First mean return time in decoherent quantum walks



Similar documents
LECTURE 4. Last time: Lecture outline

Chapter 9. Systems of Linear Equations

Quantum Computing Lecture 7. Quantum Factoring. Anuj Dawar

SIGNAL PROCESSING & SIMULATION NEWSLETTER

Three Pictures of Quantum Mechanics. Thomas R. Shafer April 17, 2009

Quantum Algorithms in NMR Experiments. 25 th May 2012 Ling LIN & Michael Loretz

The Limits of Adiabatic Quantum Computation

Rate of convergence towards Hartree dynamics

How to Gamble If You Must

Topic: Special Products and Factors Subtopic: Rules on finding factors of polynomials

December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS

System Identification for Acoustic Comms.:

DO WE REALLY UNDERSTAND QUANTUM MECHANICS?

Simplification of Radical Expressions

Time dependence in quantum mechanics Notes on Quantum Mechanics

Linear Programming I

Quantum Computing and Grover s Algorithm

SPECTRAL POLYNOMIAL ALGORITHMS FOR COMPUTING BI-DIAGONAL REPRESENTATIONS FOR PHASE TYPE DISTRIBUTIONS AND MATRIX-EXPONENTIAL DISTRIBUTIONS

Lecture 4: Thermodynamics of Diffusion: Spinodals

MATH 304 Linear Algebra Lecture 9: Subspaces of vector spaces (continued). Span. Spanning set.

Quantum Mechanics and Representation Theory

Simplifying Algebraic Fractions

3.1 Solving Systems Using Tables and Graphs

Polynomial Degree and Lower Bounds in Quantum Complexity: Collision and Element Distinctness with Small Range

~ EQUIVALENT FORMS ~

Master equation for retrodiction of quantum communication signals

Chemical group theory for quantum simulation

Factoring Quadratic Expressions

Homework set 4 - Solutions

The Australian Journal of Mathematical Analysis and Applications

Chapter 9 Unitary Groups and SU(N)

1. (First passage/hitting times/gambler s ruin problem:) Suppose that X has a discrete state space and let i be a fixed state. Let

What Has Quantum Mechanics to Do With Factoring? Things I wish they had told me about Peter Shor s algorithm

Modeling and Performance Evaluation of Computer Systems Security Operation 1

FACTORING QUADRATICS and 8.1.2

ELECTRON SPIN RESONANCE Last Revised: July 2007

The Ramsey Discounting Formula for a Hidden-State Stochastic Growth Process / 8

Research Article The General Traveling Wave Solutions of the Fisher Equation with Degree Three

path tracing computer graphics path tracing 2009 fabio pellacini 1

Matrix Differentiation

A characterization of trace zero symmetric nonnegative 5x5 matrices

Lecture 3: Finding integer solutions to systems of linear equations

Introduction to time series analysis

Example: Boats and Manatees

Correlation and Convolution Class Notes for CMSC 426, Fall 2005 David Jacobs

Traffic Behavior Analysis with Poisson Sampling on High-speed Network 1

Markov Chains. Table of Contents. Schedules

Grade Level Year Total Points Core Points % At Standard %

Algebra 2 PreAP. Name Period

The Quantum Harmonic Oscillator Stephen Webb

Factoring by Quantum Computers

FACTORISATION YEARS. A guide for teachers - Years 9 10 June The Improving Mathematics Education in Schools (TIMES) Project

Stochastic Gene Expression in Prokaryotes: A Point Process Approach

Anyone know these guys?

12.5 Equations of Lines and Planes

5 Homogeneous systems

Factoring Trinomials: The ac Method

Copyrighted Material. Chapter 1 DEGREE OF A CURVE

Stochastic Gene Expression in Prokaryotes: A Point Process Approach

Lights and Darks of the Star-Free Star

A stochastic individual-based model for immunotherapy of cancer

Rank one SVD: un algorithm pour la visualisation d une matrice non négative

15th European Union Contest for Young Scientists

Solutions to Linear First Order ODE s

Disorder-induced rounding of the phase transition. in the large-q-state Potts model. F. Iglói SZFKI - Budapest

Analysis/resynthesis with the short time Fourier transform

Factoring Methods. Example 1: 2x * x + 2 * 1 2(x + 1)

Logarithmic and Exponential Equations

MATH 304 Linear Algebra Lecture 18: Rank and nullity of a matrix.

2.1 The Present Value of an Annuity

Lecture notes on linear algebra

6.042/18.062J Mathematics for Computer Science December 12, 2006 Tom Leighton and Ronitt Rubinfeld. Random Walks

Stock price fluctuations and the mimetic behaviors of traders

Inner Product Spaces and Orthogonality

1 Short Introduction to Time Series

Numerical Analysis Lecture Notes

MATH 304 Linear Algebra Lecture 20: Inner product spaces. Orthogonal sets.

Robust Staff Level Optimisation in Call Centres

Open Problems in Quantum Information Processing. John Watrous Department of Computer Science University of Calgary

Quantum Computers. And How Does Nature Compute? Kenneth W. Regan 1 University at Buffalo (SUNY) 21 May, Quantum Computers

Quantum Monte Carlo and the negative sign problem

Part 1: Link Analysis & Page Rank

What does the number m in y = mx + b measure? To find out, suppose (x 1, y 1 ) and (x 2, y 2 ) are two points on the graph of y = mx + b.

1 Introduction. JS'12, Cnam Paris, 3-4 Avril Ourouk Jawad 1, David Lautru 2, Jean Michel Dricot, François Horlin, Philippe De Doncker 3

Numerology - A Case Study in Network Marketing Fractions

Auger width of metastable states in antiprotonic helium II

How To Find The Optimal Control Function On A Unitary Operation

Largest Fixed-Aspect, Axis-Aligned Rectangle

DATA ANALYSIS II. Matrix Algorithms

THE FUNDAMENTAL THEOREM OF ALGEBRA VIA PROPER MAPS

Lecture 11: 0-1 Quadratic Program and Lower Bounds

Introduction to Group Theory with Applications in Molecular and Solid State Physics

Time Ordered Perturbation Theory

Oscillations of the Sending Window in Compound TCP

The two dimensional heat equation

Financial Mathematics and Simulation MATH Spring 2011 Homework 2

1.2 GRAPHS OF EQUATIONS. Copyright Cengage Learning. All rights reserved.

Collatz Sequence. Fibbonacci Sequence. n is even; Recurrence Relation: a n+1 = a n + a n 1.

Using the ac Method to Factor

Let s examine the response of the circuit shown on Figure 1. The form of the source voltage Vs is shown on Figure 2. R. Figure 1.

Transcription:

First mean return time in decoherent quantum walks Péter Sinkovicz, János K. Asbóth, Tamás Kiss Wigner Research Centre for Physics Hungarian Academy of Sciences, April 0.

Problem statement Example: N=,,,... A ij = A ji transition amplitude First mean return time in decoherent quantum walks /

Problem Our numerical results Conclusion Arrival by absorption: 0i := ψ0 i First mean return time in decoherent quantum walks /

Problem Our numerical results Conclusion Arrival by absorption: 0i := ψ0 i First mean return time in decoherent quantum walks U 0i /

Problem Our numerical results Conclusion Arrival by absorption: 0i := ψ0 i U 0i or p := h0 U 0i First mean return time in decoherent quantum walks ψ i := [I 0ih0 ] U 0i q := hψ ψ i /

Conditional wave function: evolve these parts, which haven t come back where ψ t+ := [I 0 0 ] U ψ t p t := 0 U ψ t q t+ := ψ t+ ψ t+ p t probability: Prob(X t = 0 X n 0 if n < t) q t probability: Prob(X n 0 if n t) First mean return time in decoherent quantum walks /

Mean return time T := tp t q t := Trρ(t) t= t=0 t=0 where q t+ + p t+ = q t and q 0 = F. A. Grünbaum,A. H. Werner, R. F. Werner, (0) First mean return time in decoherent quantum walks /

Mean return time T := tp t q t := Trρ(t) t= t=0 t=0 where q t+ + p t+ = q t and q 0 = pt 0.0 0. N= example p t : 0.0 0. 0.0 0.0 0.00 0 0 0 t pt 0.0 0. 0.0 0. 0.0 0.0 0.00 0 0 0 t F. A. Grünbaum,A. H. Werner, R. F. Werner, (0) First mean return time in decoherent quantum walks /

Mean return time T := tp t q t := Trρ(t) t= t=0 t=0 where q t+ + p t+ = q t and q 0 = pt 0.0 0. N= example p t : 0.0 0. 0.0 0.0 0.00 0 0 0 t pt 0.0 0. 0.0 0. 0.0 0.0 0.00 0 0 0 t T is an integer number equal with the graph size independent of A ij transition amplitude T = N F. A. Grünbaum,A. H. Werner, R. F. Werner, (0) First mean return time in decoherent quantum walks /

Classical case : symmetric, regular graph T Kemeny, G.; Snell, L. (90) First mean return time in decoherent quantum walks /

Our interest? decoherence x First mean return time in decoherent quantum walks 7 /

Numerical study in order to find out?? T = N I) homogeneous decoherence II) Kraus representation III) Master equation First mean return time in decoherent quantum walks 8 /

Homogeneous decoherence For see a transition between Grünbaum et al. s and the Classical random walk we stick in a simplest decoherence. So one step of the process is the following: coherent time step decoherence C[ρ] = UρU measurement D[ρ] xy = dρ xy + ( d)ρ xx δ xy M[ρ] = [I 0 0 ] ρ [I 0 0 ] First mean return time in decoherent quantum walks 9 /

T numerical result U + homogeneous decoherence T = N N for d = unitary time evolution, and d = 0 is the classical case d 0 where d ρ t+ = M[T [ρ t ]] = M[D[C[ρ t ]]] d and D[ρ] = ρ dρ dρ... dρ ρ...... First mean return time in decoherent quantum walks 0 /

Numerical study in order to find out?? T = N I) homogeneous decoherence II) Kraus representation III) Master equation First mean return time in decoherent quantum walks /

Kraus representation In order to map all quantum channel we can use Kraus representation for the decoherence channel d ρ := D[ρ] = K µ ρk µ µ= ρ m,µ = D m,n µ,ν ρ n,ν trace preserving (probability preserving): d K µk µ = I D stochastic µ= may we will get some redundancy, because the measure and the unitary time evolution can be defined by Kraus operators First mean return time in decoherent quantum walks /

numerical guess unital map T [ N I] = N I T = N T [ ] = D[C[ ]] measure less process is unital (leave the totally mixed state invariant) only if d K µ K µ = I D T stochastic µ= trace preserving and untial map quantum walk ρ T [ρ] classical walk λ t Wλ t First mean return time in decoherent quantum walks /

Numerical study in order to find out?? T = N I) homogeneous decoherence II) Kraus representation III) Master equation First mean return time in decoherent quantum walks /

Master equation For check we formulated our guess in another language: T [ ] can be substitute with the time evolution which has generated by the t ρ = i [H, ρ] + L(ρ) L(ρ) = i,j κ i,j [ i j ρ j i ( i i ρ + ρ j j ) ] Master equation. First mean return time in decoherent quantum walks /

Master equation For check we formulated our guess in another language: T [ ] can be substitute with the time evolution which has generated by the t ρ = i [H, ρ] + L(ρ) L(ρ) = i,j κ i,j [ i j ρ j i ( i i ρ + ρ j j ) ] Master equation. numerical guess unital map L(I) = 0 κ i,j = κ j,i T = N First mean return time in decoherent quantum walks /

Conclusion I) numerically studied systems Kraus representation unitary quantum walk homogeneous decoherence inhomogeneous, but symmetric decoherence uni-stochastic map Master equation L(I) = 0 (unital map) II) analytically we need proof for numerical guess T [I] = I unital T = N First mean return time in decoherent quantum walks /