1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.

Slides:



Advertisements
Similar presentations
Department of Computer Science & Engineering University of Washington
Advertisements

Puzzle Twin primes are two prime numbers whose difference is two.
Quantum Computing MAS 725 Hartmut Klauck NTU
Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Lecture 11 (2005) Richard Cleve DC 3524
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.
Quantum One: Lecture 16.
1 Introduction to Quantum Information Processing QIC 710 / CS 667 / PH 767 / CO 681 / AM 871 Richard Cleve DC 2117 / RAC 2211 Lecture.
DENSITY MATRICES, traces, Operators and Measurements
Advanced Computer Architecture Lab University of Michigan Quantum Operations and Quantum Noise Dan Ernst Quantum Noise and Quantum Operations Dan Ernst.
Superdense coding. How much classical information in n qubits? Observe that 2 n  1 complex numbers apparently needed to describe an arbitrary n -qubit.
Introduction to Quantum Information Processing Lecture 4 Michele Mosca.
CSEP 590tv: Quantum Computing
1 Recap (I) n -qubit quantum state: 2 n -dimensional unit vector Unitary op: 2 n  2 n linear operation U such that U † U = I (where U † denotes the conjugate.
Dirac Notation and Spectral decomposition
Matrices CS485/685 Computer Vision Dr. George Bebis.
1 Introduction to Quantum Information Processing QIC 710 / CS 768 / PH 767 / CO 681 / AM 871 Richard Cleve QNC 3129 Lecture 18 (2014)
1 Introduction to Quantum Information Processing CS 667 / PH 767 / CO 681 / AM 871 Richard Cleve DC 2117 Lecture 14 (2009)
Linear Algebra Review 1 CS479/679 Pattern Recognition Dr. George Bebis.
1 Introduction to Quantum Information Processing QIC 710 / CS 678 / PH 767 / CO 681 / AM 871 Richard Cleve DC 2117 / QNC 3129 Lectures.
1 Introduction to Quantum Information Processing QIC 710 / CS 667 / PH 767 / CO 681 / AM 871 Richard Cleve DC 2117 Lecture 16 (2011)
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 667 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 2117 Lecture.
October 1 & 3, Introduction to Quantum Computing Lecture 2 of 2 Richard Cleve David R. Cheriton School of Computer Science Institute for Quantum.
1 Introduction to Quantum Information Processing CS 667 / PH 767 / CO 681 / AM 871 Richard Cleve DC 2117 Lecture 19 (2009)
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.
October 1 & 3, Introduction to Quantum Computing Lecture 1 of 2 Introduction to Quantum Computing Lecture 1 of 2
Quantum One: Lecture Representation Independent Properties of Linear Operators 3.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 667 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 2117 Lecture.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.
Michael A. Nielsen University of Queensland Density Matrices and Quantum Noise Goals: 1.To review a tool – the density matrix – that is used to describe.
Elementary Linear Algebra Anton & Rorres, 9th Edition
1 Introduction to Quantum Information Processing CS 667 / PH 767 / CO 681 / AM 871 Richard Cleve DC 2117 Lecture 20 (2009)
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.
Quantum Computing Reversibility & Quantum Computing.
8.4.2 Quantum process tomography 8.5 Limitations of the quantum operations formalism 量子輪講 2003 年 10 月 16 日 担当:徳本 晋
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 667 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 653 Lecture.
DYNAMICS OF OPEN Q-SYSTES FROM A PERSPECTIVE OF QIT IMS, Imperial College, London, 18 January 2007 Vladimír Bužek Research Center for Quantum Information.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.
Classical Control in Quantum Programs Dominique Unruh IAKS, Universität Karlsruhe Founded by the European Project ProSecCo IST
Instructor: Mircea Nicolescu Lecture 8 CS 485 / 685 Computer Vision.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.
Richard Cleve DC 2117 Introduction to Quantum Information Processing QIC 710 / CS 667 / PH 767 / CO 681 / AM 871 Lecture (2011)
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 667 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 2117 Lecture.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.
Density matrix and its application. Density matrix An alternative of state-vector (ket) representation for a certain set of state-vectors appearing with.
1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course.
Richard Cleve DC 3524 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 667 / Phys 767 C&O 481 / C&O 681 Lecture.
Richard Cleve DC 2117 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 667 / Phys 767 C&O 481 / C&O 681 Lecture.
Richard Cleve DC 2117 Introduction to Quantum Information Processing CS 667 / PH 767 / CO 681 / AM 871 Lecture 16 (2009) Richard.
Richard Cleve DC 2117 Introduction to Quantum Information Processing CS 667 / PH 767 / CO 681 / AM 871 Lecture 15 (2009) Richard.
Postulates of Quantum Mechanics
Introduction to Quantum Computing Lecture 1 of 2
For computer scientists
CS485/685 Computer Vision Dr. George Bebis
Quantum One.
Quantum One.
Richard Cleve DC 2117 Introduction to Quantum Information Processing QIC 710 / CS 667 / PH 767 / CO 681 / AM 871 Lecture 15 (2011)
Maths for Signals and Systems Linear Algebra in Engineering Lectures 13 – 14, Tuesday 8th November 2016 DR TANIA STATHAKI READER (ASSOCIATE PROFFESOR)
Richard Cleve DC 2117 Introduction to Quantum Information Processing CS 667 / PH 767 / CO 681 / AM 871 Lecture 16 (2009) Richard.
Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Lecture 17 (2005) Richard Cleve DC 3524
Linear Vector Space and Matrix Mechanics
Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Lecture 4 (2005) Richard Cleve DC 653
Richard Cleve DC 3524 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 667 / Phys 767 C&O 481 / C&O 681 Lecture.
Presentation transcript:

1 Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681 Richard Cleve DC 3524 Course web site at: Lecture 12 (2005)

2 Contents Correction to Lecture 11: 2/3   2/3 Distinguishing mixed states Basic properties of the trace Recap: general quantum operations Partial trace POVMs Simulations among operations Separable states Continuous-time evolution

3 Correction to Lecture 11: 2/3   2/3 Distinguishing mixed states Basic properties of the trace Recap: general quantum operations Partial trace POVMs Simulations among operations Separable states Continuous-time evolution

4 General quantum operations (III) Example 3 (trine state “measurent”): Let  0  =  0 ,  1  =  1/2  0  +  3/2  1 ,  2  =  1/2  0    3/2  1  Then The probability that state  k  results in “outcome” A k is 2/3, and this can be adapted to actually yield the value of k with this success probability Define A 0 =  2/3  0  0  A 1 =  2/3  1  1  A 2 =  2/3  2  2  Correction to Lecture 11:  2/3 instead of 2/3

5 Correction to Lecture 11: 2/3   2/3 Distinguishing mixed states Basic properties of the trace Recap: general quantum operations Partial trace POVMs Simulations among operations Separable states Continuous-time evolution

6 Distinguishing mixed states (I)  0  with prob. ½  0  +  1  with prob. ½  0  with prob. ½  1  with prob. ½  0  with prob. cos 2 (  /8)  1  with prob. sin 2 (  /8) 00 ++ 00 11  0  with prob. ½  1  with prob. ½ What’s the best distinguishing strategy between these two mixed states?  1 also arises from this orthogonal mixture: … as does  2 from:

7 Distinguishing mixed states (II) 00 ++ 00 11 We’ve effectively found an orthonormal basis   0 ,   1  in which both density matrices are diagonal: Rotating   0 ,   1  to  0 ,  1  the scenario can now be examined using classical probability theory: Question: what do we do if we aren’t so lucky to get two density matrices that are simultaneously diagonalizable? Distinguish between two classical coins, whose probabilities of “heads” are cos 2 (  /8) and ½ respectively (details: exercise) 11

8 Correction to Lecture 11: 2/3   2/3 Distinguishing mixed states Basic properties of the trace Recap: general quantum operations Partial trace POVMs Simulations among operations Separable states Continuous-time evolution

9 Basic properties of the trace The trace of a square matrix is defined as It is easy to check that The second property implies and Calculation maneuvers worth remembering are: and Also, keep in mind that, in general,

10 Correction to Lecture 11: 2/3   2/3 Distinguishing mixed states Basic properties of the trace Recap: general quantum operations Partial trace POVMs Simulations among operations Separable states Continuous-time evolution

11 Recap: general quantum ops Example: applying U to  yields U  U † General quantum operations (a.k.a. “completely positive trace preserving maps” ): Let A 1, A 2, …, A m be matrices satisfying Then the mappingis a general quantum op

12 Correction to Lecture 11: 2/3   2/3 Distinguishing mixed states Basic properties of the trace Recap: general quantum operations Partial trace POVMs Simulations among operations Separable states Continuous-time evolution

13 Partial trace (I) In such circumstances, if the second register (say) is discarded then the state of the first register remains  Two quantum registers (e.g. two qubits) in states  and  (respectively) are independent if then the combined system is in state  =    In general, the state of a two-register system may not be of the form    (it may contain entanglement or correlations) We can define the partial trace, Tr 2 , as the unique linear operator satisfying the identity Tr 2 (    ) =  For example, it turns out that Tr 2 () =) = index means 2 nd system traced out

14 Partial trace (II) We’ve already seen this defined in the case of 2-qubit systems: discarding the second of two qubits Let A 0 = I   0  and A 1 = I   1  For the resulting quantum operation, state    becomes  For d -dimensional registers, the operators are A k = I   k , where  0 ,  1 , …,  d  1  are an orthonormal basis

15 Partial trace (III) For 2-qubit systems, the partial trace is explicitly and

16 Correction to Lecture 11: 2/3   2/3 Distinguishing mixed states Basic properties of the trace Recap: general quantum operations Partial trace POVMs Simulations among operations Separable states Continuous-time evolution

17 POVMs (I) Positive operator valued measurement (POVM): Let A 1, A 2, …, A m be matrices satisfying Then the corresponding POVM is a stochastic operation on  that, with probability produces the outcome: j (classical information) (the collapsed quantum state) Example 1: A j =  j  j  (orthogonal projectors) This reduces to our previously defined measurements …

18 POVMs (II) Moreover, When A j =  j  j  are orthogonal projectors and  = , = Tr  j  j  j  j  =  j  j  j  j  =  j  2

19 POVMs (III) Example 3 (trine state “measurent”): Let  0  =  0 ,  1  =  1/2  0  +  3/2  1 ,  2  =  1/2  0    3/2  1  Then If the input itself is an unknown trine state,  k  k , then the probability that classical outcome is k is 2/3 = … Define A 0 =  2/3  0  0  A 1 =  2/3  1  1  A 2 =  2/3  2  2 

20 Correction to Lecture 11: 2/3   2/3 Distinguishing mixed states Basic properties of the trace Recap: general quantum operations Partial trace POVMs Simulations among operations Separable states Continuous-time evolution

21 Simulations among operations (I) Fact 1: any general quantum operation can be simulated by applying a unitary operation on a larger quantum system: U 00 00 00   Example: decoherence 00  0  +  1  output discard input

22 Simulations among operations (II) Fact 2: any POVM can also be simulated by applying a unitary operation on a larger quantum system and then measuring: U 00 00 00   quantum output input classical output j

23 Correction to Lecture 11: 2/3   2/3 Distinguishing mixed states Basic properties of the trace Recap: general quantum operations Partial trace POVMs Simulations among operations Separable states Continuous-time evolution

24 Separable states product state if  =  separable state if A bipartite (i.e. two register) state  is a: Question: which of the following states are separable? (i.e. a probabilistic mixture of product states) ( p 1,…, p m  0 )

25 Correction to Lecture 11: 2/3   2/3 Distinguishing mixed states Basic properties of the trace Recap: general quantum operations Partial trace POVMs Simulations among operations Separable states Continuous-time evolution

26 Continuous-time evolution Although we’ve expressed quantum operations in discrete terms, in real physical systems, the evolution is continuous 00 11 Let H be any Hermitian matrix and t  R Then e iHt is unitary – why? H = U † DU, where Therefore e iHt = U † e iDt U = (unitary)

27