Superposition, Entanglement, and Quantum Computation Aditya Prasad 3/31/02.

Slides:



Advertisements
Similar presentations
Quantum Computation and Quantum Information – Lecture 3
Advertisements

GENERALIZED STABILIZERS Ted Yoder. Quantum/Classical Boundary How do we study the power of quantum computers compared to classical ones? Compelling problems.
Quantum Packet Switching A. Yavuz Oruç Department of Electrical and Computer Engineering University of Maryland, College Park.
Quantum Error Correction Joshua Kretchmer Gautam Wilkins Eric Zhou.
Quantum Speedups DoRon Motter August 14, Introduction Two main approaches are known which produce fast Quantum Algorithms The first, and main approach.
Quantum Computation and the Bloch Sphere
KEG PARTY!!!!!  Keg Party tomorrow night  Prof. Markov will give out extra credit to anyone who attends* *Note: This statement is a lie.
Department of Computer Science & Engineering University of Washington
1 Welcome to the presentation on Computational Capabilities with Quantum Computer By Anil Kumar Javali.
1 Quantum Computing: What’s It Good For? Scott Aaronson Computer Science Department, UC Berkeley January 10,  John.
Grover. Part 2. Components of Grover Loop The Oracle -- O The Hadamard Transforms -- H The Zero State Phase Shift -- Z O is an Oracle H is Hadamards H.
Quantum Computing Joseph Stelmach.
Simon’s Algorithm Arathi Ramani EECS 598 Class Presentation.
Grover’s Algorithm in Machine Learning and Optimization Applications
Introduction to Quantum logic (2) Yong-woo Choi.
Quantum computing Alex Karassev. Quantum Computer Quantum computer uses properties of elementary particle that are predicted by quantum mechanics Usual.
Classical Versus Quantum. Goal: Fast, low-cost implementation of useful algorithms using standard components (gates) and design techniques Classical Logic.
Debasis Sadhukhan M.Sc. Physics, IIT Bombay. 1. Basics of Quantum Computation. 2. Quantum Circuits 3. Quantum Fourier Transform and it’s applications.
Quantum Algorithms for Neural Networks Daniel Shumow.
Alice and Bob’s Excellent Adventure
Quantum Computation for Dummies Dan Simon Microsoft Research UW students.
خودکار آبی دات کام دانلود مقاله تحقیق ، پروژه ، انواع خدمات دانشجویی ، ترجمه ، اخبار فناوری در : خودکار.
October 1 & 3, Introduction to Quantum Computing Lecture 2 of 2 Richard Cleve David R. Cheriton School of Computer Science Institute for Quantum.
Small-Depth Quantum Circuits Frederic Green Department of Math/CS Clark University Worcester, MA.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 653 Course.
Algorithms Artur Ekert. Our golden sequence H H Circuit complexity n QUBITS B A A B B B B A # of gates (n) = size of the circuit (n) # of parallel units.
October 1 & 3, Introduction to Quantum Computing Lecture 1 of 2 Introduction to Quantum Computing Lecture 1 of 2
An Introduction to Quantum Phenomena and their Effect on Computing Peter Shoemaker MSCS Candidate March 7 th, 2003.
Quantum Computer Simulation Alex Bush Matt Cole James Hancox Richard Inskip Jan Zaucha.
1 hardware of quantum computer 1. quantum registers 2. quantum gates.
1 Lecture 16 Quantum computing Ubiquitous Internet Services The client server paradigm DNS Electronic Mail World Wide Web.
You Did Not Just Read This or did you?. Quantum Computing Dave Bacon Department of Computer Science & Engineering University of Washington Lecture 3:
A Study of Error-Correcting Codes for Quantum Adiabatic Computing Omid Etesami Daniel Preda CS252 – Spring 2007.
Quantum Processing Simulation
Nawaf M Albadia
Quantum and classical computing Dalibor HRG EECS FER
Quantum Computing and Quantum Programming Language
CSEP 590tv: Quantum Computing Dave Bacon July 20, 2005 Today’s Menu n Qubit registers Begin Quantum Algorithms Administrivia Superdense Coding Finish Teleportation.
Quantum Mechanics(14/2) Hongki Lee BIOPHOTONICS ENGINEERING LABORATORY School of Electrical and Electronic Engineering, Yonsei University Quantum Computing.
Introduction to Quantum Computing
A SEMINAR ON Q UANTUM C OMPUTING Institute Of Engineering & Management (CSE 3 rd year batch) Submitted to- Submitted by- Mr. D.P.S. Rathor Sudhir.
Multipartite Entanglement and its Role in Quantum Algorithms Special Seminar: Ph.D. Lecture by Yishai Shimoni.
IPQI-2010-Anu Venugopalan 1 qubits, quantum registers and gates Anu Venugopalan Guru Gobind Singh Indraprastha Univeristy Delhi _______________________________________________.
Quantum Computers The basics. Introduction 2/47 Dušan Gajević.
Quantum Computing Charles Bloomquist CS147 Fall 2009.
An Introduction to Quantum Computation Sandy Irani Department of Computer Science University of California, Irvine.
Quantum Computer Simulation Alex Bush Matt Cole James Hancox Richard Inskip Jan Zaucha.
Christopher Monroe Joint Quantum Institute and Department of Physics NIST and University of Maryland Quantum Computation and Simulation.
1 An Introduction to Quantum Computing Sabeen Faridi Ph 70 October 23, 2007.
Beginner’s Guide to Quantum Computing Graduate Seminar Presentation Oct. 5, 2007.
Intro to Quantum Algorithms SUNY Polytechnic Institute Chen-Fu Chiang Fall 2015.
Quantum Computing Keith Kelley CS 6800, Theory of Computation.
Attendance Syllabus Textbook (hardcopy or electronics) Groups s First-time meeting.
Quantum gates SALEEL AHAMMAD SALEEL. Introduction.
Quantum Algorithms Oracles
Richard Cleve DC 3524 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 667 / Phys 767 C&O 481 / C&O 681 Lecture.
Quantum Circuit Visualization
Introduction to Quantum Computing Lecture 1 of 2
A low cost quantum factoring algorithm
For computer scientists
Quantum Computation and Information Chap 1 Intro and Overview: p 1-28
A Ridiculously Brief Overview
3rd Lecture: QMA & The local Hamiltonian problem (CNT’D)
Introduction to Quantum logic (2)
OSU Quantum Information Seminar
Quantum Computing and the Quest for Quantum Computational Supremacy
Quantum Computation and Information Chap 1 Intro and Overview: p 28-58
Quantum Computing Prabhas Chongstitvatana Faculty of Engineering
Quantum Computing Joseph Stelmach.
Presentation transcript:

Superposition, Entanglement, and Quantum Computation Aditya Prasad 3/31/02

Introduction - Feynman An N-particle quantum system can’t be simulated on a classical machine whose resources don’t grow exp with N. Would be possible on a ‘quantum computer’ Not a Turing machine Both have been proven true

Introduction, cont’d Quantum parallelism – quantum superposition of distinct states Doesn’t immediately lead to speedup Shor showed how info could be extracted usefully Polynomial factoring algo.

Intro to …….. On a classical computer, unsorted database search takes O(n) time In 1997, Grover showed a quantum algo that takes O( sqrt N )

Superposition and entaglement Quantum systems can exhibit superpositions of eigensolutions Not specifically quantum – classical too Ekert and Josza consider a multi-qubit system: apply gate U to qubits (i,j) n times Quantum system: measurement in O(n) Classical system: measurement in O(2 n )

Classical and quantum: a difference Classical waves allow superposition Qubit could be represented by classical strings? Superposition can always be described by Cartesian product of states Quantum superposition may be ‘entangled’ ½( |0> + |1> + |2> + |3> ) can be factored 1/sqrt2( |0> + |1> ) cannot be: it is entangled Difference is Cartesian vs. tensor products

Entanglement, cont’d Schroedinger says quantum entanglement is defining characteristic Entanglement depends on basis ½( |0> + |1> - |2> + |3> ) is entangled wrt C 2 x C 2, but not wrt C 4 State of n qubits is 2 n -dim, isomorphic to 1 particle with 2 n levels. Not useful for complexity consideration, as the 1 particle requires energy resources in O( 2 n )

Back to Grover Search through a phone book for name, only knowing telephone number Takes O(n) time classically O( sqrt n ) time by Grover’s algo

Basics There are N = 2 L states labelled S 0, S 1, S 2 … S N-1 Only one fulfills the condition C J so that C J (S J ) = 1 and C J (S K ) = 0, K != J Goal is to find the solution S J in the fewest evaluations of C J

Grover’s solution Start with an L-qubit register in state |0> Apply an L-qubit Hadamard gate, yields an equal superposition Perform the following two operations on the wires, O( sqrt N ) times:

Grover’s operations 1) Apply oracle U J defined by: U J |J> = -|J> U J |K> = |K>, K != J 2) Apply diffusion operator D: D = H U 0 H U 0 |0> = -|0> U 0 |K> = -|K>, K != 0

Result After O( sqrt N ) iterations, the outcome is the state |J> with high probability Grover explains D to be an ‘inversion about the average’ of the coefficients

Example Apply Hadamard to get |M> = ½( |0> + |1> + |2> + |3> ) Now apply the oracle U J UJ|M> = |M> - 2 |J> Apply Grover’s diffusion operator: D U J H|)> = -|J> Found in one pass!

Classical implementation We map each integer 0…2 L -1 into another integer in the same range: Define L qubits to be a ‘control’ register |J> and another L to be the ‘target’ register |K> Let |J> x |K>  |J> x |K x f(J)> Starting with |K> = 0, we get |f(J)>

Classical, cont’d Consider an f(M) that maps an integer M to an integer F = f(M) (bijective) Want to force init state into |M> x |F(M)> so that we can measure f -1 (F) = M Define W = V f H c Let U f be an oracle that flips the sign of the state iff it is |F>

An Electronic approach Use 2 n signal paths, one for each base state L-qubit Hadamard device uses op-amps with 2 n inputs and ouputs (Description of how they used motherboards with what color LEDs here)

Hadamard implementation A general L-qubit Hadamard operator can be written as a 2 L x 2 L matrix Split each of the 2 L input signals into 2 L separate signals, each with amplitude 1/sqrt(2 L ) Use an inverting op-amp for phase-shift

Electronic Hadamard

A photograph

Hadamard conclusion Is reversible: two applications always restores input Is not physically reversible Use of op-amps and resistors ensures correct operation with AC signals Requires 2 2L signals (analogous to Deutsch’s ‘extra universes’) This is just a demonstration

Grover schematic

Hc schematic

T matrix

V f for f(I) = 3-I

Oracle for ex. f(I) = 2

Conclusions Entanglement depends on the representation Their electronic implementation shows that any implementation without multi- particle entanglement requires exp. resources (refer to Ekert and Josza)

Final conclusion “The number of signal paths increases exponentially and makes electronic implementations of large numbers of qubits impracticable” Therefore, multi-particle entanglement is the key property of quantum systems that gives rise to the remarkable power of quantum computers