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Quantum Shannon Theory. Patrick Hayden (McGill). http://www.cs.mcgill.ca/~patrick/QLogic2005.ppt 17 July 2005, Q-Logic Meets Q-Info. Overview. Part I: What is Shannon theory? What does it have to do with quantum mechanics? Some quantum Shannon theory highlights Part II:

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Quantum Shannon Theory

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Quantum shannon theory

Quantum Shannon Theory

Patrick Hayden (McGill)

http://www.cs.mcgill.ca/~patrick/QLogic2005.ppt

17 July 2005, Q-Logic Meets Q-Info


Overview

Overview

  • Part I:

    • What is Shannon theory?

    • What does it have to do with quantum mechanics?

    • Some quantum Shannon theory highlights

  • Part II:

    • Resource inequalities

    • A skeleton key


Information shannon theory

Information (Shannon) theory

  • A practical question:

    • How to best make use of a given communications resource?

  • A mathematico-epistemological question:

    • How to quantify uncertainty and information?

  • Shannon:

    • Solved the first by considering the second.

    • A mathematical theory of communication [1948]

The


Quantifying uncertainty

Quantifying uncertainty

  • Entropy: H(X) = - xp(x) log2p(x)

  • Proportional to entropy of statistical physics

  • Term suggested by von Neumann (more on him soon)

  • Can arrive at definition axiomatically:

    • H(X,Y) = H(X) + H(Y) for independent X, Y, etc.

  • Operational point of view…


Compression

{0,1}n: 2n possible strings

2nH(X)typical strings

Compression

Source of independent copies of X

If X is binary:

0000100111010100010101100101

About nP(X=0) 0’s and nP(X=1) 1’s

X2 …

X1

Xn

Can compress n copies of X to

a binary string of length ~nH(X)


Quantifying information

H(Y)

Uncertainty in X

when value of Y

is known

H(X|Y)

I(X;Y)

Information is that which reduces uncertainty

Quantifying information

H(X)

H(X,Y)

H(Y|X)

H(X|Y)= H(X,Y)-H(Y)

= EYH(X|Y=y)

I(X;Y) = H(X) – H(X|Y) = H(X)+H(Y)-H(X,Y)


Sending information through noisy channels

´

m’

m

Decoding

Encoding

Shannon’s noisy coding theorem: In the limit of many uses, the optimal

rate at which Alice can send messages reliably to Bob through  is

given by the formula

Sending information through noisy channels

Statistical model of a noisy channel:


Shannon theory provides

Shannon theory provides

  • Practically speaking:

    • A holy grail for error-correcting codes

  • Conceptually speaking:

    • A operationally-motivated way of thinking about correlations

  • What’s missing (for a quantum mechanic)?

    • Features from linear structure:Entanglement and non-orthogonality


Quantum shannon theory provides

Quantum Shannon Theory provides

  • General theory of interconvertibility between different types of communications resources: qubits, cbits, ebits, cobits, sbits…

  • Relies on a

    • Major simplifying assumption:

      Computation is free

    • Minor simplifying assumption:

      Noise and data have regular structure


Quantifying uncertainty1

Quantifying uncertainty

  • Let  = x p(x) |xihx| be a density operator

  • von Neumann entropy:H() = - tr [ log ]

  • Equal to Shannon entropy of  eigenvalues

  • Analog of a joint random variable:

    • AB describes a composite system A ­ B

    • H(A) = H(A) = H( trBAB)


Compression1

No statistical assumptions:

Just quantum mechanics!

B­ n

(aka typical subspace)

dim(Effective supp of B­ n ) ~ 2nH(B)

Compression

Source of independent copies of AB:

­

­ 

­…

A

A

A

B

B

B

Can compress n copies of B to

a system of ~nH(B) qubits while

preserving correlations with A

[Schumacher, Petz]


Quantifying information1

H(B)

Uncertainty in A

when value of B

is known?

H(A|B)

|iAB=|0iA|0iB+|1iA|1iB

Quantifying information

H(A)

H(AB)

H(B|A)

H(A|B)= H(AB)-H(B)

H(A|B) = 0 – 1 = -1

Conditional entropy can

be negative!

B = I/2


Quantifying information2

H(B)

Uncertainty in A

when value of B

is known?

H(A|B)

I(A;B)

Information is that which reduces uncertainty

Quantifying information

H(A)

H(AB)

H(B|A)

H(A|B)= H(AB)-H(B)

I(A;B) = H(A) – H(A|B) = H(A)+H(B)-H(AB)

¸ 0


Data processing inequality strong subadditivity

I(A;B)

Data processing inequality(Strong subadditivity)

Alice

Bob

time

U

I(A;B)

I(A;B)¸ I(A;B)


Sending classical information through noisy channels

Encoding

( state)

Decoding

(measurement)

m’

m

HSW noisy coding theorem: In the limit of many uses, the optimal

rate at which Alice can send messages reliably to Bob through  is

given by the (regularization of the) formula

where

Sending classical information through noisy channels

Physical model of a noisy channel:

(Trace-preserving, completely positive map)


Sending classical information through noisy channels1

Encoding

( state)

Decoding

(measurement)

m’

m

2nH(B|A)

2nH(B|A)

2nH(B|A)

Sending classical information through noisy channels

B­ n

2nH(B)

X1,X2,…,Xn


Sending quantum information through noisy channels

Encoding

(TPCP map)

Decoding

(TPCP map)

‘

|i2 Cd

LSD noisy coding theorem: In the limit of many uses, the optimal

rate at which Alice can reliably send qubits to Bob (1/n log d) through 

is given by the (regularization of the) formula

Conditional

entropy!

where

Sending quantum information through noisy channels

Physical model of a noisy channel:

(Trace-preserving, completely positive map)


Entanglement and privacy more than an analogy

Sets of size 2n(I(X;Z)+)

All x

Random 2n(I(X;Y)-) x

Entanglement and privacy: More than an analogy

y=y1 y2 … yn

x = x1 x2 … xn

p(y,z|x)

z = z1 z2 … zn

How to send a private message from Alice to Bob?

Can send private messages at rate I(X;Y)-I(X;Z)

AC93


Entanglement and privacy more than an analogy1

Sets of size 2n(I(X:E)+)

All x

Random 2n(I(X:A)-) x

Entanglement and privacy: More than an analogy

|iBE = U­ n|xi

UA’->BE­ n

|xiA’

How to send a private message from Alice to Bob?

Can send private messages at rate I(X:A)-I(X:E)

D03


Entanglement and privacy more than an analogy2

Sets of size 2n(I(X:E)+)

All x

Random 2n(I(X:A)-) x

H(E)=H(AB)

Entanglement and privacy: More than an analogy

x px1/2|xiA|xiBE

UA’->BE­ n

x px1/2|xiA|xiA’

How to send a private message from Alice to Bob?

SW97

D03

Can send private messages at rate I(X:A)-I(X:E)=H(A)-H(E)


Notions of distinguishability

Notions of distinguishability

Basic requirement: quantum channels do not increase “distinguishability”

Fidelity

Trace distance

F(,)={Tr[(1/21/2)1/2]}2

T(,)=|-|1

F=0 for perfectly distinguishable

F=1 for identical

T=2 for perfectly distinguishable

T=0 for identical

F(,)=max |h|i|2

T(,)=2max|p(k=0|)-p(k=0|)|

where max is over POVMS {Mk}

F((),()) ¸ F(,)

T(,) ¸ T((,())

Statements made today hold for both measures


Conclusions part i

Conclusions: Part I

  • Information theory can be generalized to analyze quantum information processing

  • Yields a rich theory, surprising conceptual simplicity

  • Operational approach to thinking about quantum mechanics:

    • Compression, data transmission, superdense coding, subspace transmission, teleportation


Quantum shannon theory

Some references:

Part I: Standard textbooks:

* Cover & Thomas, Elements of information theory.

* Nielsen & Chuang, Quantum computation and quantum information.

(and references therein)

Part II: Papers available at arxiv.org:

* Devetak, The private classical capacity and quantum capacity of a

quantum channel, quant-ph/0304127

* Devetak, Harrow & Winter, A family of quantum protocols,

quant-ph/0308044.

* Horodecki, Oppenheim & Winter, Quantum information can be

negative, quant-ph/0505062


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