Quantum Geometric Algebra
|
|
- Tracy Pope
- 5 years ago
- Views:
Transcription
1 ANPA 22: Quantum Geometric Algebra Quantum Geometric Algebra ANPA Conference Cambridge, UK by Dr. Douglas J. Matzke Aug 15-18, 22 8/15/22 DJM
2 ANPA 22: Quantum Geometric Algebra Abstract Quantum computing concepts are described using geometric algebra, without using complex numbers or matrices. This novel approach enables the expression of the principle ideas of quantum computation without requiring an advanced degree in mathematics. Using a topologically derived algebraic notation that relies only on addition and the anticommutative geometric product, this talk describes the following quantum computing concepts: bits, vectors, states, orthogonality, qubits, classical states, superposition states, spinor, reversibility, unitary operator, singular, entanglement, ebits, separability, information erasure, destructive interference and measurement. These quantum concepts can be described simply in geometric algebra, thereby facilitating the understanding of quantum computing concepts by non-physicists and non-mathematicians. 8/15/22 DJM
3 ANPA 22: Quantum Geometric Algebra Overview of Presentation Co-Occurrence and Co-Exclusion Geometric Algebra G n Essentials Symmetric values, scalar addition and multiplication Graded N-vectors, scalar, bivectors, spinors Inner product, outer product, and anticommutative geometric product Qubit Definition is Co-Occurrence Standard and Superposition States, Hadamard Operator, Not Operator Reversibility, Unitary Operators, Pauli Operators, Circular basis Irreversibility, Singular Operators, Sparse Invariants and Measurement Eigenvectors, Projection Operators, trine states Quantum Registers Geometric product equivalent to tensor product, entanglement, separability Ebits and Bell/magic States/operators, non-separable and information erasure C-not, C-spin, Toffoli Operators Conclusions 8/15/22 DJM
4 ANPA 22: Quantum Geometric Algebra Co-Occurrence and Co-Exclusion a a ON and a a not ON where a a a b b a c - d d - c c - d d - c Abstract Space c - d d - c (or can not occur) ( means cannot occur) Co-occurrence means states exist exactly simultaneously Co-exclusion means a change occurred due to an operator Abstract Time Both of Mike Manthey s concepts used heavily in this research 8/15/22 DJM
5 ANPA 22: Quantum Geometric Algebra Boolean Logic using /* in G n 1 1 * Normal multiplication and mod 3 addition for ring { 1,,1}, so can simplify to {,,} and remove rows/columns for header value. * If same then invert If diff then cancel NAND > NOR > If same then 1 If diff then -1 same XNOR same > differ XNOR differ > 1 Z 3 1 Binary Values Also for any vector e: since e 2 1 then e 1/e Logic ing 2 span{a, b} GA Mapping {, } GA Mapping {, } Identity a a * 1 a a 1 a (1 a) NOT a a * 1 a 1 a (1 a) a XOR b a b 1 a b a OR b a b a b 1 a b a b a AND b 1 a b a b 1 a b a b Geometric Algebra is Boolean Complete 8/15/22 DJM
6 ANPA 22: Quantum Geometric Algebra Geometric Algebra Essentials b b a ab ab i a b where geometric product is sum ab i cosθ of inner product (is a scalar) a b i sinθ and outer product (is a bivector) a a b b a G n2 generates N2 n : span{a, b} G 2 scalars {±1}, vectors {a, b}, and bivector {a b} then: With ab i (only orthonormal basis so are perpendicular) then a b b a (due to anti-commutative outer product) a 2 b 2 1 (due to inner product since collinear) bivector is spinor because: (right multiplication by spinor) a (a b) a a b b, and b (a b) a b b a spinor is also pseudoscalar I because: (a b) 2 a b a b a a b b (a) 2 (b) 2 1 NOT a b orientation so a b 1 NOT also x'rx Rwith R α βab, R α βab, α cos( θ /2), β sin( θ /2) a b orientation a b a b 8/15/22 DJM
7 ANPA 22: Quantum Geometric Algebra Number of Elements in G n Graded: scalar, vector, bivector, trivector,, n-vector for G n with N2 n elements (1a)(1b)(1c) 1 a b c a b a c b c a b c <multivector G n G n G n <A> <A> 1 <A> 2 <A> 3 <A> n Odd grade terms G n <A> 1 <A> 3 Even Subalgebra G n <A> <A> 2 G 3 are the quaternions: 1 a b a c b c Row n Col m n n 1 N 2 m 1 m Pascal s Triangle (Binomial) n 8/15/22 DJM
8 ANPA 22: Quantum Geometric Algebra Inner Product Calculation X X Y 1 a b a b and Y ( x y ) Z ( Y z) G 2 span{a,b}: G 3 span{a,b,c}: 1 1 a b a b Outer Product a a a b Y b b a b with vector variables w, xa, yb, zc wy i wi( a b ) ( wa i ) b - ( wb i ) a wz i ( wa i ) b c ( wb i ) a c ( wc i ) a b Only one non-zero term in sum for orthogonal basis set {a,b,c} XY XiY X Y a b a b X XY i 1 a b a b 1 Inner Product a 1 b Y b 1 a a b only if X or Y are assigned vector x or y b a 1 8/15/22 DJM
9 ANPA 22: Quantum Geometric Algebra Qubit is Co-occurrence in G 2 Single Qubit: A (±a ±a1) where Q 1 G 2 span{a, a1} 4 elements & multivectors A 1 a A a1 a1 A a A Superposition: signs are same Classical: signs are opposite a a1 A 1 a a1 A A a a1 A R Row k R 1 R 2 R 3 Binary combinations of input states Anti-symmetric sums are classical states A 1 R 1 R 2 a a1 a a1 A R 2 R 1 Symmetric sums are superposition states A R R 3 A R 3 R 8/15/22 DJM
10 ANPA 22: Quantum Geometric Algebra Spinor is Hadamard Operator Start Phase Qubit State A Each Times Spinor Result A S A End Phase Classical A a a1 A 1 a a1 a (a a1) a1 a (a a1) a1 A a a1 A a a1 Superposed Superposed A a a1 A a a1 a1 (a a1) a a1 (a a1) a A 1 a a1 A a a1 Classical Hadamard is the 9 phase or spinor operator S A (a a1) NOT operator is 18 gate S 2 A (a a1)(a a1) a a a1 a1 1 r p Therefore S A 1 NOT and generally θ θ / rand θ pθ 8/15/22 DJM
11 ANPA 22: Quantum Geometric Algebra σ σ 3 1 Unitary Pauli Noise States in G 2 Flip: Bit 1 1 Phase 1 1 Case [a] [b] Case [a] [b] [c] Hilbert notation σ 1 1 Use case [a] GA equivalent is ( 1) complement ( a a1)( 1) ( a a1) σ 1 1 [b] ( a a1)( 1) ( a a1) Hilbert notation σ σ 3 σ Use cases [a]&[b] [b]&[c] GA equivalent is spinor S A a a1 ( a a1)( a a1) ( a a1) ( a a1 )(a a1) ( a a1) σ 2 Both i i Case [a] [b] Hilbert notation σ 2 i σ 2 1 i 1 Use cases [a]&[b] GA equivalent is ( 1 S A ) P A ( a a1)( 1 a a1) a1 Pauli operators -1, S A and P A are even grade! 8/15/22 DJM
12 ANPA 22: Quantum Geometric Algebra Reversible Basis Encodings: Standard, Dual, Pauli and Circular basis Label for Row classical classical 1 superposition superposition Label for Basis Start State a a1 a a1 a a1 a a1 Diagonals Reversible op. return to start Diag ( 1 a a1) a1 a1 a a Pauli Ver/Hor V/Hor (1 a a1) Diag (a) (1 a a1) ( 1 a a1) (1 a a1) ( 1 a a1) Circular Cir (a) Diag ( a a1) 1 1 a a1 (random) a a1 (random) Direct or Complex Dir ( a a1) ODD GRADE Left Diagonal Classical a Pauli Vertical a1 a1 Right Diagonal Superposition a Pauli Horizontal 1 a a1 a a1 Circular 1 Direct Ellipses are co-exclusions! EVEN GRADE 8/15/22 DJM
13 ANPA 22: Quantum Geometric Algebra Unitary Operators and Reversibility For multivector state X and multivector operator Y, If new state Z X Y then Y is unitary if-and-only-if W 1/Y Y -1 exists such that Y W Y Y -1 1 Therefore unitary operator Y is invertible/reversible: Z / Y X Y / Y X For unitary Y then requires det(y)±1 or det(y) 1 A A 1 1 A A 1 Trines are unitary: (Tr) 3 1 so 1/Tr (Tr) 2 for Tr (1 ± a ± S A ) or (1 ± a1 ± S A ) 8/15/22 DJM
14 ANPA 22: Quantum Geometric Algebra Singular Operators in G n If 1/X is undefined then requires det(x), Since (±1±x) -1 is undefined then det(±1±x) and therefore X (±1±x) is singular Singular examples: det(±1±a) det(±1±b) Also fact that: det(x)det(y) det(xy), which means if factor X has det(x), then product (XY) also has det(xy). In G 2 : det(1±a)det(1±b) det(1±a±b±ab) X 1 * X ( ) 1 det( X ) 8/15/22 DJM T
15 ANPA 22: Quantum Geometric Algebra Row Decode Operators R k are Singular Row k a a1 ( 1)(1 a) ( 1)(1 a) ( 1)(1 a1) ( 1)(1 a1) R R 1 R 2 R 3 Summation of R k A R R 1 A R 2 R 3 A1 R R 2 A1 R 1 R 3 Denoted as Vector [ ] [ ] [ ] [ ] Row k a a1 (1 a)(1 a1) (1 a)(1a1) (1a)(1 a1) (1a)(1a1) R R 1 R 2 R 3 State logic R A A1 R 1 A A1 R 2 A A1 R 3 A A1 Denoted as Vector R [ ] R 1 [ ] R 2 [ ] R 3 [ ] Standard Algebraic Notation Dual Vector Notation: matrix diagonal R R 1 R 2 R 3 [ ] 1 R k are topologically smallest elements in G 2 and are linearly independent 8/15/22 DJM
16 ANPA 22: Quantum Geometric Algebra Measurement and Sparse Invariants Start States A A a a1 A 1 a a1 A a a1 A a a1 Each start state A times each R k gives the answer A(1a)(1 a1) A(1 a)(1a1) 1 a1 I 1 a1 I 1 a1 I 1 a1 I a ( 1 a1) a (1 a1) a (1 a1) a ( 1 a1) A(1a)(1a1) A(1 a)(1 a1) a (1 a1) a ( 1 a1) a ( 1 a1) a (1 a1) 1 a1 I 1 a1 I 1 a1 I 1 a1 I End State A > a a1 A > a a1 A > a a1 A > a a1 Description Classical States Measurement Superposition States Measurement I ~ 1 I ~ 1 I I (I ± ) 2 I 1 a1 [ ] I 1 a1 [ ] I 1 a1 [ ] I 9 1 a1 [ ] I 9 8/15/22 DJM
17 ANPA 22: Quantum Geometric Algebra Projection Operators P k and Eigenvectors E k Primary Tetrahedron (k 3) k E k R k 1 P k R k R k 1E k [ ] [ ] [ ] 1 [ ] [ ] [ ] 2 [ ] [ ] [ ] 3 [ ] [ ] [ ] sum [ ] [ ] [ ] a1 E E 6 a1 k sum Dual Tetrahedron (7 k) E k R k 1 [ ] [ ] [ ] [ ] [ ] a1 P k R k [ ] [ ] [ ] [ ] [ ] R k 1E k [ ] [ ] [ ] [ ] [ ] E E 6 a1 R k P k E k 2 1 E k R k R k P k 2 P k Idempotent!! E 2 E 4 E 2 E E3 a E 5 a a E 4 E 5 a a a1 E 1 E 7 a a1 a a1 E 1 E 7 a a1 E k ± a ± a1 ± a a1 PiP3 PP 1i 2 PiP4 P6iP5 7 8/15/22 DJM
18 ANPA 22: Quantum Geometric Algebra Qubits form Quantum Register Q q with A (±a ±a1), B (±b ±b1), C (±c ±c1) then A B C (±a ±a1)(±b ±b1)(±c ±c1) so A B (a a1)(b b1) a b a b1 a1 b a1 b1 Geometric product replaces the tensor product Row k State Combinations Individual bivector products Column Vector a a1 b b1 a b a b1 a1 b a1 b1 A B A B R R 3 R 5 R 6 R 9 R 1 R 12 R 15 Q q G n2q State Count: Total: 2 2q 4 q Non-zero: 2 q Zeros: 4 q 2 q A B C A 1 B 1 P A P B a1 b1 S 11 8/15/22 DJM
19 ANPA 22: Quantum Geometric Algebra Ebits: Bell/magic States and Operators Separable: A B (S A )(S B ) A (S A ) B (S B ) A B Non-Separable: A B (S A S B ) A B A B a b a b1 a1 b a1 b1 a b a1 b1 S S 11 B Row k State Combinations a a1 b b1 Individual bivectors a b a1 b1 Concurrent! Output column R 1 R 2 R 4 R 7 R 8 R 11 R 13 R 14 Valid states where exactly one qubit in superposition phase!! B (S A S B ) B i±1 ±B i B B S S 11 B 1 S 1 S 1 B 2 S S 11 B 3 S 1 S 1 Φ Ψ Φ Ψ M (S A S B ) M i±1 ±M i M M S 1 S 1 M 1 S S 11 M 2 S 1 S 1 M 3 S S 11 M 3 B 2 (S 1 S 1 ) B & M are 8/15/22 Singular! DJM
20 ANPA 22: Quantum Geometric Algebra Interesting Facts about Ebits B 2 I and M 2 I and B B I - P A P B B (1 S A S B ) B I A B B i M j but B i M M i B, so A BB B i1 Φ S 11 A /1 B /1 P AB B M 3 S 1 A /1 B ± P AB M 2 B 1 Ψ S A ± B ± P AB S 11 S M 1 B 2 Φ S 1 A ± B /1 P AB Ψ B 3 B and M are valid for Q q>2 as (S A ± S B ± S C ± ) S 1 S 1 M 8/15/22 DJM
21 ANPA 22: Quantum Geometric Algebra Cnot, Cspin and Toffoli Operators For Q 2 with qubits A and B, where A is the control: CNot AB A (a a1) where (A ) 2 1 Cspin AB ( 1 A ) ( 1 a a1) CNot For Q 3 : qubits A, B & D where A & B are controls: Tof AB CNot AD CNot BD A 1 B (concurrent!) Row k R 42 a a1 b b1 where (Tof AB ) 2 1 a R 21 A 1 B 1 & D 1 R 22 A 1 B 1 & D R 41 A B & D 1 State Combinations a1 b b1 d d1 Active States A B & D A B D (TOF AB ) Inverted Identity Also for Q q P k 2q P k E kx 1 q x 2 6 8??? 8/15/22 DJM
22 ANPA 22: Quantum Geometric Algebra Conclusions The Quantum Geometric Algebra approach appears to simply and elegantly define many of the properties of quantum computing. This work was facilitated tremendously by the use of custom tools that automatically maintained the GA anticommutative and topological rules in an algebraic fashion. Many thanks to Mike Manthey for all his inspiration and support on my PhD effort. Many questions and much work still remains. 8/15/22 DJM
QUANTUM COMPUTING MADE EASY. Douglas J. Matzke i University of Texas at Dallas Richardson, Texas , USA
QUANTUM COMPUTING MADE EASY Douglas J. Matzke i matzke@ieee.org University of Texas at Dallas Richardson, Texas 75083-0688, USA C. D. Cantrell cantrell@utdallas.edu University of Texas at Dallas P.O. Box
More informationQuantum Geometric Algebra Version 1.1 Jan 2003
Quantum Geometric Algebra Version 1.1 Jan 2003 Douglas J. Matzke Lawrence Technologies, LLC Dallas, Texas 75254, USA matzke@ieee.org Michael Manthey P.O. Box 846 Crestone, Colorado 81131, USA manthey@acm.org
More informationQuantum Computing. 6. Quantum Computer Architecture 7. Quantum Computers and Complexity
Quantum Computing 1. Quantum States and Quantum Gates 2. Multiple Qubits and Entangled States 3. Quantum Gate Arrays 4. Quantum Parallelism 5. Examples of Quantum Algorithms 1. Grover s Unstructured Search
More informationIntroduction to Quantum Computing
Introduction to Quantum Computing Part I Emma Strubell http://cs.umaine.edu/~ema/quantum_tutorial.pdf April 12, 2011 Overview Outline What is quantum computing? Background Caveats Fundamental differences
More informationProjects about Quantum adder circuits Final examination June 2018 Quirk Simulator
Projects about Quantum adder circuits Final examination June 2018 Quirk Simulator http://algassert.com/2016/05/22/quirk.html PROBLEM TO SOLVE 1. The HNG gate is described in reference: Haghparast M. and
More information1 Readings. 2 Unitary Operators. C/CS/Phys C191 Unitaries and Quantum Gates 9/22/09 Fall 2009 Lecture 8
C/CS/Phys C191 Unitaries and Quantum Gates 9//09 Fall 009 Lecture 8 1 Readings Benenti, Casati, and Strini: Classical circuits and computation Ch.1.,.6 Quantum Gates Ch. 3.-3.4 Kaye et al: Ch. 1.1-1.5,
More informationQuantum Computing Lecture 2. Review of Linear Algebra
Quantum Computing Lecture 2 Review of Linear Algebra Maris Ozols Linear algebra States of a quantum system form a vector space and their transformations are described by linear operators Vector spaces
More information. 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.
CS 94- Hilbert Spaces, Tensor Products, Quantum Gates, Bell States 1//07 Spring 007 Lecture 01 Hilbert Spaces Consider a discrete quantum system that has k distinguishable states (eg k distinct energy
More informationAn Introduction to Geometric Algebra
An Introduction to Geometric Algebra Alan Bromborsky Army Research Lab (Retired) brombo@comcast.net November 2, 2005 Typeset by FoilTEX History Geometric algebra is the Clifford algebra of a finite dimensional
More informationQuantum computing! quantum gates! Fisica dell Energia!
Quantum computing! quantum gates! Fisica dell Energia! What is Quantum Computing?! Calculation based on the laws of Quantum Mechanics.! Uses Quantum Mechanical Phenomena to perform operations on data.!
More informationC/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).
C/CS/Phys 191 Quantum Gates and Universality 9//05 Fall 005 Lecture 8 1 Readings Benenti, Casati, and Strini: Classical circuits and computation Ch.1.,.6 Quantum Gates Ch. 3.-3.4 Universality Ch. 3.5-3.6
More informationMATH Topics in Applied Mathematics Lecture 12: Evaluation of determinants. Cross product.
MATH 311-504 Topics in Applied Mathematics Lecture 12: Evaluation of determinants. Cross product. Determinant is a scalar assigned to each square matrix. Notation. The determinant of a matrix A = (a ij
More informationIntroduction into Quantum Computations Alexei Ashikhmin Bell Labs
Introduction into Quantum Computations Alexei Ashikhmin Bell Labs Workshop on Quantum Computing and its Application March 16, 2017 Qubits Unitary transformations Quantum Circuits Quantum Measurements Quantum
More informationPh 219b/CS 219b. Exercises Due: Wednesday 20 November 2013
1 h 219b/CS 219b Exercises Due: Wednesday 20 November 2013 3.1 Universal quantum gates I In this exercise and the two that follow, we will establish that several simple sets of gates are universal for
More informationIntroduction to Quantum Logic. Chris Heunen
Introduction to Quantum Logic Chris Heunen 1 / 28 Overview Boolean algebra Superposition Quantum logic Entanglement Quantum computation 2 / 28 Boolean algebra 3 / 28 Boolean algebra A Boolean algebra is
More informationMATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators.
MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators. Adjoint operator and adjoint matrix Given a linear operator L on an inner product space V, the adjoint of L is a transformation
More informationMath Bootcamp An p-dimensional vector is p numbers put together. Written as. x 1 x =. x p
Math Bootcamp 2012 1 Review of matrix algebra 1.1 Vectors and rules of operations An p-dimensional vector is p numbers put together. Written as x 1 x =. x p. When p = 1, this represents a point in the
More informationIntroduction to Quantum Algorithms Part I: Quantum Gates and Simon s Algorithm
Part I: Quantum Gates and Simon s Algorithm Martin Rötteler NEC Laboratories America, Inc. 4 Independence Way, Suite 00 Princeton, NJ 08540, U.S.A. International Summer School on Quantum Information, Max-Planck-Institut
More informationQLang: Qubit Language
QLang: Qubit Language Christopher Campbell Clément Canonne Sankalpa Khadka Winnie Narang Jonathan Wong September 24, 24 Introduction In 965, Gordon Moore predicted that the number of transistors in integrated
More informationHilbert Space, Entanglement, Quantum Gates, Bell States, Superdense Coding.
CS 94- Bell States Bell Inequalities 9//04 Fall 004 Lecture Hilbert Space Entanglement Quantum Gates Bell States Superdense Coding 1 One qubit: Recall that the state of a single qubit can be written as
More information2. Introduction to quantum mechanics
2. Introduction to quantum mechanics 2.1 Linear algebra Dirac notation Complex conjugate Vector/ket Dual vector/bra Inner product/bracket Tensor product Complex conj. matrix Transpose of matrix Hermitian
More informationSome Introductory Notes on Quantum Computing
Some Introductory Notes on Quantum Computing Markus G. Kuhn http://www.cl.cam.ac.uk/~mgk25/ Computer Laboratory University of Cambridge 2000-04-07 1 Quantum Computing Notation Quantum Computing is best
More informationMC9211 Computer Organization
MC92 Computer Organization Unit : Digital Fundamentals Lesson2 : Boolean Algebra and Simplification (KSB) (MCA) (29-2/ODD) (29 - / A&B) Coverage Lesson2 Introduces the basic postulates of Boolean Algebra
More informationLecture notes on Quantum Computing. Chapter 1 Mathematical Background
Lecture notes on Quantum Computing Chapter 1 Mathematical Background Vector states of a quantum system with n physical states are represented by unique vectors in C n, the set of n 1 column vectors 1 For
More informationChapter 10. Quantum algorithms
Chapter 10. Quantum algorithms Complex numbers: a quick review Definition: C = { a + b i : a, b R } where i = 1. Polar form of z = a + b i is z = re iθ, where r = z = a 2 + b 2 and θ = tan 1 y x Alternatively,
More informationNumber System. Decimal to binary Binary to Decimal Binary to octal Binary to hexadecimal Hexadecimal to binary Octal to binary
Number System Decimal to binary Binary to Decimal Binary to octal Binary to hexadecimal Hexadecimal to binary Octal to binary BOOLEAN ALGEBRA BOOLEAN LOGIC OPERATIONS Logical AND Logical OR Logical COMPLEMENTATION
More informationQuantum Information & Quantum Computing
Math 478, Phys 478, CS4803, February 9, 006 1 Georgia Tech Math, Physics & Computing Math 478, Phys 478, CS4803 Quantum Information & Quantum Computing Problems Set 1 Due February 9, 006 Part I : 1. Read
More informationBoolean Algebra & Logic Gates. By : Ali Mustafa
Boolean Algebra & Logic Gates By : Ali Mustafa Digital Logic Gates There are three fundamental logical operations, from which all other functions, no matter how complex, can be derived. These Basic functions
More informationLinear Algebra Review. Vectors
Linear Algebra Review 9/4/7 Linear Algebra Review By Tim K. Marks UCSD Borrows heavily from: Jana Kosecka http://cs.gmu.edu/~kosecka/cs682.html Virginia de Sa (UCSD) Cogsci 8F Linear Algebra review Vectors
More informationEx: Boolean expression for majority function F = A'BC + AB'C + ABC ' + ABC.
Boolean Expression Forms: Sum-of-products (SOP) Write an AND term for each input combination that produces a 1 output. Write the input variable if its value is 1; write its complement otherwise. OR the
More informationQuantum Error Correcting Codes and Quantum Cryptography. Peter Shor M.I.T. Cambridge, MA 02139
Quantum Error Correcting Codes and Quantum Cryptography Peter Shor M.I.T. Cambridge, MA 02139 1 We start out with two processes which are fundamentally quantum: superdense coding and teleportation. Superdense
More informationIntroduction to Matrix Algebra
Introduction to Matrix Algebra August 18, 2010 1 Vectors 1.1 Notations A p-dimensional vector is p numbers put together. Written as x 1 x =. x p. When p = 1, this represents a point in the line. When p
More informationXOR - XNOR Gates. The graphic symbol and truth table of XOR gate is shown in the figure.
XOR - XNOR Gates Lesson Objectives: In addition to AND, OR, NOT, NAND and NOR gates, exclusive-or (XOR) and exclusive-nor (XNOR) gates are also used in the design of digital circuits. These have special
More informationComplex numbers: a quick review. Chapter 10. Quantum algorithms. Definition: where i = 1. Polar form of z = a + b i is z = re iθ, where
Chapter 0 Quantum algorithms Complex numbers: a quick review / 4 / 4 Definition: C = { a + b i : a, b R } where i = Polar form of z = a + b i is z = re iθ, where r = z = a + b and θ = tan y x Alternatively,
More informationCP3 REVISION LECTURES VECTORS AND MATRICES Lecture 1. Prof. N. Harnew University of Oxford TT 2013
CP3 REVISION LECTURES VECTORS AND MATRICES Lecture 1 Prof. N. Harnew University of Oxford TT 2013 1 OUTLINE 1. Vector Algebra 2. Vector Geometry 3. Types of Matrices and Matrix Operations 4. Determinants
More informationLecture 3: Hilbert spaces, tensor products
CS903: Quantum computation and Information theory (Special Topics In TCS) Lecture 3: Hilbert spaces, tensor products This lecture will formalize many of the notions introduced informally in the second
More informationGeometric Algebra 2 Quantum Theory
Geometric Algebra 2 Quantum Theory Chris Doran Astrophysics Group Cavendish Laboratory Cambridge, UK Spin Stern-Gerlach tells us that electron wavefunction contains two terms Describe state in terms of
More information1 Matrices and vector spaces
Matrices and vector spaces. Which of the following statements about linear vector spaces are true? Where a statement is false, give a counter-example to demonstrate this. (a) Non-singular N N matrices
More informationMath 346 Notes on Linear Algebra
Math 346 Notes on Linear Algebra Ethan Akin Mathematics Department Fall, 2014 1 Vector Spaces Anton Chapter 4, Section 4.1 You should recall the definition of a vector as an object with magnitude and direction
More informationDecomposition of Quantum Gates
Decomposition of Quantum Gates With Applications to Quantum Computing Dean Katsaros, Eric Berry, Diane C. Pelejo, Chi-Kwong Li College of William and Mary January 12, 215 Motivation Current Conclusions
More informationMATRICES ARE SIMILAR TO TRIANGULAR MATRICES
MATRICES ARE SIMILAR TO TRIANGULAR MATRICES 1 Complex matrices Recall that the complex numbers are given by a + ib where a and b are real and i is the imaginary unity, ie, i 2 = 1 In what we describe below,
More informationENGI 9420 Lecture Notes 2 - Matrix Algebra Page Matrix operations can render the solution of a linear system much more efficient.
ENGI 940 Lecture Notes - Matrix Algebra Page.0. Matrix Algebra A linear system of m equations in n unknowns, a x + a x + + a x b (where the a ij and i n n a x + a x + + a x b n n a x + a x + + a x b m
More informationKnowledge Discovery and Data Mining 1 (VO) ( )
Knowledge Discovery and Data Mining 1 (VO) (707.003) Review of Linear Algebra Denis Helic KTI, TU Graz Oct 9, 2014 Denis Helic (KTI, TU Graz) KDDM1 Oct 9, 2014 1 / 74 Big picture: KDDM Probability Theory
More informationQuantum Computation. Alessandra Di Pierro Computational models (Circuits, QTM) Algorithms (QFT, Quantum search)
Quantum Computation Alessandra Di Pierro alessandra.dipierro@univr.it 21 Info + Programme Info: http://profs.sci.univr.it/~dipierro/infquant/ InfQuant1.html Preliminary Programme: Introduction and Background
More informationImaginary Numbers Are Real. Timothy F. Havel (Nuclear Engineering)
GEOMETRIC ALGEBRA: Imaginary Numbers Are Real Timothy F. Havel (Nuclear Engineering) On spin & spinors: LECTURE The operators for the x, y & z components of the angular momentum of a spin / particle (in
More informationLinear Algebra. Workbook
Linear Algebra Workbook Paul Yiu Department of Mathematics Florida Atlantic University Last Update: November 21 Student: Fall 2011 Checklist Name: A B C D E F F G H I J 1 2 3 4 5 6 7 8 9 10 xxx xxx xxx
More informationDiagonalizing Matrices
Diagonalizing Matrices Massoud Malek A A Let A = A k be an n n non-singular matrix and let B = A = [B, B,, B k,, B n ] Then A n A B = A A 0 0 A k [B, B,, B k,, B n ] = 0 0 = I n 0 A n Notice that A i B
More informationProperties of Linear Transformations from R n to R m
Properties of Linear Transformations from R n to R m MATH 322, Linear Algebra I J. Robert Buchanan Department of Mathematics Spring 2015 Topic Overview Relationship between the properties of a matrix transformation
More informationReview of Linear Algebra
Review of Linear Algebra Definitions An m n (read "m by n") matrix, is a rectangular array of entries, where m is the number of rows and n the number of columns. 2 Definitions (Con t) A is square if m=
More information2.0 Basic Elements of a Quantum Information Processor. 2.1 Classical information processing The carrier of information
QSIT09.L03 Page 1 2.0 Basic Elements of a Quantum Information Processor 2.1 Classical information processing 2.1.1 The carrier of information - binary representation of information as bits (Binary digits).
More informationQuantum Information & Quantum Computing
Math 478, Phys 478, CS4803, September 18, 007 1 Georgia Tech Math, Physics & Computing Math 478, Phys 478, CS4803 Quantum Information & Quantum Computing Homework # Due September 18, 007 1. Read carefully
More informationBoolean algebra. Examples of these individual laws of Boolean, rules and theorems for Boolean algebra are given in the following table.
The Laws of Boolean Boolean algebra As well as the logic symbols 0 and 1 being used to represent a digital input or output, we can also use them as constants for a permanently Open or Closed circuit or
More informationCombinational Logic. By : Ali Mustafa
Combinational Logic By : Ali Mustafa Contents Adder Subtractor Multiplier Comparator Decoder Encoder Multiplexer How to Analyze any combinational circuit like this? Analysis Procedure To obtain the output
More information1 Linear Algebra Problems
Linear Algebra Problems. Let A be the conjugate transpose of the complex matrix A; i.e., A = A t : A is said to be Hermitian if A = A; real symmetric if A is real and A t = A; skew-hermitian if A = A and
More informationLinear Algebra 1 Exam 2 Solutions 7/14/3
Linear Algebra 1 Exam Solutions 7/14/3 Question 1 The line L has the symmetric equation: x 1 = y + 3 The line M has the parametric equation: = z 4. [x, y, z] = [ 4, 10, 5] + s[10, 7, ]. The line N is perpendicular
More informationEECS150 - Digital Design Lecture 4 - Boolean Algebra I (Representations of Combinational Logic Circuits)
EECS150 - Digital Design Lecture 4 - Boolean Algebra I (Representations of Combinational Logic Circuits) September 5, 2002 John Wawrzynek Fall 2002 EECS150 Lec4-bool1 Page 1, 9/5 9am Outline Review of
More informationDigital Techniques. Figure 1: Block diagram of digital computer. Processor or Arithmetic logic unit ALU. Control Unit. Storage or memory unit
Digital Techniques 1. Binary System The digital computer is the best example of a digital system. A main characteristic of digital system is its ability to manipulate discrete elements of information.
More informationUC Berkeley College of Engineering, EECS Department CS61C: Representations of Combinational Logic Circuits
2 Wawrzynek, Garcia 2004 c UCB UC Berkeley College of Engineering, EECS Department CS61C: Representations of Combinational Logic Circuits 1 Introduction Original document by J. Wawrzynek (2003-11-15) Revised
More informationQuantum Subsystem Codes Their Theory and Use
Quantum Subsystem Codes Their Theory and Use Nikolas P. Breuckmann - September 26, 2011 Bachelor thesis under supervision of Prof. Dr. B. M. Terhal Fakultät für Mathematik, Informatik und Naturwissenschaften
More informationBoolean Algebra and Digital Logic 2009, University of Colombo School of Computing
IT 204 Section 3.0 Boolean Algebra and Digital Logic Boolean Algebra 2 Logic Equations to Truth Tables X = A. B + A. B + AB A B X 0 0 0 0 3 Sum of Products The OR operation performed on the products of
More information88 CHAPTER 3. SYMMETRIES
88 CHAPTER 3 SYMMETRIES 31 Linear Algebra Start with a field F (this will be the field of scalars) Definition: A vector space over F is a set V with a vector addition and scalar multiplication ( scalars
More information6. Quantum error correcting codes
6. Quantum error correcting codes Error correcting codes (A classical repetition code) Preserving the superposition Parity check Phase errors CSS 7-qubit code (Steane code) Too many error patterns? Syndrome
More informationReview problems for MA 54, Fall 2004.
Review problems for MA 54, Fall 2004. Below are the review problems for the final. They are mostly homework problems, or very similar. If you are comfortable doing these problems, you should be fine on
More informationInvariant Quantum Ensemble Metrics
Invariant Quantum Ensemble Metrics SPIE Defense and Security Symposium Quantum Information and Computation III Orlando, Florida, Tuesday March 29, 2005 By Douglas J. Matzke and P. Nick Lawrence Lawrence
More informationOutline. EECS150 - Digital Design Lecture 4 - Boolean Algebra I (Representations of Combinational Logic Circuits) Combinational Logic (CL) Defined
EECS150 - Digital Design Lecture 4 - Boolean Algebra I (Representations of Combinational Logic Circuits) January 30, 2003 John Wawrzynek Outline Review of three representations for combinational logic:
More informationShort Course in Quantum Information Lecture 2
Short Course in Quantum Information Lecture Formal Structure of Quantum Mechanics Course Info All materials downloadable @ website http://info.phys.unm.edu/~deutschgroup/deutschclasses.html Syllabus Lecture
More informationChapter 2 Combinational Logic Circuits
Logic and Computer Design Fundamentals Chapter 2 Combinational Logic Circuits Part 1 Gate Circuits and Boolean Equations Chapter 2 - Part 1 2 Chapter 2 - Part 1 3 Chapter 2 - Part 1 4 Chapter 2 - Part
More informationMatrices and Linear Algebra
Contents Quantitative methods for Economics and Business University of Ferrara Academic year 2017-2018 Contents 1 Basics 2 3 4 5 Contents 1 Basics 2 3 4 5 Contents 1 Basics 2 3 4 5 Contents 1 Basics 2
More informationMATH2210 Notebook 2 Spring 2018
MATH2210 Notebook 2 Spring 2018 prepared by Professor Jenny Baglivo c Copyright 2009 2018 by Jenny A. Baglivo. All Rights Reserved. 2 MATH2210 Notebook 2 3 2.1 Matrices and Their Operations................................
More informationQuantum Computing Lecture 3. Principles of Quantum Mechanics. Anuj Dawar
Quantum Computing Lecture 3 Principles of Quantum Mechanics Anuj Dawar What is Quantum Mechanics? Quantum Mechanics is a framework for the development of physical theories. It is not itself a physical
More informationPhysics 251 Solution Set 1 Spring 2017
Physics 5 Solution Set Spring 07. Consider the set R consisting of pairs of real numbers. For (x,y) R, define scalar multiplication by: c(x,y) (cx,cy) for any real number c, and define vector addition
More informationBoolean State Transformation
Quantum Computing Boolean State Transformation Quantum Computing 2 Boolean Gates Boolean gates transform the state of a Boolean system. Conventionally a Boolean gate is a map f : {,} n {,}, where n is
More informationChap 2. Combinational Logic Circuits
Overview 2 Chap 2. Combinational Logic Circuits Spring 24 Part Gate Circuits and Boolean Equations Binary Logic and Gates Boolean Algebra Standard Forms Part 2 Circuit Optimization Two-Level Optimization
More informationHOMEWORK PROBLEMS FROM STRANG S LINEAR ALGEBRA AND ITS APPLICATIONS (4TH EDITION)
HOMEWORK PROBLEMS FROM STRANG S LINEAR ALGEBRA AND ITS APPLICATIONS (4TH EDITION) PROFESSOR STEVEN MILLER: BROWN UNIVERSITY: SPRING 2007 1. CHAPTER 1: MATRICES AND GAUSSIAN ELIMINATION Page 9, # 3: Describe
More informationMatrix Algebra Determinant, Inverse matrix. Matrices. A. Fabretti. Mathematics 2 A.Y. 2015/2016. A. Fabretti Matrices
Matrices A. Fabretti Mathematics 2 A.Y. 2015/2016 Table of contents Matrix Algebra Determinant Inverse Matrix Introduction A matrix is a rectangular array of numbers. The size of a matrix is indicated
More informationChapter 2. Basic Principles of Quantum mechanics
Chapter 2. Basic Principles of Quantum mechanics In this chapter we introduce basic principles of the quantum mechanics. Quantum computers are based on the principles of the quantum mechanics. In the classical
More informationLecture 2: From Classical to Quantum Model of Computation
CS 880: Quantum Information Processing 9/7/10 Lecture : From Classical to Quantum Model of Computation Instructor: Dieter van Melkebeek Scribe: Tyson Williams Last class we introduced two models for deterministic
More informationLecture 10. The Dirac equation. WS2010/11: Introduction to Nuclear and Particle Physics
Lecture 10 The Dirac equation WS2010/11: Introduction to Nuclear and Particle Physics The Dirac equation The Dirac equation is a relativistic quantum mechanical wave equation formulated by British physicist
More informationTotal Time = 90 Minutes, Total Marks = 50. Total /50 /10 /18
University of Waterloo Department of Electrical & Computer Engineering E&CE 223 Digital Circuits and Systems Midterm Examination Instructor: M. Sachdev October 23rd, 2007 Total Time = 90 Minutes, Total
More informationXI STANDARD [ COMPUTER SCIENCE ] 5 MARKS STUDY MATERIAL.
2017-18 XI STANDARD [ COMPUTER SCIENCE ] 5 MARKS STUDY MATERIAL HALF ADDER 1. The circuit that performs addition within the Arithmetic and Logic Unit of the CPU are called adders. 2. A unit that adds two
More informationMore advanced codes 0 1 ( , 1 1 (
p. 1/24 More advanced codes The Shor code was the first general-purpose quantum error-correcting code, but since then many others have been discovered. An important example, discovered independently of
More information4. Determinants.
4. Determinants 4.1. Determinants; Cofactor Expansion Determinants of 2 2 and 3 3 Matrices 2 2 determinant 4.1. Determinants; Cofactor Expansion Determinants of 2 2 and 3 3 Matrices 3 3 determinant 4.1.
More informationQuantum Circuits and Algorithms
Quantum Circuits and Algorithms Modular Arithmetic, XOR Reversible Computation revisited Quantum Gates revisited A taste of quantum algorithms: Deutsch algorithm Other algorithms, general overviews Measurements
More informationChapter 2. Digital Logic Basics
Chapter 2 Digital Logic Basics 1 2 Chapter 2 2 1 Implementation using NND gates: We can write the XOR logical expression B + B using double negation as B+ B = B+B = B B From this logical expression, we
More informationLecture 3: Matrix and Matrix Operations
Lecture 3: Matrix and Matrix Operations Representation, row vector, column vector, element of a matrix. Examples of matrix representations Tables and spreadsheets Scalar-Matrix operation: Scaling a matrix
More informationQuantum Error Correction Codes-From Qubit to Qudit. Xiaoyi Tang, Paul McGuirk
Quantum Error Correction Codes-From Qubit to Qudit Xiaoyi Tang, Paul McGuirk Outline Introduction to quantum error correction codes (QECC) Qudits and Qudit Gates Generalizing QECC to Qudit computing Need
More informationLinear Algebra and Dirac Notation, Pt. 3
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
More informationIr O D = D = ( ) Section 2.6 Example 1. (Bottom of page 119) dim(v ) = dim(l(v, W )) = dim(v ) dim(f ) = dim(v )
Section 3.2 Theorem 3.6. Let A be an m n matrix of rank r. Then r m, r n, and, by means of a finite number of elementary row and column operations, A can be transformed into the matrix ( ) Ir O D = 1 O
More informationReversible and Quantum computing. Fisica dell Energia - a.a. 2015/2016
Reversible and Quantum computing Fisica dell Energia - a.a. 2015/2016 Reversible computing A process is said to be logically reversible if the transition function that maps old computational states to
More informationApplied Linear Algebra in Geoscience Using MATLAB
Applied Linear Algebra in Geoscience Using MATLAB Contents Getting Started Creating Arrays Mathematical Operations with Arrays Using Script Files and Managing Data Two-Dimensional Plots Programming in
More informationIntroduction to Quantum Information Processing QIC 710 / CS 768 / PH 767 / CO 681 / AM 871
Introduction to Quantum Information Processing QIC 710 / CS 768 / PH 767 / CO 681 / AM 871 Lecture 1 (2017) Jon Yard QNC 3126 jyard@uwaterloo.ca TAs Nitica Sakharwade nsakharwade@perimeterinstitute.ca
More informationGeometric Algebra. 4. Algebraic Foundations and 4D. Dr Chris Doran ARM Research
Geometric Algebra 4. Algebraic Foundations and 4D Dr Chris Doran ARM Research L4 S2 Axioms Elements of a geometric algebra are called multivectors Multivectors can be classified by grade Grade-0 terms
More informationQUANTUM CRYPTOGRAPHY QUANTUM COMPUTING. Philippe Grangier, Institut d'optique, Orsay. from basic principles to practical realizations.
QUANTUM CRYPTOGRAPHY QUANTUM COMPUTING Philippe Grangier, Institut d'optique, Orsay 1. Quantum cryptography : from basic principles to practical realizations. 2. Quantum computing : a conceptual revolution
More informationLinear Algebra: Matrix Eigenvalue Problems
CHAPTER8 Linear Algebra: Matrix Eigenvalue Problems Chapter 8 p1 A matrix eigenvalue problem considers the vector equation (1) Ax = λx. 8.0 Linear Algebra: Matrix Eigenvalue Problems Here A is a given
More informationCS61c: Representations of Combinational Logic Circuits
CS61c: Representations of Combinational Logic Circuits J. Wawrzynek March 5, 2003 1 Introduction Recall that synchronous systems are composed of two basic types of circuits, combination logic circuits,
More informationChapter 2. Linear Algebra. rather simple and learning them will eventually allow us to explain the strange results of
Chapter 2 Linear Algebra In this chapter, we study the formal structure that provides the background for quantum mechanics. The basic ideas of the mathematical machinery, linear algebra, are rather simple
More informationLinear Algebra - Part II
Linear Algebra - Part II Projection, Eigendecomposition, SVD (Adapted from Sargur Srihari s slides) Brief Review from Part 1 Symmetric Matrix: A = A T Orthogonal Matrix: A T A = AA T = I and A 1 = A T
More informationQuantum Entanglement and Error Correction
Quantum Entanglement and Error Correction Fall 2016 Bei Zeng University of Guelph Course Information Instructor: Bei Zeng, email: beizeng@icloud.com TA: Dr. Cheng Guo, email: cheng323232@163.com Wechat
More informationLecture 2: Introduction to Quantum Mechanics
CMSC 49: Introduction to Quantum Computation Fall 5, Virginia Commonwealth University Sevag Gharibian Lecture : Introduction to Quantum Mechanics...the paradox is only a conflict between reality and your
More informationDECAY OF SINGLET CONVERSION PROBABILITY IN ONE DIMENSIONAL QUANTUM NETWORKS
DECAY OF SINGLET CONVERSION PROBABILITY IN ONE DIMENSIONAL QUANTUM NETWORKS SCOTT HOTTOVY Abstract. Quantum networks are used to transmit and process information by using the phenomena of quantum mechanics.
More information