quantum computing mas 725 hartmut klauck ntu 9.4.2012 texpoint fonts used in emf. read the texpoint...
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Quantum ComputingMAS 725Hartmut KlauckNTU9.4.2012
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Today
Lower bounds in the black box model polynomial method adversary method
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The black box model
Input x1,…,xn with query access We want to compute some function f(x1,…,xn) Examples:
Deutsch‘s problem: x1,x2 are the values g(0) and g(1) for a function g, we want to know if g is balanced) Compute parity of x1,x2
Grover: Find i mit xi=1, consider f=OR
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The black box model
We measure the number of queries an algorithm does in the worst case, cost of an algorithm Not time, space etc.
The query complexity of a function f is the minimal cost of an algorithm computing f
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Types of algorithms
Deterministic algorithms: classical, no error, the complexity is denoted D(f)
Randomized algorithms: allow error probability 1/3, over the randomness of the algorithm, complexity R(f)
Quantum algorithms: We count the number of quantum queries. With error 1/3: Q(f) Withour error: QE(f)
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Examples
We know some bounds already: QE(XOR2)=1, but R(XOR2)=2 QE(XORn)· d n/2e Q(OR)=O(n1/2) R(OR)=(n) Q(OR)=(n1/2)
How can we show quantum lower bounds in general? How much better can quantum algorithms be compared to
randomized algorithms?
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Examples
We showed the lower bound for OR with an adversary argument
Are there other more general techniques?
For certain problems (Simon, Period Finding) we have seen exponential speedups
What is the largest speedup for a total Boolean function?
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What is the hardest function?
Consider ID: ID(x)=x How many queries are needed to compute ID?
R(ID)=n We get no information on a position we do not
query Formally: apply the Yao principle: fix random
string, then a deterministic algo with n-1 queries must have error 1/2.
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Q(ID)· n/2+O(n1/2)
Ansatz: We use the sign oracle (-1)xi |ii Formula for Hadamard transform:
Another Hadamard maps back to |xi So suffices to generate this superposition Doing this exactly needs n queries Number of queries corresponds to |y| Use only y with|y|· n/2+O(n1/2)
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Algorithm
Generate uniform superposition over all y with |y|· n/2+O(n1/2)
On |yi query all bits xi with yi=1 Apply Hadamard
Success probability? 99% of all strings have Hamming weight at most
n/2+O(n1/2) Hence resulting superposition is close to the
desired one and we will have small error For example error 5% with n/2+n1/2 queries
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Conclusion
n/2· Q(ID)· n/2+n1/2
...if we can show Q(XOR)¸ n/2
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The polynomial method
A quantum black box algorithm is a sequence of unitaries and query unitaries
We construct a low degree polynomial from such an algorithm that represents the computed function
Then we analyze the minimum degree for such polynomials
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The polynomial method
We claim that the amplitudes of the final state of a T query algorithm can be written as polynomials of degree T
Proof by induction: T=0: amplitudes do not depend on the input, i.e. are constants T! T+1: i(x) is given by a degree T polynomial Next we apply a unitary that does not depend on x The new ®i(x) is a linear combination of degree T polynomials,
degree unchanged The query transformation: state |ii|ai|ki maps to |ii|a© xii|ki The new iak(x) is xi¢ i, a©1, k + (1-xi)¢ i, a, k Degree is no more than T+1
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The polynomial method
The acceptance probability on input x is the sum of squred amplitudes
Hence can be written as a polynomial of degree 2T We may replace xi
k by xi
The result is multilinear
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Conclusion
Given a quantum algorithm with T queries that computed f exactly we get a multilinear polynomial of degree 2T that satisfies p(x)=f(x) for all x.
If the quantum algorithm has error 1/, then there is a polynomial with p(x)2[0,1/3] for f(x)=0 and p(x)2[2/3,1] for f(x)=1
Now we have to consider the degree of polynomials representing Boolean functions
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Exact quantum algorithms
For every total Boolean function f there is a unique multilinear polynomial that represents f exactly
deg(f) denotes the degree of this polynomial QE(f)¸ deg(f)/2 Example: XOR, the polynomial is
Degree is n, and QE(XOR) =n/2
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Multilinear polynomials
Claim: For every Boolean function there is a unique multilinear polynomial, which represents f exactly, i.e. f(x)= p(x) for all Boolean x. Proof: Assume f(x)=p(x)=q(x) for alle Boolean x, yet pq Then p-q is a multilinear polynomial for g(x)=0, and p-q is
not the zero polynomial Take a minimum degree monomial m in p-q with nonzero
coefficient a0. Let z be the string string, that contains all xi in m as ones,
and contains no other ones m(z)=1, and p(z)=a, contradiction
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Another example
Polynomial for OR:
Also has degree n
Hence QE(OR)¸ n/2
But actually QE(OR)=n
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QE(OR)
We consider the amplitudes of the final state of an optimal algorithm for OR (no error), as polynomials of degree T
Basis states |0yi are rejecting. B is the set of those For i in B we have pi(x)=0 when x is not 0n
There is a j in B with pj(0n) 0 Consider the real part q(x) on
1-pj(x)/pj(0n) Then: deg (q) · T = QE(OR) and q(0n)=0 and q(x)=1 for other x,
hence deg(q)¸ deg(OR) = n
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Some facts about polynomials
If a Boolean f depends on n variables, we have deg(f)¸ log n - loglog n
All symmetric, nonconstant f have degree n-o(n) Hence QE(f)¸ (log n)/2 –o(log n) And QE(f)¸ n/2-o(n) for symmetric nonconstant f
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Approximating polynomials
Given a quantum algorithm with T queries that we get a multilinear polynomial of degree 2T with p(x)2[0,1/3] for f(x)=0 and p(x)2[2/3,1] for f(x)=1
adeg(f) denotes the minimal degree of such a polynomial
Then: Q(f)¸ adeg(f)/2 Example OR on 2 bits: x1/3 + x2/3 + 1/3,
adeg(OR2)=1
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Symmetrization
Let f denote a symmetric function, i.e, f is constant on all x with |x|=k, i.e., we have function values f0,…,fn
Symmetrization turns a multilinear polynomial for f into a univariate polynomial of the same degree
p(k)2[0,1/3] for fk=0 and p(k)2[2/3,1] for fk=1
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XOR
We get a polynomial such that p(k)2[0,1/3] for even k and p(k)2[2/3,1] for odd k
k2{0,…,n}
Clearly p(k)-1/2 has n roots! And degree n
Q(XOR)=n/2
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Symmetrization
Let be a permutation of [1,…,n], p polynomial in n var. Set psym(x)=
Lemma: If p is a multilinear polynom of degree d, then there is a univariate degree d polynomial with q(|x|) = psym(x) for all x Proof: Let Vj denote the sum of all products of j variables Then psym can be written as i=0...d bi Vi
Value of Vj on x is
The sum is
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The approximation degree of OR But what about OR?
We know already Q(OR)=n1/2
But adeg(OR) could be smaller Symmetrization:
p(0) 2[0,1/3] p(1),…,p(n)2[2/3,1]
What is the minimal degree of such a polynomial?
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A result from approximation theory Theorem: Let p be a polynomial with 0· p(x)· 1 for
all integers 0· x· nsuch that |p’(x)|¸ c for some real 0· x· n
Then deg(p)¸ (cn/(c+1))1/2
But: p(0)<1/3 and p(1)>2/3 Hence p’(x)¸ 1/3 for some x2 [0,1] adeg(OR)¸ (n/4)1/2
We recover the lower bound for search etc.
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Some more facts
For a total Boolean function f we have D(f)=O(adeg(f)6)
Hence also D(f)=O(Q(f)6) This is clearly only true for total functions The best speedup that is known (Grover) is only
quadratic Polynomial method is very useful for functions with
a lot of symmetry, example Element Distinctness
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The adversary method
This method actually characterizes Q(f) [in its strongest form]
Leads to a characterization as a semidefinite program
Original idea is to bound the progress achieved by one query in distinguishing pairs of inputs
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Certificate complexity
A certificate for x is a set of positions and values that fixes the function value for all x that are consistent with them Example: x1=1 fixes OR XOR has no certificate of length <n
C(f) is the max over all x of the min certificate for x C(XOR)=n C(OR)=n
0n needs a certificate of size n
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An observation
There are 1-certificates and 0-certificates xi=1 is 1-cert for OR x1=0,…, xn=0 is 0-cert for OR
For all f: 1-cert and 0-cert need to share at least one variable
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Certificate and adversaryC(f ) = min
px2f 0;1gnmax
x
X
jpx(j )
such thatX
j :xj 6=yj
px(j )py(j ) ¸ 1 if f (x) 6= f (y)
Adv(f ) = minpx2R n
maxx
X
jpx(j )2
such thatX
j :xj 6=yj
px(j )py(j ) ¸ 1 if f (x) 6= f (y)
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Adversary boundAdv(f ) = min
px2R nmax
xjjpxjj2
such thatX
j :xj 6=yj
px(j )py(j ) ¸ 1 if f (x) 6= f (y)
Example: OR on 0
n 10
n-1 … 0
n-11
px: (1…1) (1 0….) (01 0…) (0…01)
Rescale by 1/n
1/2 n
1/2
Adv(OR)· n
1/2
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Adversary bound
Need to show two things: Q(f)=(Adv(f)) How to prove lower bounds on Adv(f)
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How to prove lower bounds
Adversary bound as stated is a minimization problem, so we take the dual
Adv(f ) = max¡ 2R D£ D
jj¡ jj
such that
¡ (x;y) ¸ 0
¡ (x;y) = 0 if f (x) = f (y)
8j jj¡ ±X
x;y:xj 6=yj
jxihyj jj · 1
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Generalized adversary bound
Adv§ (f ) = max¡ 2R D£ nD
jj¡ jj
such that
¡ (x;y) = 0 if f (x) = f (y)
8j jj¡ ±X
x;y:xj 6=yj
jxihyjjj · 1
This bound is asymptotically equal to Q(f)