σ 2. 1 n. (x T i β y i ) 2. 1 ˆβ arg min. β R n d Liear regressio Daiel Hsu (COMS 477) Maximum likelihood estimatio Oe of the simplest liear regressio models is the followig: (X, Y ),..., (X, Y ), (X, Y ) are iid radom pairs takig values i R d R, ad Y

More information

Lecture 8: October 20, Applications of SVD: least squares approximation

Lecture 8: October 20, Applications of SVD: least squares approximation Mathematical Toolkit Autum 2016 Lecturer: Madhur Tulsiai Lecture 8: October 20, 2016 1 Applicatios of SVD: least squares approximatio We discuss aother applicatio of sigular value decompositio (SVD) of

More information

(VII.A) Review of Orthogonality

(VII.A) Review of Orthogonality VII.A Review of Orthogoality At the begiig of our study of liear trasformatios i we briefly discussed projectios, rotatios ad projectios. I III.A, projectios were treated i the abstract ad without regard

More information

Session 5. (1) Principal component analysis and Karhunen-Loève transformation

Session 5. (1) Principal component analysis and Karhunen-Loève transformation 200 Autum semester Patter Iformatio Processig Topic 2 Image compressio by orthogoal trasformatio Sessio 5 () Pricipal compoet aalysis ad Karhue-Loève trasformatio Topic 2 of this course explais the image

More information

5.74 TIME-DEPENDENT QUANTUM MECHANICS

5.74 TIME-DEPENDENT QUANTUM MECHANICS p. 1 5.74 TIME-DEPENDENT QUANTUM MECHANICS The time evolutio of the state of a system is described by the time-depedet Schrödiger equatio (TDSE): i t ψ( r, t)= H ˆ ψ( r, t) Most of what you have previously

More information

Physics 324, Fall Dirac Notation. These notes were produced by David Kaplan for Phys. 324 in Autumn 2001.

Physics 324, Fall Dirac Notation. These notes were produced by David Kaplan for Phys. 324 in Autumn 2001. Physics 324, Fall 2002 Dirac Notatio These otes were produced by David Kapla for Phys. 324 i Autum 2001. 1 Vectors 1.1 Ier product Recall from liear algebra: we ca represet a vector V as a colum vector;

More information

Introduction to Quantum Computing

Introduction to Quantum Computing Introduction to Quantum Computing Petros Wallden Lecture 7: Complexity & Algorithms I 13th October 016 School of Informatics, University of Edinburgh Complexity - Computational Complexity: Classification

More information

Nonlinear regression

Nonlinear regression oliear regressio How to aalyse data? How to aalyse data? Plot! How to aalyse data? Plot! Huma brai is oe the most powerfull computatioall tools Works differetly tha a computer What if data have o liear

More information

PRELIM PROBLEM SOLUTIONS

PRELIM PROBLEM SOLUTIONS PRELIM PROBLEM SOLUTIONS THE GRAD STUDENTS + KEN Cotets. Complex Aalysis Practice Problems 2. 2. Real Aalysis Practice Problems 2. 4 3. Algebra Practice Problems 2. 8. Complex Aalysis Practice Problems

More information

Singular value decomposition. Mathématiques appliquées (MATH0504-1) B. Dewals, Ch. Geuzaine

Singular value decomposition. Mathématiques appliquées (MATH0504-1) B. Dewals, Ch. Geuzaine Lecture 11 Sigular value decompositio Mathématiques appliquées (MATH0504-1) B. Dewals, Ch. Geuzaie V1.2 07/12/2018 1 Sigular value decompositio (SVD) at a glace Motivatio: the image of the uit sphere S

More information

6. Kalman filter implementation for linear algebraic equations. Karhunen-Loeve decomposition

6. Kalman filter implementation for linear algebraic equations. Karhunen-Loeve decomposition 6. Kalma filter implemetatio for liear algebraic equatios. Karhue-Loeve decompositio 6.1. Solvable liear algebraic systems. Probabilistic iterpretatio. Let A be a quadratic matrix (ot obligatory osigular.

More information

NBHM QUESTION 2007 Section 1 : Algebra Q1. Let G be a group of order n. Which of the following conditions imply that G is abelian?

NBHM QUESTION 2007 Section 1 : Algebra Q1. Let G be a group of order n. Which of the following conditions imply that G is abelian? NBHM QUESTION 7 NBHM QUESTION 7 NBHM QUESTION 7 Sectio : Algebra Q Let G be a group of order Which of the followig coditios imply that G is abelia? 5 36 Q Which of the followig subgroups are ecesarily

More information

5.1 Review of Singular Value Decomposition (SVD)

5.1 Review of Singular Value Decomposition (SVD) MGMT 69000: Topics i High-dimesioal Data Aalysis Falll 06 Lecture 5: Spectral Clusterig: Overview (cotd) ad Aalysis Lecturer: Jiamig Xu Scribe: Adarsh Barik, Taotao He, September 3, 06 Outlie Review of

More information

Topics in Eigen-analysis

Topics in Eigen-analysis Topics i Eige-aalysis Li Zajiag 28 July 2014 Cotets 1 Termiology... 2 2 Some Basic Properties ad Results... 2 3 Eige-properties of Hermitia Matrices... 5 3.1 Basic Theorems... 5 3.2 Quadratic Forms & Noegative

More information

LECTURE 8: ORTHOGONALITY (CHAPTER 5 IN THE BOOK)

LECTURE 8: ORTHOGONALITY (CHAPTER 5 IN THE BOOK) LECTURE 8: ORTHOGONALITY (CHAPTER 5 IN THE BOOK) Everythig marked by is ot required by the course syllabus I this lecture, all vector spaces is over the real umber R. All vectors i R is viewed as a colum

More information

Iterative method for computing a Schur form of symplectic matrix

Iterative method for computing a Schur form of symplectic matrix Aals of the Uiversity of Craiova, Mathematics ad Computer Sciece Series Volume 421, 2015, Pages 158 166 ISSN: 1223-6934 Iterative method for computig a Schur form of symplectic matrix A Mesbahi, AH Betbib,

More information

Entropies & Information Theory

Entropies & Information Theory Etropies & Iformatio Theory LECTURE I Nilajaa Datta Uiversity of Cambridge,U.K. For more details: see lecture otes (Lecture 1- Lecture 5) o http://www.qi.damtp.cam.ac.uk/ode/223 Quatum Iformatio Theory

More information

Assignment 2 Solutions SOLUTION. ϕ 1 Â = 3 ϕ 1 4i ϕ 2. The other case can be dealt with in a similar way. { ϕ 2 Â} χ = { 4i ϕ 1 3 ϕ 2 } χ.

Assignment 2 Solutions SOLUTION. ϕ 1  = 3 ϕ 1 4i ϕ 2. The other case can be dealt with in a similar way. { ϕ 2 Â} χ = { 4i ϕ 1 3 ϕ 2 } χ. PHYSICS 34 QUANTUM PHYSICS II (25) Assigmet 2 Solutios 1. With respect to a pair of orthoormal vectors ϕ 1 ad ϕ 2 that spa the Hilbert space H of a certai system, the operator  is defied by its actio

More information

Algebra of Least Squares

Algebra of Least Squares October 19, 2018 Algebra of Least Squares Geometry of Least Squares Recall that out data is like a table [Y X] where Y collects observatios o the depedet variable Y ad X collects observatios o the k-dimesioal

More information

Quantum Circuits with Unbounded Fan-out

Quantum Circuits with Unbounded Fan-out Quatum Circuits with Ubouded Fa-out Peter øyer 1, ad Robert Špalek, 1 Dept. of Comp. Sci., Uiv. of Calgary, AB, Caada. hoyer@cpsc.ucalgary.ca Cetrum voor Wiskude e Iformatica, Amsterdam, The Netherlads.

More information

Math 778S Spectral Graph Theory Handout #3: Eigenvalues of Adjacency Matrix

Math 778S Spectral Graph Theory Handout #3: Eigenvalues of Adjacency Matrix Math 778S Spectral Graph Theory Hadout #3: Eigevalues of Adjacecy Matrix The Cartesia product (deoted by G H) of two simple graphs G ad H has the vertex-set V (G) V (H). For ay u, v V (G) ad x, y V (H),

More information

Inverse Matrix. A meaning that matrix B is an inverse of matrix A.

Inverse Matrix. A meaning that matrix B is an inverse of matrix A. Iverse Matrix Two square matrices A ad B of dimesios are called iverses to oe aother if the followig holds, AB BA I (11) The otio is dual but we ofte write 1 B A meaig that matrix B is a iverse of matrix

More information

ON THE EXISTENCE OF A GROUP ORTHONORMAL BASIS. Peter Zizler (Received 20 June, 2014)

ON THE EXISTENCE OF A GROUP ORTHONORMAL BASIS. Peter Zizler (Received 20 June, 2014) NEW ZEALAND JOURNAL OF MATHEMATICS Volume 45 (2015, 45-52 ON THE EXISTENCE OF A GROUP ORTHONORMAL BASIS Peter Zizler (Received 20 Jue, 2014 Abstract. Let G be a fiite group ad let l 2 (G be a fiite dimesioal

More information

ECE 564/645 - Digital Communication Systems (Spring 2014) Final Exam Friday, May 2nd, 8:00-10:00am, Marston 220

ECE 564/645 - Digital Communication Systems (Spring 2014) Final Exam Friday, May 2nd, 8:00-10:00am, Marston 220 ECE 564/645 - Digital Commuicatio Systems (Sprig 014) Fial Exam Friday, May d, 8:00-10:00am, Marsto 0 Overview The exam cosists of four (or five) problems for 100 (or 10) poits. The poits for each part

More information

CS284A: Representations and Algorithms in Molecular Biology

CS284A: Representations and Algorithms in Molecular Biology CS284A: Represetatios ad Algorithms i Molecular Biology Scribe Notes o Lectures 3 & 4: Motif Discovery via Eumeratio & Motif Represetatio Usig Positio Weight Matrix Joshua Gervi Based o presetatios by

More information

Lecture 2: Uncomputability and the Haling Problem

Lecture 2: Uncomputability and the Haling Problem CSE 200 Computability ad Complexity Wedesday, April 3, 2013 Lecture 2: Ucomputability ad the Halig Problem Istructor: Professor Shachar Lovett Scribe: Dogcai She 1 The Uiversal Turig Machie I the last

More information

Multiple Groenewold Products: from path integrals to semiclassical correlations

Multiple Groenewold Products: from path integrals to semiclassical correlations Multiple Groeewold Products: from path itegrals to semiclassical correlatios 1. Traslatio ad reflectio bases for operators Traslatio operators, correspod to classical traslatios, withi the classical phase

More information

PAPER : IIT-JAM 2010

PAPER : IIT-JAM 2010 MATHEMATICS-MA (CODE A) Q.-Q.5: Oly oe optio is correct for each questio. Each questio carries (+6) marks for correct aswer ad ( ) marks for icorrect aswer.. Which of the followig coditios does NOT esure

More information

Chapter 9 Computer Design Basics

Chapter 9 Computer Design Basics Logic ad Computer Desig Fudametals Chapter 9 Computer Desig Basics Part 1 Datapaths Overview Part 1 Datapaths Itroductio Datapath Example Arithmetic Logic Uit (ALU) Shifter Datapath Represetatio Cotrol

More information

For a 3 3 diagonal matrix we find. Thus e 1 is a eigenvector corresponding to eigenvalue λ = a 11. Thus matrix A has eigenvalues 2 and 3.

For a 3 3 diagonal matrix we find. Thus e 1 is a eigenvector corresponding to eigenvalue λ = a 11. Thus matrix A has eigenvalues 2 and 3. Closed Leotief Model Chapter 6 Eigevalues I a closed Leotief iput-output-model cosumptio ad productio coicide, i.e. V x = x = x Is this possible for the give techology matrix V? This is a special case

More information

Matrix Algebra from a Statistician s Perspective BIOS 524/ Scalar multiple: ka

Matrix Algebra from a Statistician s Perspective BIOS 524/ Scalar multiple: ka Matrix Algebra from a Statisticia s Perspective BIOS 524/546. Matrices... Basic Termiology a a A = ( aij ) deotes a m matrix of values. Whe =, this is a am a m colum vector. Whe m= this is a row vector..2.

More information

A meta-converse for private communication over quantum channels

A meta-converse for private communication over quantum channels A meta-coverse for private commuicatio over quatum chaels Mario Berta with Mark M. Wilde ad Marco Tomamichel IEEE Trasactios o Iformatio Theory, 63(3), 1792 1817 (2017) Beyod IID Sigapore - July 17, 2017

More information

6. Sufficient, Complete, and Ancillary Statistics

6. Sufficient, Complete, and Ancillary Statistics Sufficiet, Complete ad Acillary Statistics http://www.math.uah.edu/stat/poit/sufficiet.xhtml 1 of 7 7/16/2009 6:13 AM Virtual Laboratories > 7. Poit Estimatio > 1 2 3 4 5 6 6. Sufficiet, Complete, ad Acillary

More information

5.1. The Rayleigh s quotient. Definition 49. Let A = A be a self-adjoint matrix. quotient is the function. R(x) = x,ax, for x = 0.

5.1. The Rayleigh s quotient. Definition 49. Let A = A be a self-adjoint matrix. quotient is the function. R(x) = x,ax, for x = 0. 40 RODICA D. COSTIN 5. The Rayleigh s priciple ad the i priciple for the eigevalues of a self-adjoit matrix Eigevalues of self-adjoit matrices are easy to calculate. This sectio shows how this is doe usig

More information

Lecture 9: Hierarchy Theorems

Lecture 9: Hierarchy Theorems IAS/PCMI Summer Sessio 2000 Clay Mathematics Udergraduate Program Basic Course o Computatioal Complexity Lecture 9: Hierarchy Theorems David Mix Barrigto ad Alexis Maciel July 27, 2000 Most of this lecture

More information

arxiv:quant-ph/ v1 10 Oct 2002

arxiv:quant-ph/ v1 10 Oct 2002 A Quatum Radom Walk Search Algorithm Neil Shevi 1, Julia Kempe 1,,3, ad K. Birgitta Whaley 1 Departmets of Chemistry 1 ad Computer Sciece, Uiversity of Califoria, Berkeley, CA 9470 3 CNRS-LRI, UMR 863,

More information

The z-transform. 7.1 Introduction. 7.2 The z-transform Derivation of the z-transform: x[n] = z n LTI system, h[n] z = re j

The z-transform. 7.1 Introduction. 7.2 The z-transform Derivation of the z-transform: x[n] = z n LTI system, h[n] z = re j The -Trasform 7. Itroductio Geeralie the complex siusoidal represetatio offered by DTFT to a represetatio of complex expoetial sigals. Obtai more geeral characteristics for discrete-time LTI systems. 7.

More information

Orthogonal transformations

Orthogonal transformations Orthogoal trasformatios October 12, 2014 1 Defiig property The squared legth of a vector is give by takig the dot product of a vector with itself, v 2 v v g ij v i v j A orthogoal trasformatio is a liear

More information

Factor Analysis. Lecture 10: Factor Analysis and Principal Component Analysis. Sam Roweis

Factor Analysis. Lecture 10: Factor Analysis and Principal Component Analysis. Sam Roweis Lecture 10: Factor Aalysis ad Pricipal Compoet Aalysis Sam Roweis February 9, 2004 Whe we assume that the subspace is liear ad that the uderlyig latet variable has a Gaussia distributio we get a model

More information

Quantum Lower Bounds for the Goldreich-Levin Problem

Quantum Lower Bounds for the Goldreich-Levin Problem Quatum Lower Bouds for the Goldreich-Levi Problem Mark Adcock 1, Richard Cleve 1, Kazuo Iwama, Raymod Putra,4, Shigeru Yamashita 3, 1 Departmet of Computer Sciece, Uiversity of Calgary, Caada Graduate

More information

arxiv: v1 [math.fa] 3 Apr 2016

arxiv: v1 [math.fa] 3 Apr 2016 Aticommutator Norm Formula for Proectio Operators arxiv:164.699v1 math.fa] 3 Apr 16 Sam Walters Uiversity of Norther British Columbia ABSTRACT. We prove that for ay two proectio operators f, g o Hilbert

More information

PC5215 Numerical Recipes with Applications - Review Problems

PC5215 Numerical Recipes with Applications - Review Problems PC55 Numerical Recipes with Applicatios - Review Problems Give the IEEE 754 sigle precisio bit patter (biary or he format) of the followig umbers: 0 0 05 00 0 00 Note that it has 8 bits for the epoet,

More information

Building an NMR Quantum Computer

Building an NMR Quantum Computer Buildig a NMR Quatum Computer Spi, the Ster-Gerlach Experimet, ad the Bloch Sphere Kevi Youg Berkeley Ceter for Quatum Iformatio ad Computatio, Uiversity of Califoria, Berkeley, CA 9470 Scalable ad Secure

More information

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + 62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of

More information

Machine Learning for Data Science (CS 4786)

Machine Learning for Data Science (CS 4786) Machie Learig for Data Sciece CS 4786) Lecture & 3: Pricipal Compoet Aalysis The text i black outlies high level ideas. The text i blue provides simple mathematical details to derive or get to the algorithm

More information

MATH10212 Linear Algebra B Proof Problems

MATH10212 Linear Algebra B Proof Problems MATH22 Liear Algebra Proof Problems 5 Jue 26 Each problem requests a proof of a simple statemet Problems placed lower i the list may use the results of previous oes Matrices ermiats If a b R the matrix

More information

Signal Processing in Mechatronics

Signal Processing in Mechatronics Sigal Processig i Mechatroics Zhu K.P. AIS, UM. Lecture, Brief itroductio to Sigals ad Systems, Review of Liear Algebra ad Sigal Processig Related Mathematics . Brief Itroductio to Sigals What is sigal

More information

Department of Mathematics

Department of Mathematics Departmet of Mathematics Ma 3/103 KC Border Itroductio to Probability ad Statistics Witer 2017 Lecture 19: Estimatio II Relevat textbook passages: Larse Marx [1]: Sectios 5.2 5.7 19.1 The method of momets

More information

Geometry of LS. LECTURE 3 GEOMETRY OF LS, PROPERTIES OF σ 2, PARTITIONED REGRESSION, GOODNESS OF FIT

Geometry of LS. LECTURE 3 GEOMETRY OF LS, PROPERTIES OF σ 2, PARTITIONED REGRESSION, GOODNESS OF FIT OCTOBER 7, 2016 LECTURE 3 GEOMETRY OF LS, PROPERTIES OF σ 2, PARTITIONED REGRESSION, GOODNESS OF FIT Geometry of LS We ca thik of y ad the colums of X as members of the -dimesioal Euclidea space R Oe ca

More information

Machine Learning Brett Bernstein

Machine Learning Brett Bernstein Machie Learig Brett Berstei Week 2 Lecture: Cocept Check Exercises Starred problems are optioal. Excess Risk Decompositio 1. Let X = Y = {1, 2,..., 10}, A = {1,..., 10, 11} ad suppose the data distributio

More information

Tridiagonal reduction redux

Tridiagonal reduction redux Week 15: Moday, Nov 27 Wedesday, Nov 29 Tridiagoal reductio redux Cosider the problem of computig eigevalues of a symmetric matrix A that is large ad sparse Usig the grail code i LAPACK, we ca compute

More information

On the Determinants and Inverses of Skew Circulant and Skew Left Circulant Matrices with Fibonacci and Lucas Numbers

On the Determinants and Inverses of Skew Circulant and Skew Left Circulant Matrices with Fibonacci and Lucas Numbers WSEAS TRANSACTIONS o MATHEMATICS Yu Gao Zhaoli Jiag Yapeg Gog O the Determiats ad Iverses of Skew Circulat ad Skew Left Circulat Matrices with Fiboacci ad Lucas Numbers YUN GAO Liyi Uiversity Departmet

More information

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a Math E-2b Lecture #8 Notes This week is all about determiats. We ll discuss how to defie them, how to calculate them, lear the allimportat property kow as multiliearity, ad show that a square matrix A

More information

Optimum LMSE Discrete Transform

Optimum LMSE Discrete Transform Image Trasformatio Two-dimesioal image trasforms are extremely importat areas of study i image processig. The image output i the trasformed space may be aalyzed, iterpreted, ad further processed for implemetig

More information

Symmetric Matrices and Quadratic Forms

Symmetric Matrices and Quadratic Forms 7 Symmetric Matrices ad Quadratic Forms 7.1 DIAGONALIZAION OF SYMMERIC MARICES SYMMERIC MARIX A symmetric matrix is a matrix A such that. A = A Such a matrix is ecessarily square. Its mai diagoal etries

More information

Maths /2014. CCP Maths 2. Reduction, projector,endomorphism of rank 1... Hadamard s inequality and some applications. Solution.

Maths /2014. CCP Maths 2. Reduction, projector,endomorphism of rank 1... Hadamard s inequality and some applications. Solution. CCP Maths 2 Reductio, projector,edomorphism of rak 1... Hadamard s iequality ad some applicatios Solutio Exercise 1. 1 A is a symmetric matrix so diagoalizable. 2 Diagoalizatio of A : A characteristic

More information

IIT JAM Mathematical Statistics (MS) 2006 SECTION A

IIT JAM Mathematical Statistics (MS) 2006 SECTION A IIT JAM Mathematical Statistics (MS) 6 SECTION A. If a > for ad lim a / L >, the which of the followig series is ot coverget? (a) (b) (c) (d) (d) = = a = a = a a + / a lim a a / + = lim a / a / + = lim

More information

ACO Comprehensive Exam 9 October 2007 Student code A. 1. Graph Theory

ACO Comprehensive Exam 9 October 2007 Student code A. 1. Graph Theory 1. Graph Theory Prove that there exist o simple plaar triagulatio T ad two distict adjacet vertices x, y V (T ) such that x ad y are the oly vertices of T of odd degree. Do ot use the Four-Color Theorem.

More information

Computability and computational complexity

Computability and computational complexity Computability ad computatioal complexity Lecture 4: Uiversal Turig machies. Udecidability Io Petre Computer Sciece, Åbo Akademi Uiversity Fall 2015 http://users.abo.fi/ipetre/computability/ 21. toukokuu

More information

Unbiased Estimation. February 7-12, 2008

Unbiased Estimation. February 7-12, 2008 Ubiased Estimatio February 7-2, 2008 We begi with a sample X = (X,..., X ) of radom variables chose accordig to oe of a family of probabilities P θ where θ is elemet from the parameter space Θ. For radom

More information

7.1 Convergence of sequences of random variables

7.1 Convergence of sequences of random variables Chapter 7 Limit Theorems Throughout this sectio we will assume a probability space (, F, P), i which is defied a ifiite sequece of radom variables (X ) ad a radom variable X. The fact that for every ifiite

More information

Matrix Algebra 2.2 THE INVERSE OF A MATRIX Pearson Education, Inc.

Matrix Algebra 2.2 THE INVERSE OF A MATRIX Pearson Education, Inc. 2 Matrix Algebra 2.2 THE INVERSE OF A MATRIX MATRIX OPERATIONS A matrix A is said to be ivertible if there is a matrix C such that CA = I ad AC = I where, the idetity matrix. I = I I this case, C is a

More information

Goal. Adaptive Finite Element Methods for Non-Stationary Convection-Diffusion Problems. Outline. Differential Equation

Goal. Adaptive Finite Element Methods for Non-Stationary Convection-Diffusion Problems. Outline. Differential Equation Goal Adaptive Fiite Elemet Methods for No-Statioary Covectio-Diffusio Problems R. Verfürth Ruhr-Uiversität Bochum www.ruhr-ui-bochum.de/um1 Tübige / July 0th, 017 Preset space-time adaptive fiite elemet

More information

AH Checklist (Unit 3) AH Checklist (Unit 3) Matrices

AH Checklist (Unit 3) AH Checklist (Unit 3) Matrices AH Checklist (Uit 3) AH Checklist (Uit 3) Matrices Skill Achieved? Kow that a matrix is a rectagular array of umbers (aka etries or elemets) i paretheses, each etry beig i a particular row ad colum Kow

More information

Notes for Lecture 11

Notes for Lecture 11 U.C. Berkeley CS78: Computatioal Complexity Hadout N Professor Luca Trevisa 3/4/008 Notes for Lecture Eigevalues, Expasio, ad Radom Walks As usual by ow, let G = (V, E) be a udirected d-regular graph with

More information

Improving the Localization of Eigenvalues for Complex Matrices

Improving the Localization of Eigenvalues for Complex Matrices Applied Mathematical Scieces, Vol. 5, 011, o. 8, 1857-1864 Improvig the Localizatio of Eigevalues for Complex Matrices P. Sargolzaei 1, R. Rakhshaipur Departmet of Mathematics, Uiversity of Sista ad Baluchesta

More information

Abstract Vector Spaces. Abstract Vector Spaces

Abstract Vector Spaces. Abstract Vector Spaces Astract Vector Spaces The process of astractio is critical i egieerig! Physical Device Data Storage Vector Space MRI machie Optical receiver 0 0 1 0 1 0 0 1 Icreasig astractio 6.1 Astract Vector Spaces

More information

arxiv: v3 [cs.it] 17 May 2017

arxiv: v3 [cs.it] 17 May 2017 Liear Programmig Bouds for Etaglemet-Assisted Quatum Error-Correctig Codes by Split Weight Eumerators Chig-Yi Lai ad Alexei Ashikhmi arxiv:1602.00413v3 [cs.t] 17 May 2017 Abstract Liear programmig approaches

More information

Complex Analysis Spring 2001 Homework I Solution

Complex Analysis Spring 2001 Homework I Solution Complex Aalysis Sprig 2001 Homework I Solutio 1. Coway, Chapter 1, sectio 3, problem 3. Describe the set of poits satisfyig the equatio z a z + a = 2c, where c > 0 ad a R. To begi, we see from the triagle

More information

L = n i, i=1. dp p n 1

L = n i, i=1. dp p n 1 Exchageable sequeces ad probabilities for probabilities 1996; modified 98 5 21 to add material o mutual iformatio; modified 98 7 21 to add Heath-Sudderth proof of de Fietti represetatio; modified 99 11

More information

Lecture 14: Randomized Computation (cont.)

Lecture 14: Randomized Computation (cont.) CSE 200 Computability ad Complexity Wedesday, May 15, 2013 Lecture 14: Radomized Computatio (cot.) Istructor: Professor Shachar Lovett Scribe: Dogcai She 1 Radmized Algorithm Examples 1.1 The k-th Elemet

More information

We are mainly going to be concerned with power series in x, such as. (x)} converges - that is, lims N n

We are mainly going to be concerned with power series in x, such as. (x)} converges - that is, lims N n Review of Power Series, Power Series Solutios A power series i x - a is a ifiite series of the form c (x a) =c +c (x a)+(x a) +... We also call this a power series cetered at a. Ex. (x+) is cetered at

More information

Math 510 Assignment 6 Due date: Nov. 26, 2012

Math 510 Assignment 6 Due date: Nov. 26, 2012 1 If A M is Hermitia, show that Math 510 Assigmet 6 Due date: Nov 6, 01 tr A rak (Atr (A with equality if ad oly if A = ap for some a R ad orthogoal projectio P M (ie P = P = P Solutio: Let A = A If A

More information

Math Solutions to homework 6

Math Solutions to homework 6 Math 175 - Solutios to homework 6 Cédric De Groote November 16, 2017 Problem 1 (8.11 i the book): Let K be a compact Hermitia operator o a Hilbert space H ad let the kerel of K be {0}. Show that there

More information

Chapter 6 Principles of Data Reduction

Chapter 6 Principles of Data Reduction Chapter 6 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 0 Chapter 6 Priciples of Data Reductio Sectio 6. Itroductio Goal: To summarize or reduce the data X, X,, X to get iformatio about a

More information

Introduction to Optimization Techniques. How to Solve Equations

Introduction to Optimization Techniques. How to Solve Equations Itroductio to Optimizatio Techiques How to Solve Equatios Iterative Methods of Optimizatio Iterative methods of optimizatio Solutio of the oliear equatios resultig form a optimizatio problem is usually

More information

ADVANCED DIGITAL SIGNAL PROCESSING

ADVANCED DIGITAL SIGNAL PROCESSING ADVANCED DIGITAL SIGNAL PROCESSING PROF. S. C. CHAN (email : sccha@eee.hku.hk, Rm. CYC-702) DISCRETE-TIME SIGNALS AND SYSTEMS MULTI-DIMENSIONAL SIGNALS AND SYSTEMS RANDOM PROCESSES AND APPLICATIONS ADAPTIVE

More information

Lecture 10: Universal coding and prediction

Lecture 10: Universal coding and prediction 0-704: Iformatio Processig ad Learig Sprig 0 Lecture 0: Uiversal codig ad predictio Lecturer: Aarti Sigh Scribes: Georg M. Goerg Disclaimer: These otes have ot bee subjected to the usual scrutiy reserved

More information

Infinite Sequences and Series

Infinite Sequences and Series Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet

More information

Eigenvalues and Eigenvectors

Eigenvalues and Eigenvectors 5 Eigevalues ad Eigevectors 5.3 DIAGONALIZATION DIAGONALIZATION Example 1: Let. Fid a formula for A k, give that P 1 1 = 1 2 ad, where Solutio: The stadard formula for the iverse of a 2 2 matrix yields

More information

arxiv: v1 [math.pr] 13 Oct 2011

arxiv: v1 [math.pr] 13 Oct 2011 A tail iequality for quadratic forms of subgaussia radom vectors Daiel Hsu, Sham M. Kakade,, ad Tog Zhag 3 arxiv:0.84v math.pr] 3 Oct 0 Microsoft Research New Eglad Departmet of Statistics, Wharto School,

More information

Ray Optics Theory and Mode Theory. Dr. Mohammad Faisal Dept. of EEE, BUET

Ray Optics Theory and Mode Theory. Dr. Mohammad Faisal Dept. of EEE, BUET Ray Optics Theory ad Mode Theory Dr. Mohammad Faisal Dept. of, BUT Optical Fiber WG For light to be trasmitted through fiber core, i.e., for total iteral reflectio i medium, > Ray Theory Trasmissio Ray

More information

An operator method in entanglement entropy

An operator method in entanglement entropy operator method i etaglemet etropy Noburo hiba YITP Kyoto U. Luch semiar at YITP, Jauary 14, 2015 N.hiba, JEP 1412 2014 152 [arxiv:1408.1637] YITP 2015/1/14 Cotets operator method = ew computatioal method

More information

2 Geometric interpretation of complex numbers

2 Geometric interpretation of complex numbers 2 Geometric iterpretatio of complex umbers 2.1 Defiitio I will start fially with a precise defiitio, assumig that such mathematical object as vector space R 2 is well familiar to the studets. Recall that

More information

BIOSTATISTICS. Lecture 5 Interval Estimations for Mean and Proportion. dr. Petr Nazarov

BIOSTATISTICS. Lecture 5 Interval Estimations for Mean and Proportion. dr. Petr Nazarov Microarray Ceter BIOSTATISTICS Lecture 5 Iterval Estimatios for Mea ad Proportio dr. Petr Nazarov 15-03-013 petr.azarov@crp-sate.lu Lecture 5. Iterval estimatio for mea ad proportio OUTLINE Iterval estimatios

More information

CHAPTER 3. GOE and GUE

CHAPTER 3. GOE and GUE CHAPTER 3 GOE ad GUE We quicly recall that a GUE matrix ca be defied i the followig three equivalet ways. We leave it to the reader to mae the three aalogous statemets for GOE. I the previous chapters,

More information