quantum channels and their capacities
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Quantum Channels and their capacities. Graeme Smith, IBM Research 11 th Canadian Summer School on Quantum Information. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A. Information Theory. - PowerPoint PPT PresentationTRANSCRIPT
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Quantum Channels and their capacities
Graeme Smith, IBM Research
11th Canadian Summer School on Quantum Information
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Information Theory
• “A Mathematical Theory of Communication”, C.E. Shannon, 1948
• Lies at the intersection of Electrical Engineering, Mathematics, and Computer Science
• Concerns the reliable and efficient storage and transmission of information.
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Information Theory: Some Hits
• Source Coding: Cell Phones
Lempel-Ziv compression (gunzip, winzip, etc)
Voyager (Reed Solomon codes)
Low density parity check codes
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Quantum Information Theory
When we include quantum mechanics (which was there all along!) things get much more interesting!
Secure communication, entanglement enhanced communication, sending quantum information,…
Capacity, error correction, compression, entropy..
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Outline
• Lecture 1: Classical Information Theory
• Lecture 2: Quantum Channels and their many capacities
• Lecture 3: Advanced Topics--- Additivity, Partial Transpose, LOCC,Gaussian Noise,etc
• Lecture 4: Advanced Topics---Additivity, Partial Transpose, LOCC,Gaussian Noise,etc.
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Example: Flipping a biased coinLet’s say we flip n coins. They’re independent and identically distributed (i.i.d):
Pr( Xi = 0 ) = 1-p Pr( Xi = 1 ) = p
Pr( Xi = xi, Xj = xj ) = Pr( Xi = xi ) Pr( Xj = xj )
x1x2 … xn
Q: How many 1’s am I likely to get?
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Example: Flipping a biased coinLet’s say we flip n coins. They’re independent and identically distributed (i.i.d):
Pr( Xi = 0 ) = 1-p Pr( Xi = 1 ) = p
Pr( Xi = xi, Xj = xj ) = Pr( Xi = xi ) Pr( Xj = xj )
x1x2 … xn
Q: How many 1’s am I likely to get?
A: Around pn and, with very high probability between (p-)n and (pn
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Shannon EntropyFlip n i.i.d. coins, Pr( Xi = 0) = p, Pr( Xi = 1) = 1-p Outcome: x1…xn.
w.h.p. get ¼ pn 1’s, but how many different configurations?
There are such strings.
Using we get
Where H(p) = -plogp – (1-p)log(1-p)
So, now, if I want to transmit x_1…x_n, I can just check which typical sequence, and report that! Maps n bits to nH(p)
Similar for larger alphabet:
¡ npn
¢= n!
(pn)!((1¡ p)n)!
logn!= n logn ¡ n + O(logn)
logµ
npn
¶¼ nlogn ¡ n ¡ pn log(pn) + pn ¡ (1¡ p)n log((1¡ p)n) + (1¡ p)n
= nH (p)
H(X ) =P
x ¡ p(x) logp(x)
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Shannon Entropy
Flip n i.i.d. coins, Pr( Xi = xi) = p(xi)x1…xn has prob P(x1…xn) = p(x1)…p(xn)
Typically,
So, typical sequences have
More or less uniform dist over typical sequences.
logP (x1:::xn) =P n
i=1 logp(xi )
¡ 1n logP (x1:::xn) ¼¡ hlogp(x)i = H(X )
P (x1:::xn) » 12n (H (X ) § ±)
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Correlations and Mutual Information
H(X) – bits of information transmitted by letter from ensemble X
Noisy Channel:p(y|x)
(X,Y) correlated. How much does Y tell us about X?
After I get Y, how much more do you need to tell me so that I know X?
Given y, update expectations:
Only bits per letter needed.
Can calculate
Savings: H(X)–H(X|Y) = H(X) + H(Y) – H(X,Y) =: I(X;Y)
N NX Y
p(xjy) = p(yjx)p(x)p(y)
H(X jY ) = h¡ logp(xjy)i
H (X jY ) = h¡ log p(x;y)p(y) i = H (X ;Y ) ¡ H (Y )
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Channel Capacity
m0¼m
N N
N N
N N
.
.
.
Decoder
mEncoder
Given n uses of a channel, encode a message m 2 {1,…,M} to a codeword xn = (x1(m),…, xn(m))
At the output of the channel, use yn = (y1,…,yn) to make a guess, m0.
The rate of the code is (1/n)log M.
The capacity of the channel, C(N), is defined as the maximum rate you can get with vanishing error probability as n ! 1
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Binary Symmetric Channel
N NX Y
p(0|0) = 1-p p(1|0) = p
p(0|1) = p p(1|1) = 1-p
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Capacity of Binary Symmetric Channel
Input string
xn =(x1,…, xn)
2n possible outputs
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Capacity of Binary Symmetric Channel
Input string
xn =(x1,…, xn)
2n possible outputs2nH(p) typical errors
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Capacity of Binary Symmetric Channel
Input string
x1n =(x11,…, x1n)
2n possible outputs2nH(p) typical errors
x2n =(x21,…, x2n)
2nH(p)
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Capacity of Binary Symmetric Channel
Input string
x1n =(x11,…, x1n)
2n possible outputs2nH(p) typical errors
x2n =(x21,…, x2n)
xMn =(xM1,…, xMn)
.
.
.
2nH(p)
2nH(p)
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Capacity of Binary Symmetric Channel
Input string
x1n =(x11,…, x1n)
2n possible outputs2nH(p) typical errors
x2n =(x21,…, x2n)
xMn =(xM1,…, xMn)
.
.
.
2nH(p)
2nH(p)
Each xmn gets
mapped to 2nH(p) different outputs.
If these sets overlap for different inputs, the decoder will be confused.
So, we need
M 2nH(p) · 2n, which implies
(1/n)log M · 1-H(p)
Upper bound on capacity
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Direct Coding Theorem:Achievability of 1-H(p)
(1) Choose 2nR codewords randomly according to Xn (50/50 variable)
(2) xmn ! yn. To decode, look at all strings within 2n(H(p)+ ) bit-
flips of yn. If this set contains exactly one codeword, decode to that. Otherwise, report error.
Decoding sphere is big enough that w.h.p. the correct codeword xm
n is in there.
So, the only source of error is if two codewords are in there. What are the chances of that???
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Direct coding theorem: Achievablility of 1-H(p)
2n total
Random codeword
Mapped here
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Direct coding theorem: Achievablility of 1-H(p)
2n total
Size 2n(H(p) + )
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Direct coding theorem: Achievablility of 1-H(p)
2n total
Size 2n(H(p) + )If code is chosen randomly, what’s the chance of another codeword in this ball?
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Direct coding theorem: Achievablility of 1-H(p)
2n total
Size 2n(H(p) + )If code is chosen randomly, what’s the chance of another codeword in this ball?
If I choose one more word, the chance is 2n (H (p)+ ±)
2n
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Direct coding theorem: Achievablility of 1-H(p)
2n total
Size 2n(H(p) + )If code is chosen randomly, what’s the chance of another codeword in this ball?
If I choose one more word, the chance is 2n (H (p)+ ±)
2n
Choose 2nR more, the chance is 2n (H (p)+ R + ±)
2n
If R < 1 – H(p) – d, this ! 0 as n ! 1
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Direct coding theorem: Achievablility of 1-H(p)
So, the average probability of decoding error (averaged over codebook choice and codeword) is small.
As a result, there must be some codebook with low prob of error (averaged over codewords).
2n total
Size 2n(H(p) + )If code is chosen randomly, what’s the chance of another codeword in this ball?
If I choose one more word, the chance is 2n (H (p)+ ±)
2n
Choose 2nR more, the chance is 2n (H (p)+ R + ±)
2n
If R < 1 – H(p) – d, this ! 0 as n ! 1
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Low worst-case probability of error
Showed: there’s a code with rate R such that
Let N2 = #{i | Pi > 2}. Then,
So that N2 < 2nR - 1. Throw these away.
Gives a code with rate R – 1/n and Pi < 2 for all i.
12n R
P 2n R
i=1 Pi < ²
2²N 2²2n R · 1
2n R
Pi st P i >2² Pi · 1
2n R
P 2n R
i=1 Pi < ²
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Coding theorem: general case
• 2nH(X) typical xn
• For typical yn, ¼ 2n(H(X|Y) + ) candidate xn’s in decoding sphere. If unique candidate, report this.
• Each sphere of size 2n(H(X|Y)+) contains a fraction of the inputs.
• If we choose 2nR codewords, prob of accidentally falling into wrong sphere is
• Okay as long as R < I(X;Y)
N NX Y
2n (H (X j Y )+ ±)
2n H (X )
2n R
2n (H (X ) ¡ H (X j Y ) ¡ ±) = 2n R
2n I (X ;Y ) ¡ ±
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Capacity for general channel
• We showed that for any input distribution p(x), given p(y|x), we can approach rate R = I(X;Y). By picking the best X, we can achieve C(N) = maxXI(X;Y). This is called the “direct” part of the capacity theorem.
• In fact, you can’t do any better. Proving there’s no way to beat maxXI(X;Y) is called the “converse”.
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Converse
For homework, you’ll prove two great results about entropy:
1. H(X,Y) · H(X)+ H(Y)
2. H(Y1Y2|X1X2) · H(Y1|X1) + H(Y2|X2)
We’re going to use the first one to prove that no good code can transmit at a rate better than maxX I(X;Y)
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Converse• Choose some code with 2nR strings of n letters.• Let Xn be uniform distribution on the codewords (codeword i with
prob 1/2nR)• Because the channels are independent, we get p(y1…yn|x1…xn) =
p(y1|x1)…p(yn|xn)• As a result, the conditional entropy satisfies H(Yn|Xn) = h –log p(y1…
yn|x1…xn) i = h –log p(yi|xi)i = H(Yi|Xi).• Now, I(Yn;Xn) = H(Yn) – H(Yn|Xn) · H(Yi) - H(Yi|Xi) = I(Yi;Xi) · n
maxX I(X;Y)
• Furthermore, I(Yn;Xn) = H(Xn)-H(Xn|Yn) = nR - H(Xn|Yn)· n maxX I(X;Y).
• Finally, since Yn must be sufficient to decode Xn, we must have (1/n)H(Yn|Xn) ! 0, which means R · maxX I(X;Y).
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Recap
• Information theory is concerned with efficient and reliable transmission and storage of data.
• Focused on the problem of communication in the presence of noise. Requires error correcting codes.
• Capacity is the maximum rate of communication for a noisy channel. Given by maxX I(X;Y).
• Two steps to capacity proof: (1) direct part: show by randomized argument that there exist codes with low probability of error. Hard part: error estimates. (2) Converse: there are no codes that do better---use entropy inequalities.
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Coming up:
Quantum channels and their various capacities.
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• Homework is due at the Beginning of next class.
• Fact II is called Jensen’s inequality
• No partial credit