Hallgren’s Quantum Algorithm for Pell’s Equation · 2011-12-12 · Pradeep Sarvepalli...
Transcript of Hallgren’s Quantum Algorithm for Pell’s Equation · 2011-12-12 · Pradeep Sarvepalli...
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Hallgren’s Quantum Algorithm forPell’s Equation
Pradeep Sarvepalli
Department of Computer Science,
Texas A&M University
Quantum Algorithm for Pell’s Equation – p. 1/60
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Outline
Pell’s equation
Classical method for solving Pell’s equation
Reformulating the Pell’s equation in number theoreticterms
Overview of Hallgren’s quantum algorithm
Hallgren’s algorithmAlgebraic backgroundQuantum period finding algorithmClassical post processingPutting all the pieces together
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Outline - cont’d
ApplicationsClass group of a quadratic fieldPrincipal ideal problem
GeneralizationsUnit group of a finite extension of Q
Class group of a finite extension of Q
Principal ideal problem
References"Notes on Hallgren’s efficient quantum algorithm forsolving Pell’s equation" by Richard Jozsa"Polynomial time quantum algorithms for Pell’sequation and principal ideal problem" by SeanHallgren
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Pell’s Equation
Goal is to find finding integral and positive solutions tothe following equation
x2 − dy2 = 1
x2 − 2y2 = 1, Solutions (3, 2); (17, 12); . . .
Has infinite number of solutions
Harder than factoring
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Classical Method for Pell’s equation
Based on continued fractions
Approximations of the continued fraction of√d give the
solution
Example
x2 − 2y2 = 1,√
2 = 1 +1
2 + 12+ 1
2+···
√2 ≈ 1 +
1
2=
3
2=x
y
Observe that 32 − 2 · 22 = 9 − 8 = 1
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Continued Fraction Method
Why does this work?
Is every approximation a solution?
What is the complexity of the algorithm?
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Continued Fraction Method - cont’d
Example - cont’d
√2 ≈ 1 +
1
2 + 12
= 1 +2
5=
7
5=x
y
√2 ≈ 1 +
1
2 + 12+ 1
2
= 1 +1
2 + 25
= 1 +5
12=
17
12=x
y
72 − 2 · 52 = 49 − 50 = −1 6= 1
172 − 2 · 122 = 289 − 288 = 1
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Continued Fraction Method - cont’d
Not every approximation gives a solution to the Pell’sequation
We might have to take many terms in the continuedfraction before we get the solution
Typically if we use the input size as log d, size of thesolution will be O(
√d) ≈ O(elog d)
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A Close Look at the Solutions
Every solution can be uniquely identified with x+ y√d,
x, y > 0
Smallest solution with x+ y√d is called the
Fundamental solution
x1 + y1
√d
Every other solution can be obtained as power of thefundamental solution
x+ y√d = (x1 + y1
√d)n
It suffices to compute the fundamental solution
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A Closer Look at the Solutions
d x
10 1913 64929 980153 6624961 1766319049
109 158070671986249181 2469645423824185801277 159150073798980475849397 838721786045180184649409 25052977273092427986049421 3879474045914926879468217167061449
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Regulator
O(x1 + y1
√d) = O(e
√d)
Just to write down the solution will take exponentialamount of write operations
We will rather compute a representation of the solution
Regulator Rd = ln(x1 + y1
√d), and x1 + y1
√d is the
smallest in magnitude of all solutions
Rd being irrational is computed to n digit accuracy
Even for this reduced problem the best classicalalgorithm has a running time O(e
√log d poly(n))
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Overview of Hallgren’s Algorithm
Reformulate the Pell’s equation as a period findingproblem
Form a function h(x) with period Rd
Modify the quantum period finding algorithm to findirrational period
The quantum part of the algorithmComputes only the integral part of Rd
Perform classical post processing given the integralpart of Rd
Compute the fractional part of Rd making use of theintegral part of Rd provided by the previous step
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Some Algebraic Number Theory
Let Q(√d) = {a+ b
√d | a, b ∈ Q}
The solutions of Pell’s equation are elements in Q(√d)
O is called the ring of algebraic integers
O = {a ∈ Q(√d) | f(a) = 0, f(x) ∈ Z[x]}
Z[x] consists of polynomials with integral coefficients
Units of O are elements in O that they havemultiplicative inverses
O× = { Units of O} = {u ∈ O | u−1 exists }
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Algebraic Number Theory - cont’d
x+ y√d is a solution of x2 − dy2 = 1 if and only if
x+ y√d is a unit of O
ǫo smallest unit in O×, with ǫo > 1
O× = {±ǫko | k ∈ Z}Rd = ln ǫo
All the solutions of x2 − dy2 can be obtained from thefundamental unit ǫo
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Algebraic Number Theory - cont’d
Product of sets A,B ⊆ Q(√d), then
A ·B = {n∑
i=1
aibi | n > 0, ai ∈ A, bi ∈ B}
Ideal I ⊆ Q(√d) such that I · O = I
Integral Ideal I ⊆ OFractional Ideal I ⊆ Q(
√d)
Principal Ideal I = γOIf ǫ ∈ O, then ǫO = OEquality of ideals αO = βO if and only if α = βǫ, whereǫ is a unit in O
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Reformulating Pell’s equation
Let P = {γO | γ ∈ Q(√d)}
Consider g : R → P
g(x) = exO = Ix
g(x) is periodic with Rd
g(x+ kRd) = ex+kRdO,= exǫkoO = exO= g(x)
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Using a periodic function to findRd
Naive algorithm to compute Rd
Find g(x) = Ix
Find y > x such that g(y) = Ixy − x = kRd
Another naive algorithmAssume that the ideals could be ordered somehowGiven Ix find g−1(Ix)
Find an ideal Iy next to Ix such that Iy = Ix
g−1(Iy) − g−1(Ix) = kRd
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Problems with the Naive Algorithms
y > eRd is exponentially large in log d
Ix is an infinite set, comparing two ideals posesproblem
There are infinitely many ideals between x, x+ δ forwhich Ix = Ix+δ, Q(
√d) and P are both dense
We need to somehow order the ideals
We need to move from one ideal to another
Finite precision arithmetic
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More Algebraic Number Theory
O is a Z-module, so it behaves like a vector space
O = m+ nD +
√D
2, D =
{
d, d ≡ 1 mod 4
4d, d ≡ 2, 3 mod 4,
O = Z +D +
√D
2Z
For any principal ideal I = γO
I = γZ + γD +
√D
2Z
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Comparing Principal Ideals
The basis is not unique, we can rewrite I as
I = k
(
aZ +b+
√D
2Z
)
−a < b ≤ a, a >√D√
D − a < b ≤√D, a <
√D
Presentation of an ideal is the triplet (a, b, k) which iscomputable in polynomial time given γ.
Addresses the problem of comparing infinite sets.Equality of ideals is equivalent to having the samepresentation
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Discretizing Ideals
Reduced principal ideals have the form
I = Z +b+
√D
2Z
Reduced principal ideals are finite in number
Addresses the problem of density of sets Q(√d) and P
Cycle of Reduced Principal Ideals
J = {O = J0, J1, . . . , Jm−1}
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Moving Among Ideals
I = Z + γZ, then define
ρ(I) =1
γI
Gives another principal ideal
If I ∈ J then ρ(I) ∈ J
If repeated then finally it gives a reduced principal ideal
If I was reduced principal ideal ρ(I)is another reducedprincipal ideal
Since there are a finite number of reduced principalideals it cycles
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Moving Among Ideals - cont’d
σ(I) :√d 7→ −
√d
Inverse operation of moving back is given by ρ−1 = σρσ
This addresses the problem of moving between theideals back and forth
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Ordering Ideals
Let Iy = γIx. Then the distance between Iy and Ix is
δ(Ix, Iy) = ln γ
Distance from O: δ(exO) = δ(O, exO) = ln ex = x
Distance allows us to order the ideals along thenumber line in the same form as the input x
Addresses the problems of invertibility and ordering
If Ix = Iy, then δ(Ix, Iy) = y − x = kRd
δ(I, ρ(I)) can be computed in polynomial time
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Spacing of Ideals
δ(Ji, Ji+1) ≥ 332D
δ(Ji, Ji+2) ≥ ln 2
δ(Ji, Ji+1) ≤ 12lnD
If we succesively apply ρ to a non reduced ideal I, untilwe just get a reduced principal ideal Ired, then
|δ(I, Ired)| ≤ lnD
and Ired ∈ {Jk−1, Jk, Jk+1}
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Jumping Across Ideals
We note that the ideals are exponential in number somoving across efficiently requires more than use ofreduction operator ρ
Use the product of ideals to move faster than ρ
I1 · I2 is much farther from O than either of I1, I2
δ(I1 · I2) = δ(I1) + δ(I2)
Product of ideals is not necessarily reduced so wereduce it to bring it back to the principal cycle ofreduced ideals
We denote this operation by ∗
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Review
We need positive, intgeral solutions for x2 − dy2 = 1
Each solution can be encoded as x+ y√d an element
in Q(√d)
All solutions can be obtained from the smallest solutionx1 + y1
√d
We introduced the Regulator Rd = ln(x1 + y1
√d)
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Review - cont’d
Every solution is an element of Q(√d)
More specifically every solution is also an element ofthe ring of algebraic integers in Q(
√d)
A solution of Pell’s equation is precisely the setelements in O which have multiplicative inverses
Our goal is to compute the generator of this subgroupwhich is precisely the regulator Rd
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Review - cont’d
Solutions Pell’s Eqn
Quadratic Number Field
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Review - cont’d
Solutions Pell’s Eqn
Quadratic Number Field
Algebraic Integes, O
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Review - cont’d
Solutions Pell’s Eqn
Quadratic Number Field
Unit group of O
Algebraic Integes, O
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Review - cont’d
Solutions Pell’s Eqn
Quadratic Number Field
Unit group of O
Algebraic Integes, O
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Review - cont’d
We then defined the ideals as special sets in Q(√d)
which are loosely speaking closed under addition andmultiplication
A special type of ideals are the principal ideals whichtake the form I = γOWe defined a periodic function that is periodic with Rd
from R to the set of principal reduced ideals
They can all be ordered with respect to their distancefrom OWe can move among the ideals using ρ and ρ−1
We have a means of moving from one ideal to anotherexponentially large steps
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Review - cont’d
Reduced Principal IdealsIdeals closed under +,x
Principal Ideals
Algebraic Integes, O
Quadratic Number Field
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Review - cont’d
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Quadratic Number Field
Algebraic Integes, O
Principal Ideals
Ideals closed under +,x Reduced Principal Ideals
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Hallgren’s Periodic Function
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(O,x0) (O,,x2)
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h(x) = (g(x), x− δ(g(x))) = (Ix, x− δ(Ix))
Ix is the nearest reduced principal ideal to the left of xi.e, δ(Ix) < x
h(x) is periodic with Rd
h(x) is one to one
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Hallgren’s Periodic Function
δ(Ι)
I_x
Principal Ideals
X
J0J2J1J0
NR321
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h(x) = (g(x), x− δ(g(x))) = (Ix, x− δ(Ix))
Quantum Algorithm for Pell’s Equation – p. 37/60
![Page 38: Hallgren’s Quantum Algorithm for Pell’s Equation · 2011-12-12 · Pradeep Sarvepalli pradeep@cs.tamu.edu Department of Computer Science, Texas A&M University Quantum Algorithm](https://reader033.fdocuments.us/reader033/viewer/2022050522/5fa5b13669bf3d54b85fa42a/html5/thumbnails/38.jpg)
Discretizingh(x)
For practical implementation we would like to discretizeh(x)
Assuming that x is discretized with a step of 1/N thediscretized function hN(k) = ⌊h(k/N)⌋N
hN(k) is weakly periodic with P = NRd
hN(k + ⌊lNRd⌋) = hN(k) or hN(k + ⌈lNRd⌉) = hN(k)
Quantum Algorithm for Pell’s Equation – p. 38/60
![Page 39: Hallgren’s Quantum Algorithm for Pell’s Equation · 2011-12-12 · Pradeep Sarvepalli pradeep@cs.tamu.edu Department of Computer Science, Texas A&M University Quantum Algorithm](https://reader033.fdocuments.us/reader033/viewer/2022050522/5fa5b13669bf3d54b85fa42a/html5/thumbnails/39.jpg)
Hallgren’s Periodic Function
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Principal Ideals
X
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After discretization h(x) is weakly periodic not periodicQuantum Algorithm for Pell’s Equation – p. 39/60
![Page 40: Hallgren’s Quantum Algorithm for Pell’s Equation · 2011-12-12 · Pradeep Sarvepalli pradeep@cs.tamu.edu Department of Computer Science, Texas A&M University Quantum Algorithm](https://reader033.fdocuments.us/reader033/viewer/2022050522/5fa5b13669bf3d54b85fa42a/html5/thumbnails/40.jpg)
Quantum Period Finding Algorithms
A state in superposition
Transformation to a state with a suitable function
Partial measurement
Fourier transform to get rid of offset
Measurement
Classical post processing
Quantum Algorithm for Pell’s Equation – p. 40/60
![Page 41: Hallgren’s Quantum Algorithm for Pell’s Equation · 2011-12-12 · Pradeep Sarvepalli pradeep@cs.tamu.edu Department of Computer Science, Texas A&M University Quantum Algorithm](https://reader033.fdocuments.us/reader033/viewer/2022050522/5fa5b13669bf3d54b85fa42a/html5/thumbnails/41.jpg)
Hallgren’s Period Finding Algorithm
Form is an uniform superpostion of q states whereq = pNRd + r
|ψ〉 =1√q
q−1∑
j=0
|j〉|0〉
Compute hN(|ψ〉)
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0 q−11 2 1/NNR
J1 J2 OO=J0 =J0 J1 J2 J0
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Quantum Algorithm for Pell’s Equation – p. 41/60
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Superposition
hN |ψ〉 =1√q
q−1∑
j=0
|j〉|hN(j)〉
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J1 J2 OO=J0 =J0 J1 J2 J0
(O,x0) (O,,x2)
(O,x1) (J1,x3)
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Quantum Algorithm for Pell’s Equation – p. 42/60
![Page 43: Hallgren’s Quantum Algorithm for Pell’s Equation · 2011-12-12 · Pradeep Sarvepalli pradeep@cs.tamu.edu Department of Computer Science, Texas A&M University Quantum Algorithm](https://reader033.fdocuments.us/reader033/viewer/2022050522/5fa5b13669bf3d54b85fa42a/html5/thumbnails/43.jpg)
Partial Measurement
h(0) h(1) h(2) h(3)
h(0) h(1) h(2) h(3)
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hN |ψ〉 =1√q
NRd−1∑
j=0
(
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l=0
|j + [lNRd]〉)
|hN(j)〉
Measure the second register, say we measure hN(k)h(1) h(2) h(3)
h(0) h(1) h(2) h(3)
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Quantum Algorithm for Pell’s Equation – p. 43/60
![Page 44: Hallgren’s Quantum Algorithm for Pell’s Equation · 2011-12-12 · Pradeep Sarvepalli pradeep@cs.tamu.edu Department of Computer Science, Texas A&M University Quantum Algorithm](https://reader033.fdocuments.us/reader033/viewer/2022050522/5fa5b13669bf3d54b85fa42a/html5/thumbnails/44.jpg)
Partial Measurement - cont’d
(2+3NR)
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(2+NR)
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Second Register
First Register
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After Measurement
On measurement the state will collapse to
|ψ〉 =1√p
p−1∑
n=0
|k + [nNRd]〉|hN(k)〉
Quantum Algorithm for Pell’s Equation – p. 44/60
![Page 45: Hallgren’s Quantum Algorithm for Pell’s Equation · 2011-12-12 · Pradeep Sarvepalli pradeep@cs.tamu.edu Department of Computer Science, Texas A&M University Quantum Algorithm](https://reader033.fdocuments.us/reader033/viewer/2022050522/5fa5b13669bf3d54b85fa42a/html5/thumbnails/45.jpg)
QFT to Remove Offset
Take the Quantum Fourier transform
F|ψ〉 =1√pq
p−1∑
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e2πi(k+[nNRd])|j〉,
=
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j=0
aj|j〉
Assumes q=pNR
Pro
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lity
1/p=NR/q
5q/NR4q/NR3q/NR2q/NRq/NR
Register after Quantum Fourier Transform
Quantum Algorithm for Pell’s Equation – p. 45/60
![Page 46: Hallgren’s Quantum Algorithm for Pell’s Equation · 2011-12-12 · Pradeep Sarvepalli pradeep@cs.tamu.edu Department of Computer Science, Texas A&M University Quantum Algorithm](https://reader033.fdocuments.us/reader033/viewer/2022050522/5fa5b13669bf3d54b85fa42a/html5/thumbnails/46.jpg)
QFT - cont’d
Register after Quantum Fourier Transform
q/NR 2q/NR 3q/NR 4q/NR 5q/NR
Prob(lq/NR) < 1/p
Pro
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lity
Assumes q=NR+r
q/NR 2q/NR 3q/NR 4q/NR 5q/NR
1/p=NR/q
Pro
babi
lity
Assumes q=NR
Quantum Algorithm for Pell’s Equation – p. 46/60
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Identifying Periodicity
Register after Quantum Fourier Transform
Assumes q=NR+r
Pro
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lity
Prob(lq/NR) < 1/p
5q/NR4q/NR3q/NR2q/NRq/NR
We are interested inj = lq/NR
j is small, more precisely, j < q
log NRd
Prob(j) must be large
Quantum Algorithm for Pell’s Equation – p. 47/60
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Identifying Periodicity - cont’d
For sufficiently large q ≥ 3(NRd)2, many j such that
j is a multiple of q/NRd
j < q
log NRd
Probability of such j is highly likely,
Prob(j) >α
logNRd
,
α a constant
Quantum Algorithm for Pell’s Equation – p. 48/60
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Extracting Periodicity
But how do we extract the periodicity from the state?
Measure and repeat to get another measurement suchthat
c = [kq/NRd]
d = [lq/NRd]
We do not know k, l, Rd
Compute the convergents of cd
| cd− a
b| < 1
2b2
Quantum Algorithm for Pell’s Equation – p. 49/60
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Extracting Periodicity - cont’d
Compute the convergents of c/d then
k
l=cndn
and k = cn,
for some n
c = [ kq
NRd
] = kq
NRd
Estimate the period as
NRd = [kq
c]
Check if NRd = cnq/c satisfies |lRd −NRd| < 1
Quantum Algorithm for Pell’s Equation – p. 50/60
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Extracting Periodicity - cont’d
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If |NRd − jRd| < 1, then h(NRd) is an ideal among{ρ−4(O), ρ−3(O), . . . ,O, . . . , ρ3(O), ρ4(O)}Because δ(I, ρ2(I)) > ln 2 > 0.693
Quantum Algorithm for Pell’s Equation – p. 51/60
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Integral Part ofRd
Integral part of Rd
⌊Rd⌋ =
⌊
NRd
N
⌋
We know Rd to a precision 1/N
With probability ≥ 1/ poly(logNRd) this algorithm willreturn NRd such that |NRd −NRd| < 1
Quantum Algorithm for Pell’s Equation – p. 52/60
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Computing the Fractional part ofRd
Given ⌊Rd⌋Compute h(⌊Rd⌋) = (Ix, x− δ(Ix))
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>0.693
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ρ (Ο)−2−4ρ (Ο)
−3ρ (Ο) ρ (Ο)−1
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We know that δ(I, ρ2(I)) > ln 2 = 0.693
Quantum Algorithm for Pell’s Equation – p. 53/60
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Computing the Fractional part ofRd - cont’
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NRNR−1NR−2
Ο
> 0.693
>0.693
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ρ (Ο)−2−4ρ (Ο)
−3ρ (Ο) ρ (Ο)−1
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Therefore δ(I, ρ4(I)) > 2 ln 2 > ⌈Rd⌉This implies that O must be one of the ideals{ρ−3(O), ρ−2(O), ρ−1(O),O}δ(I,O)) can be computed in polynomial and this givesthe fractional part of Rd
Quantum Algorithm for Pell’s Equation – p. 54/60
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Summary of the Algorithm
Start with a superpostion of inputs
Compute Hallgren’s periodic fucntion for all theseinputs
Perform partial measurement
Perform QFT to get rid of offset
Perform a measurement to get c
Repeat to get another value d
Extract the Integral part of Rd
Compute the fractional part of Rd
Quantum Algorithm for Pell’s Equation – p. 55/60
![Page 56: Hallgren’s Quantum Algorithm for Pell’s Equation · 2011-12-12 · Pradeep Sarvepalli pradeep@cs.tamu.edu Department of Computer Science, Texas A&M University Quantum Algorithm](https://reader033.fdocuments.us/reader033/viewer/2022050522/5fa5b13669bf3d54b85fa42a/html5/thumbnails/56.jpg)
Applications
Principal Ideal ProblemGiven an ideal determine if it is a principal ideal
Class Group structureDetermine the structure of the group Iinv/P
Quantum Algorithm for Pell’s Equation – p. 56/60
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Principal Ideal Problem
Recall an ideal I ⊆ Q(√d) such that I · O = I
Principal ideal I = γOAll ideals of the form I = αZ + βZ
Given an ideal, decide if there exists a γ such thatI = γO
Quantum Algorithm for Pell’s Equation – p. 57/60
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Computing the Class Group
Invertible idealsAn ideal I is called invertible if there exists anotherideal J such that I · J = OLet I = {I | I−1 exists }P = {I | I = γO}Let C = I/PC is a finite abelian group and called the classgroupClass group problem is to determine the structure ofC
Quantum Algorithm for Pell’s Equation – p. 58/60
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Generalizations
A general problem is to compute the unit group of aO ⊆ Q(θ) where [Q(θ) : Q] = n
For Pell’s equation n = 2
Similarly the class group and the principal idealproblem also can be generalized
Two algorithms for the same have appeared recently byHallgren and Vollmer, Schmidt independetly this year
Quantum Algorithm for Pell’s Equation – p. 59/60
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Questions ?
Quantum Algorithm for Pell’s Equation – p. 60/60
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Questions ?
Thank You
Quantum Algorithm for Pell’s Equation – p. 61/60