introduction to quantum information processing

41
Introduction to Quantum Information Processing Lecture 4 Michele Mosca

Upload: daktari

Post on 25-Feb-2016

41 views

Category:

Documents


0 download

DESCRIPTION

Introduction to Quantum Information Processing. Lecture 4. Michele Mosca. Overview. Von Neumann measurements General measurements Traces and density matrices and partial traces. “Von Neumann measurement in the computational basis”. - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: Introduction to Quantum Information Processing

Introduction to Quantum Information Processing

Lecture 4

Michele Mosca

Page 2: Introduction to Quantum Information Processing

Overview

Von Neumann measurements General measurements Traces and density matrices and

partial traces

Page 3: Introduction to Quantum Information Processing

“Von Neumann measurement in the computational basis”

Suppose we have a universal set of quantum gates, and the ability to measure each qubit in the basis

If we measure we get with probability

}1,0{2

bαb)10( 10

Page 4: Introduction to Quantum Information Processing

In section 2.2.5, this is described as follows

00P0 11P1

We have the projection operatorsand satisfying

We consider the projection operator or “observable”

Note that 0 and 1 are the eigenvalues When we measure this observable M, the

probability of getting the eigenvalue is and we

are in that case left with the state

IPP 10

110 PP1P0M

b2ΦΦ)Pr( bbPb α

bb)b(p

Pb

bb

Page 5: Introduction to Quantum Information Processing

“Expected value” of an observable

b If we associate with outcome the

eigenvalue then the expected outcome is

ΦΦΦΦ

ΦΦΦΦ

)Pr(

MTrbPTr

bPPb

bb

bb

bb

bb

b

b

Page 6: Introduction to Quantum Information Processing

“Von Neumann measurement in the computational basis”

Suppose we have a universal set of quantum gates, and the ability to measure each qubit in the basis

Say we have the state If we measure all n qubits, then we

obtain with probability Notice that this means that probability of

measuring a in the first qubit equals

}1,0{ xn}1,0{x

x

x 2x

0

1n}1,0{0x

2x

Page 7: Introduction to Quantum Information Processing

Partial measurements

(This is similar to Bayes Theorem)

xp1n}1,0{0x 0

x

0

1n}1,0{0x

2x0p

If we only measure the first qubit and leave the rest alone, then we still get with probability

The remaining n-1 qubits are then in the renormalized state

Page 8: Introduction to Quantum Information Processing

In section 2.2.5 This partial measurement corresponds to

measuring the observable1n1n I111I000M

Page 9: Introduction to Quantum Information Processing

Von Neumann Measurements

A Von Neumann measurement is a type of projective measurement. Given an orthonormal basis , if we perform a Von Neumann measurement with respect to of the state

then we measure with probability

}{ k

kk}{ k

k

kkkk

kk2

k2

k

TrTr

Page 10: Introduction to Quantum Information Processing

Von Neumann Measurements

E.x. Consider Von Neumann measurement of the state with respect to the orthonormal basis

Note that

210,2

10

2

1022

102

)10(

We therefore get with probability

2

10

22

Page 11: Introduction to Quantum Information Processing

Von Neumann Measurements

Note that22

10

2210 **

2210

210Tr

210

210

2

Page 12: Introduction to Quantum Information Processing

How do we implement Von Neumann

measurements? If we have access to a universal set of

gates and bit-wise measurements in the computational basis, we can implement Von Neumann measurements with respect to an arbitrary orthonormal basis

as follows.}{ k

Page 13: Introduction to Quantum Information Processing

How do we implement Von Neumann

measurements? Construct a quantum network that

implements the unitary transformationkU k

Then “conjugate” the measurement operation with the operation U

kk U k2

kprob

1U k

Page 14: Introduction to Quantum Information Processing

Another approach

kk U 1U

kk

000

kkk000k000

kkk

kkk

2kprob

Page 15: Introduction to Quantum Information Processing

Ex. Bell basis change

100101

Consider the orthonormal basis consisting of the “Bell” states

110000

110010 100111 Note that

xyxy

H

Page 16: Introduction to Quantum Information Processing

Bell measurement We can “destructively” measure

Or non-destructively project

xyy,x

y,x xy

H 2xyprob

xyy,x

y,x xyy,x

H

2xyprob 00

H

Page 17: Introduction to Quantum Information Processing

Most general measurement

kk

000 U

Page 18: Introduction to Quantum Information Processing

Trace of a matrixThe trace of a matrix is the sum of its diagonal elementse.g.

221100

222120

121110

020100

aaaaaaaaaaaa

Tr

Some properties:

i

ii AATrATrUAUTr

CABTrABCTrBATrABTr

ByTrAxTryBxATr

φφ

t][][

Orthonormal basis { }iφ

Page 19: Introduction to Quantum Information Processing

Density Matrices

Notice that 0=0|, and 1

=1|.So the probability of getting 0 when measuring | is:

220 0)0( p

ρφφ

φφφφ

φφφφ

0000

0000

0000

TrTr

Tr

where = || is called the density matrix for the state |

10 10 ααφ

Page 20: Introduction to Quantum Information Processing

Mixture of pure states

A state described by a state vector | is called a pure state.

What if we have a qubit which is known to be in the pure state |1 with probability p1, and in |2 with probability p2 ? More generally, consider probabilistic mixtures of pure states (called mixed states):

... , ,, , 2211 pp φφφ

Page 21: Introduction to Quantum Information Processing

Density matrix of a mixed state

…then the probability of measuring 0 is given by conditional probability:

i

iipp state pure given 0 measuring of prob.)0(

ρφφ

φφ

00

00

00

Tr

pTr

Trp

iiii

iiii

where

i

iiip is the density matrix for the mixed state

Density matrices contain all the useful information about an arbitrary quantum state.

Page 22: Introduction to Quantum Information Processing

Density MatrixIf we apply the unitary operation U to

the resulting state is with density matrix

U

tt UUUU

Page 23: Introduction to Quantum Information Processing

Density Matrix

If we apply the unitary operation U to the resulting state is with density matrix

kkq ψ, kk Uq ψ,

t

t

t

UU

UqU

UUq

kk

kk

kk

kk

ρ

ψψ

ψψ

Page 24: Introduction to Quantum Information Processing

Density Matrix

If we perform a Von Neumann measurement of the state wrt a basis containing , the probability of obtaining is

Tr2

Page 25: Introduction to Quantum Information Processing

Density MatrixIf we perform a Von Neumann

measurement of the state wrt a basis containing the

probability of obtaining is

kkq ψ,

φφρ

φφψψ

φφψψφψ

Tr

qTr

Trqq

kkkk

kkkk

kkk

2

Page 26: Introduction to Quantum Information Processing

Density Matrix

In other words, the density matrix contains all the information necessary to compute the probability of any outcome in any future measurement.

Page 27: Introduction to Quantum Information Processing

Spectral decomposition Often it is convenient to rewrite the

density matrix as a mixture of its eigenvectors

Recall that eigenvectors with distinct eigenvalues are orthogonal; for the subspace of eigenvectors with a common eigenvalue (“degeneracies”), we can select an orthonormal basis

Page 28: Introduction to Quantum Information Processing

Spectral decomposition In other words, we can always

“diagonalize” a density matrix so that it is written as

kk

kkp φφρ

where is an eigenvector with eigenvalue and forms an orthonormal basis

kφkp kφ

Page 29: Introduction to Quantum Information Processing

Partial Trace How can we compute probabilities

for a partial system? E.g.

yxp

p

yx

yx

y x y

xyy

y xxy

yxxy

,

Page 30: Introduction to Quantum Information Processing

Partial Trace If the 2nd system is taken away and

never again (directly or indirectly) interacts with the 1st system, then we can treat the first system as the following mixture

E.g.

ρρα

ρα

22,2 Trx

pp

yxp

p

x y

xyy

Trace

y x y

xyy

Page 31: Introduction to Quantum Information Processing

Partial Trace

ρρα

ρα

22,2 Trx

pp

yxp

p

x y

xyy

Trace

y x y

xyy

yyy

ypTr ΦΦ2 ρ x y

xyy x

p

αΦ

Page 32: Introduction to Quantum Information Processing

Why? the probability of measuring e.g.

in the first register depends only on

ρ

αα

2

22

ΦΦ

ΦΦ

TrwwTr

pwwTr

wwTrp

pp

yyy

y

yyy

y

y y y

wyywy

wρ2Tr

Page 33: Introduction to Quantum Information Processing

Partial Trace Notice that it doesn’t matter in

which orthonormal basis we “trace out” the 2nd system, e.g.

11001100 222 βαβα Tr

In a different basis

1

210

2110

211100 βαβα

1

210

2110

21 βα

Page 34: Introduction to Quantum Information Processing

Partial Trace

1100

101021

101021

22

**

**2

βα

βαβα

βαβα

Tr

1

210

2110

21 βα

1

210

2110

21 βα

Page 35: Introduction to Quantum Information Processing

Distant transformations don’t change the local density

matrix Notice that the previous observation

implies that a unitary transformation on the system that is traced out does not affect the result of the partial trace

I.e.

ρρ

ρ

22,2 Φ

Φ

Trp

UIyUp

yyTrace

yyy

Page 36: Introduction to Quantum Information Processing

Distant transformations don’t change the local density

matrix In fact, any legal quantum

transformation on the traced out system, including measurement (without communicating back the answer) does not affect the partial trace

I.e. ρρ 22,

2 Φ

Φ,

Trp

yp

yyTrace

yy

Page 37: Introduction to Quantum Information Processing

Why?? Operations on the 2nd system should

not affect the statistics of any outcomes of measurements on the first system

Otherwise a party in control of the 2nd system could instantaneously communicate information to a party controlling the 1st system.

Page 38: Introduction to Quantum Information Processing

Principle of implicit measurement

If some qubits in a computation are never used again, you can assume (if you like) that they have been measured (and the result ignored)

The “reduced density matrix” of the remaining qubits is the same

Page 39: Introduction to Quantum Information Processing

Partial Trace This is a linear map that takes

bipartite states to single system states.

We can also trace out the first system

We can compute the partial trace directly from the density matrix description

kijljlkiljTrkiljkiTr

2

Page 40: Introduction to Quantum Information Processing

Partial Trace using matrices

Tracing out the 2nd system

33223120

13021100

3332

2322

3130

2120

1312

0302

1110

0100

33323130

23222120

13121110

03020100

2

aaaaaaaa

aaaa

Traaaa

Tr

aaaa

Traaaa

Tr

aaaaaaaaaaaaaaaa

Tr

Page 41: Introduction to Quantum Information Processing

Most general measurement

000 U 0000002 Tr