Lecture 9: Shor s Algorithm

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1 Quantum Computation (CMU 8-859BB, Fall 05) Lecture 9: Shor Algorithm October 7, 05 Lecturer: Ryan O Donnell Scribe: Sidhanth Mohanty Overview Let u recall the period finding problem that wa et up a a function f : Z color, with the promie that f wa periodic. That i, there exit ome for which f(x + ) = f(x) (note that addition i done in Z ) for all x Z and that color in a block of ize were pairwie ditinct. Thi etup implie that, o that greatly narrow down what could be. Thi problem i not hard to do claically, but can be done better with a quantum computer. Slight variant of thi problem can be olved with a quantum computer too, and we hall explore uch a variant in thi lecture. Here i a ketch of the period finding algorithm that wa covered during lat lecture (ee the period finding lecture for a deeper treatment). We begin by preparing our favorite quantum tate We then tenor thi tate with 0 n. x We pa the tate (after tenoring) through an oracle for f and obtain the tate x=0 x f(x) x=0 We then meaure the qubit repreenting f(x) and obtain a random color c. Thi caue the overall tate to collape a uperpoition of tate where x i in the preimage of c. x 0 + k c k=0 The coefficient can be thought of a f c (x) where f c(x) = when f(x) = c and 0 otherwie.

2 We then apply the Quantum Fourier Tranform on thi tate to obtain a quantum tate where the coefficient are ˆf c (γ) where γ i a multiple of. From the previou lecture, we know that ˆf c ha a period of and hence γ for which ˆf c (γ) i nonzero i a multiple of. { } Meauring k give u a random γ in 0,,,, (S ). Take a contant number of ample and take the GCD of all thee ample. With high probability, you get, from which we can retrieve. Review of complexity of algorithm involving number In general, an efficient algorithm dealing with number mut run in time polynomial in n where n i the number of bit ued to repreent the number (number are of order n ) To refreh, let go over thing we can do in polynomial time with integer. Say P, Q and R are n bit integer. P Q can be computed in polynomial time. P Q and P mod Q can be computed in polynomial time. P Q i maive, and writing it out itelf would caue the time to go exponential. But P Q mod R can be done polynomially by computing p, p, p 4, p 8,..., p n for n Q. The GCD of P and Q can be done polynomially with Euclid algorithm. ow for omething more intereting: checking if P i prime. It can be done in Õ(n ) uing a randomized algorithm (Miller-Rabin) and in Õ(n6 ) uing a determinitic algorithm (AKS). ow, why not try to factor P? And uddenly we are tuck if we try to approach the problem claically. The bet known determinitic algorithm run in Õ(n 3 ) 3 Shor Algorithm There are three tep to undertanding Shor algorithm [Sho97].

3 . Factoring Order-finding: Factoring reduce to order-finding, which mean that if we have an algorithm to olve order-finding efficiently, we can efficiently olve the factoring problem a well by a polynomial time reduction from factoring to order-finding. ote that thi reduction can be made claically.. Order-finding Period-finding: Vaguely, order-finding i approximately the ame problem a period finding for a quantum computer. Thi will be expanded in more detail thi lecture. 3. Identifying imple fraction: Thi part i neceary in the order-finding algorithm that i crucial for Shor algorithm and can be done claically a well. The econd tep i the key tep in Shor algorithm. 3. What i order finding? We are given A, M (n-bit number) along with a promie that A and M are coprime. The objective i to find the leat ( M) uch that A mod M. i called the order of A. ote that divide ϕ(m), where ϕ i the Euler Totient function that give u the number of element le than M that are coprime with M. A another remark, ϕ(m) i the order of the multiplicative group Z m and divide ϕ(m). 3. Proof that Factoring Order-finding In thi ection, we hall aume that we have an efficient order-finding algorithm. Say M i a number that we want to factor. The key to olving the factoring problem uing order-finding lie in finding a nontrivial quare root of mod M, that i, a number r with r mod M and r ± mod M. Then we know that (r + )(r ) 0 mod M and both r + and r are nonzero mod M and are factor of ome multiple of M. (A nontrivial quare root may not alway exit, for intance, when M i a power of an odd prime, but we ll ee how to handle that cae) Computing the GCD of M and r would give u a nontrivial factor of M, called c. We can divide out c from M, check if c or M are prime and for each of c and M, if they are c c not prime, we recurively factor them, and if they are prime, we tore them a prime factor and wait until the ret of the term are factored. We then return the et of all prime factor. (Recall that we can efficiently tet primality.) ote that the number of recurive call made i logarithmic in M becaue there are at mot log M prime factor of M and each recurive call increae how many number we have not plit by. Hence, after log M recurive call, there are about log M number that we have not plit. Splitting further would force the number of prime factor to exceed log M, which i not poible. 3

4 ow, one might ak how one would go about finding a nontrivial quare root of mod M. We take a random A Z M, and find it order. Perhap, we get lucky and have be even, o we could et r A mod M (then r A mod M mod M). Maybe we could puh our luck a bit more and hope r mod M. But turn out, we can actually make thee two lucky thing happen, thank to a number theory lemma! Lemma 3.. Suppoe M ha ditinct odd prime factor. Then if we pick A Z M uniformly at random, the probability that the order of A i even and that A = i at leat. Proof. See Lemma 9. and Lemma 9.3 of Vazirani coure note [Vaz04] One can pick a uniformly random A Z M by randomly picking element A from Z M and computing GCD(M, A) until it we find A for which the GCD i. And with at leat chance, our lucky condition are atified. Repeatedly picking A boot thi probability further. If we cannot find uch a number A after picking randomly many time, then it mean that M i an odd prime power, in which cae, we factorize it by binary earching the k-th root of M where k i guaranteed to be an integer in [, log M]. 3.3 Quantum algorithm for Order-Finding By etablihing that Factoring Order-Finding, we howed that if we could omehow find the order of A Z M, we could then claically factorize M. ow, we hall ee how one actually find the order. Given n bit integer A and M, let = poly(n) >> M where poly(n) i omething ridiculouly large like n 0. Such a number can till be written in poly(n) bit. Define f : {0,,..., } Z M to be f(x) = A x mod M. otice that A 0 = A =, all power in between are ditinct and then it repeat. So it i almot -periodic, but not quite, becaue we do not know if divide. But we houldn t have much trouble modifying period-finding lightly to olve thi variant of the original problem. Jut like in period-finding, we tart with our favorite tate x {0,} n x And then we tenor thi tate with 0 n and pa the overall quantum tate through an oracle for f, O f and end up with the tate x {0,} n x f(x) 4

5 And we meaure the econd regiter, collaping the tate to a uperpoition of tate that involve x where f(x) i a random element c in the ubgroup generated by A. Thi i where order-finding tart getting different from period finding. ote that doe not divide, o we cannot be ure of the exact number of time each color c appear. Intead, we can ay that appear only D time where D i either or. We will now ee how taking a maive come in handy. We apply a Quantum Fourier Tranform on our tate to obtain the tate D γ=0 D ω γ j γ c j=0 In the above tate, ω = e πi. And ampling γ from thi tate give u ome fixed γ 0 with probability Pr[ampling γ 0 ] = D D D j=0 ω γ 0 j The reaon we eparate a D and move the denominator into the quare i that it D D i nice to think of the um being quared a an average. We want γ we elect by ampling to be of the form k (thi i notation for nearet integer) for k uniformly ditributed in {0,,..., }. The idea i that if γ i of the given form, then γ i a real number that i extremely cloe to the imple fraction k where it i known that both k and are n-bit integer. More formally, given γ within ± of k, we claim we can find k. ow, we call upon another lemma to how how uch a γ can be ampled. Lemma 3.. For each γ of the form k ampling γ. with 0 k <, there i 0.4 probability of Proof. A proof can be found in lemma 9.4 of Vazirani coure note [Vaz04]. We will now how how one can get k when they have. Continued fraction are a way γ to approximately decribe real number in term of integer. A real number r would look omething like a 0 + a + a a M We will ue a method involving continued fraction and go over a rough ketch of thi method in lecture to ue continued fraction to obtain k from γ. Firt, let u illutrate with an example how one can obtain the expanion of ome number with continued fraction. 5

6 Conider the fraction 4. We firt plit the fraction into it integer part and fractional 3 part and expre it a the um of both. 4 3 = We then expre the fraction a an inverion of it reciprocal = ow, plit the denominator of the econd term into it integer part and fractional part and repeat ow we will ee how one could ue continued fraction to compute k. The idea i to ue Euclid algorithm on and γ and top when we get ome value cloe to 0 rather than when we get exactly 0, and keep track of quotient of the form a whenever we compute a value b of the form a mod b. We will illutrate the method with another example. If k i, then γ. 5 5 ow, we take mod γ and get approximately 3 with a the quotient. A the 5 next tep, we take mod 3 and get roughly with 3 a the quotient. Then, we get approximately 3 a the remainder from mod and get a the quotient. Finally, in the lat tep, we get the remainder to be approximately 0 and the quotient to be when we take recurively apply Euclidean algorithm on term that are approximately and 5 Ṫhe 5 quotient at any given tep in the Euclidean algorithm could be thought of a the integral part, and finding the bigger element modulo the maller element help u obtain the fractional part. Uing the quotient we obtained, we get the continued fraction approximation for γ a = 5 To wrap up, we will how how we can eliminate poibilitie of failure. If k and have a common factor, then the fraction k returned by computing the continued fraction γ approximation of would be one of implet form, but with k k and. We will treat thi poibility by howing that we can alway find k with k and coprime by running the algorithm enough time. We claim that with probability, k and are coprime. poly(n) 6

7 Proof. ote that ha at mot log prime factor. By the prime number theorem, there are at leat prime number le than. The order of the number of prime number le log than that are coprime with i about the ame, becaue log i aymptotically much le than o excluding thoe prime without loing many element. Thu, when k i picked log uniformly at random between and, there i a chance that it i a prime that i coprime log to. i at mot n bit long, and hence the probability that k i a coprime to i at leat. poly(n) Repeat the algorithm until you get k k and in lowet term with GCD(k, k ) =. Once we accomplih thi, we can find, which i the order of element A. And by uing the reduction of factoring to order finding that we proved in the previou ection, we can efficiently olve the factoring problem! Reference [Sho97] Peter Shor. Polynomial-time algorithm for prime factorization and dicrete logarithm on a quantum computer. SIAM journal on computing, 6(5): , 997. [Vaz04] Umeh Vazirani. Shor factoring algorithm. CS 94-, Fall

Lecture 8: Period Finding: Simon s Problem over Z N

Quantum Computation (CMU 8-859BB, Fall 205) Lecture 8: Period Finding: Simon Problem over Z October 5, 205 Lecturer: John Wright Scribe: icola Rech Problem A mentioned previouly, period finding i a rephraing