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New quantum lower bound method, with applications to direct product theorems
Andris Ambainis, U. WaterlooRobert Spalek, CWI,
AmsterdamRonald de Wolf, CWI,
Amsterdam
Query model
Input x1, …, xN accessed by queries.
Complexity = the number of queries.
0 1 0 0...x1 x2 xNx3
i0
ixi
Grover's search
Is there i such that xi=1? Queries: ask i, get xi. Classically, N queries required. Quantum: O(N) queries [Grover, 1996]. Speeds up any search problem.
0 1 0 0...
x1 x2 xNx3
Quantum counting [Boyer et al., 1998]
Is the fraction of i:xi=1 more than ½+ or less than ½- ?
Classical: queries.
Quantum: queries.
0 1 0 0...
x1 x2 xNx3
2
1
1
O
Element distinctness
Are there i, j such that ij but xi=xj? Classically: N queries. Quantum: O(N2/3).
3 1 17 5...
x1 x2 xNx3
Lower bounds Search requires N) queries
[Bennett et al., 1997]. Counting: 1/) [Nayak, Wu,
1999]. Element distinctness: (N2/3) [Shi,
2002].
Lower bound methods Adversary: analyze algorithm,
prove it is incorrect on some input. Polynomials: describe algorithm by
low degree polynomial.
Limits of adversary method Certificate for f on input (x1, x2, …,
xN):
set of variables xi which determine
f(x1, x2, …, xN). Search: is there i:xi=1?
0 1 0 0...
x1 x2 xNx3
Limits of adversary method Certificate for f on input (x1, x2, …,
xN):
set of variables xi which determine
f(x1, x2, …, xN). Search: is there i:xi=1?
0 0 0 0...
x1 x2 xNx3
Certificate complexity Cx(f): the size of the smallest
certificate for f on the input x.
)(max)( 0)(:0 fCfC xxfx )(max)( 1)(:1 fCfC xxfx
Search: C0=N, C1=1.
Limits of adversary method
Theorem [Spalek, Szegedy, 2004] Any quantum adversary lower
bound is at most
NfCfCO ))(),(min( 10
Example:element distinctness
Are there i, j:xi= xj? 1-certificate: {i, j}, xi= xj.
Adversary bound: Actual complexity: O(N2/3).
NONO 2
3 1 17 5...
x1 x2 xNx3
Example: triangle finding Graph G, specified by N2 variables xij:
xij=1, if there is edge between i and j. Does G contain a triangle? 1-certificate:{ij, jk, ik}, xij= xik= xjk=1. Adversary lower bound: at most
The best algorithm: O(N1.3) [MSS 03].
NONO 23
Quantum query model
Fixed starting state. U0, U1, …, UT – independent of x1, x2,
…, xN. Q – queries. Measuring final state gives the result.
U0 Q Qstart U1 UT…
Queries Basis states for algorithm’s
workspace: |i, z, i{1, 2, …, N}. Query transformation:
Example: |i, z|i, z, if xi=0; |i, z-|i, z, if xi=1;
zQiziQix
,
|
Adversary framework
Quantum algorithm A x1 x2 …
xN
NxxN xxxQxxxN
...... 21...21 1
Two registers: HA, HI.Query Q:
Example:Grover search Start state: |start|0,
End state
1...00...0...010...101
0 N
1...00...0...0120...1011
NN
Density matrices Measure HA, look at density matrix
of HI
N
N
N
end
100
01
0
001
NNN
NNN
NNN
start
111
111
111
Density matrices Sum of off-diagonal entries. N(N-1) entries. Sum for starting state: Sum for end state: 0. Query changes the sym by at most
2N. (N) queries needed.
11
)1( NN
NN
Limits of this approach (end)x, y measures the possibility of
distinguishing x from y. If every (end)x, y small, we can,
given x, y: f(x)f(y), distinguish x from y.
Limits of this approach It might be that:
Every x can be distinguished from every y;
There is no measurement that distinguishes all x from all y.
f(x)=0 f(y)=1
Adversary method fails
quantum algorithm
K-fold search
K items i:xi=1, find all of them. O(NK) queries: O(N/K) for each
item. This is optimal.
0 1 0 0...
x1 x2 xNx3
Direct product theorem Theorem [KSW 04] Solving K-fold
search with success probability c-K, c>1 requires NK queries.
Easy to prove for success probability c.
Difficult for probability c-K.Why is this useful????
Application:sorting Theorem [KSW04] A quantum
algorithm for sorting x1, x2, …, xN with S qubits of workspace must use
queries.
S
N 5.1
Proof
Divide algorithm into stages: first K items sorted, next K items sorted, …
Suffices to show each stage requires (NK) queries.
Each stage reduces to K-fold search.
Proof At the beginning of ith stage, we get
S qubits from the previous stage. Theorem K-fold search requires (NK) queries, even if we allow K/C qubits of advice.
Proof Theorem K-fold search requires (NK) queries, even if we allow K/C qubits of advice.
Proof Replace advice by completely mixed state.
Success probability p with advice => Success probability p2-K/C, no advice.
Direct product theorem Theorem Solving K-fold search with
success probability c-K, c>1 requires NK queries.
[KSW 04]: proof by polynomials method.
This talk: (new) adversary method.
|
Proof Adversary framework
Start state for input:
Kxi
N
i
xxx|}1:{|
21
Quantum algorithm A x1 x2 …
xN
Proof State of HI if we know
Subspace Tj spanned by all
1...
|}1:{|21...
1
1
jii
i
j
xx
KxiNii xxx
1...1
jii xx
jii ...1
Proof | - state of algorithm including
the input register |x1 … xN.
|j belongs to HA Sj. Probability of “know-j”:
,0
K
jj
2
jjp
Proof Start state: p0=1, p1=…=pK=0. Change in one query:
After NK queries, pK/2+1, …, pK are exponentially small.
Success probability exponentially small.
111' jjjj ppN
Kcpp
Threshold functions
F(x1, x2, …, xN)=1 if xi=1 for at least t values i{1, 2, …, N}.
F(x1, x2, …, xN)=0 if xi=1 for at most t-1 values i{1, 2, …, N}.
Query complexity: (Nt).
0 1 0 0...
x1 x2 xNx3
Threshold functions
F(x1, x2, …, xN)=1 if xi=1 for at least t values i{1, 2, …, N}.
F(x1, x2, …, xN)=0 if xi=1 for at most t-1 values i{1, 2, …, N}.
Query complexity: (Nt).
0 1 0 0...
x1 x2 xNx3
Threshold functions K instances of threshold function. (KNt) queries. Theorem Solving all K instances
with probability at most c-K requires KNt queries.
Proof
K input registers. Each input register initially
,|0, |1 - uniform over |x1 … xN with t-1 and t values i:xi=1.
10
Algorithm11
211 Nxxx K
NKK xxx 21
…
Proof For each instance, states “solved”,
“know-0”, “know-1”, … “know-(t-1)”. For K instances, vector of K states. Progress of a state:
“solved” – progress t/2. “know-t/2”, … “know-(t-1)” – progress
t/2. “know-j”, j<t/2 – progress j.
Proof If progress of final state less than tK/4,
the probability of getting all K correct answers is c-K.
Decompose current state
Potential function
j
j jprogressp ""
,j
jjqpP
tNq
11
Proof
Start state: P()=1. For pj, jtK/4 to be more than c-K,
One query increases P() by at most a factor of
j
jjqpP
KtKK CqcP 4/
tN
O1
1
Proof F(x1, x2, …, xN)=0, “know-j”:
F(x1, x2, …, xN)=1, “know-j”:
1...
1|}1:{|21
0
1
...1
jii
ijii
xx
txiNxxx
1...
|}1:{|21
1
1
...1
jii
ijii
xx
txiNxxx
Application: testing linear inequalities
aij known, xi, bj accessed by queries.
Which inequalities are true?
NNNNNN
NN
bxaxaxa
bxaxaxa
2211
11212111
Our result
Memory limited to S (qu)bits. Classically: (N2/S) queries. Quantum: (N3/2t1/2/S1/2) queries. Lower bound follows from threshold
function lower bound.
NNNNNN
NN
bxaxaxa
bxaxaxa
2211
11212111
Conclusion New quantum lower bound
method, by eigenspace analysis. Direct product theorems for K-fold
search and threshold functions. Consequences for time-space
tradeoffs.
More details A. Ambainis. A new quantum lower
bound method, with application to direct product theorem for search, quant-ph/0508200.
A. Ambainis, R. Spalek, R. de Wolf, Quantum direct product theorems for symmetric functions and time-space tradeoffs , quant-ph/0511200.