Quantum computing. Shor s factoring algorithm. Dimitri Petritis. UFR de mathématiques Université de Rennes CNRS (UMR 6625) Rennes, 30 novembre 2018

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1 Shor s factoring algorithm Dimitri Petritis UFR de mathématiques Université de Rennes CNRS (UMR 6625) Rennes, 30 novembre 2018

2 Classical Turing machines Theoretical model of classical computer = classical Turing machine (deterministic, non-deterministic, probabilistic). Complexity classes (P,NP,BPP). Practical computation performed on logical gates acting on binary representations of numbers. b N, Z b = {0,..., b 1}. n 1 Z n b (x 0,, x n 1) x = x k b k = x n 1 x 0 b N. If b = 2, b =. Any function expessible by Boolean functions. Logical gates = elementary Boolean functions. Basis for computation = complete set of gates. Eg. {NOT, OR, AND} complete but redundante; {NOT, OR}, {NOT, AND}, {AND, XOR} complete and minimale. k=0

3 Example: addition with carry Example x = x 1 x 0, y = y 1 y 0. z = x + y = z 2 z 1 z 0 within basis B = {XOR, AND} = {, }. x 1 x 0 y 1 y 0 z 2 z 1 z z 0 = x 0 y 0 z 1 = (x 0 y 0) (x 1 y 1) z 2 = (x 1 y 1) [(x 1 y 1) (x 0 y 0)]

4 Quantum operations In quantum mechanics: 2 types of transformations: isolated evolution (unitary hence invertible), measurement (projective hence non-invertible). Non-degenerate mesurement projects onto 1-dimensional Hilbert space. Quantum phenomena manifest themselves starting at dimension 2. Once a measurement performed, quantum system becomes classical. Conclusion: quantum computer must compute all time with invertible gates (unitaries); at the end of the computation perform measurement (that reveals the result). Consequence: no knowledge of intermediate resutls; special care for halting condition.

5 A reversible gate Fredkin 1982 Input Output a b c a b c { c a = a, b = b if c = 0 = c et a = b, b = a otherwise. Fredkin s gate equivalent to (0, x, y) (x y, x y, y) (1, x, y) (x y, x y, y). hence universal since it can simulate AND and NOT.

6 Reversible gates Implement arbitrary Boolean functions Arbitrary Boolean function F : B m B n can be extended into F : B m+n B m+n, defined by F (x, y) = (x, y F (x)), where is bitwise addition modulo 2. F is a permutation: F 2 (x, y) = F (x, y F (x)) = (x, y F (x) F (x)) = (x, y). F (x, 0) = (x, F (x)). Permutation 2-bit gates not enough to implement F. But B = {NOT, Λ } where Λ : B 3 B 3 the Toffoli gate, defined by Λ (x, y, z) = (x, y, z (x y)), is a basis. Notice that Λ (Lambda) is different from (conjunction).

7 Quantum logical gates Fundamental requirements Classically: Bits: elements of B = {0, 1}. Base: complete family B of logical gates acting on small number of bits, eg. B = {XOR, NOT}. Logical circuit: allows computation of any Boolean function f : B n B n, n arbitrary. Quantically: Qubits: unit vectors of B = H = C 2. Base: complete family B of unitary operators acting on small number of qubits. Logical circuit: allows computation of any unitary operator U : H n H n, n arbitrary.

8 Standard quantum logical gates Controlled and multiply controlled gates Definition With every unitary U : H n H n, are associated a family of unitary operators C k (U) : H k H n H H n, k 1 defined for ξ H n by C k (U) b 1 b k ξ = { b1 b k ξ if b 1 b k = 0 b 1 b k U ξ if b 1 b k = 1 Example σ 1 = ( ) unitary. : B B the bit-flip operation 0 = 1, 1 = 0 (corresponds to classical gate NOT). σ 1 b = b, C 2 (σ 1 ) = ˆΛ.

9 Standard quantum logical gates Hadamard and phase gates Definition ( ) Hadamard gate: H = ( ) 1 0 Phase gate: Φ(φ) =. 0 exp(iφ) ( ) 1 0 Enough to consider K = Φ(π/2) = for the phase gate. 0 i Definition Basis B = {H, K, K 1, C(σ 1 ), C 2 (σ 1 )} is termed the standard basis.

10 Shor s algorithm (1994) Integer factoring Algorithm allowing factoring of a large integer n, with N = log n, in polynomial time temps in N. Decomposed into sub-routines: quantum Fourier transform, quantum phase estimation, quantum order finding, factoring.

11 Quantum Fourier transform (QFT) Generalisation of the discrete Fourier transform (DFT) N fixed > 0 integer. x : R C signal sampled at instants {0,..., N 1} becomes vector x = (x 0,..., x N 1 ) C N. Definition Discrete Fourier transform C N x = (x 0,..., x N 1 ) F(x) = y := (y 0,..., y N 1 ) C N, where y j = 1 N 1 N k=0 x k exp(2πik j N ), j {0,..., N 1}. By analogy: quantum Fourier transform on H N = C N : H N = C N j F j = 1 N 1 exp(2πik j N N ) k H N. k=0 For i = 0,..., N 1, abridge unit vector e i into i.

12 Quantum Fourier transform (QFT) Unitarity Recall Theorem F is unitary. F j = 1 N 1 exp(2πik j ) k. N N k=0 Proof. j F Fj = 1 N = 1 N N 1 k,l=0 N 1 k,l=0 exp( 2πik j N ) exp(2πil j N ) k l exp( 2πik (j j) N ) = δ jj.

13 Quantum Fourier transform N = 2 n, H = C 2, H = n 1 k=0 H. Basis vector j H, indexed by integer j = 0,..., 2 n 1. Identify {0,..., 2 n 1} j j = (j 1,... j n ) B n : j = j 1 2 n j n 2 0 = 2 n ( j j n 2 n ) = 2 n 0.j 1 j n 2 = j 1 j n 2 = j 2. j = j 1 j n F 1 2 n 1 exp(2πij k 2 n/2 2 n ) k = 1 2 n/2 = 1 2 n/2 n l=1 k=0 exp(2πij 0.k 1 k n 2 ) k 1 k n (k 1 k n) B n ( 0 + exp(2πij/2 l ) 1 ) = 1 2 n/2 [ 0 + exp(2πij/2) 1 ] [ 0 + exp(2πij/2n ) 1 ].

14 Quantum Fourier transform Logical circuit H Φ 2 H Φ 2 Φ 3 j 1 j 2 j 3 Φ n 2 ψ j Φ n 3... Φ n 2... Φ n 1 Φ n 1 Φ n.... j n 2 H Φ 2 j n 1 j n Theorem ψ j = F j. F implemented by reverting the circuit (reading from left to right).

15 Quantum phase estimation Statement of the problem Definition U : H n H n unitary, u H n eigenvector of U (assumed known by some other source of information). Phase estimation: estimation of φ u [0, 1] s.t. U u = exp(2πiφ u ) u. Assume we have black boxes U 2j, j = 0,..., t 1 and eigenvector u. Immédiat to construct controlled gates C(U 2j ).

16 Quantum phase estimation Quantum circuit H H 0 0 ˆφ u F. H H u U 2t U 21. U 20. u D C B A

17 Quantum phase estimation Functioning principle of the quantum circuit Content of registers ψ u H t H n defore action of the operator F : ψ u = 1 2 [ 0 + t/2 exp(2πi2t 1 φ u) 1 ] [ 0 + exp(2πi2 0 φ u) 1 ] u = 1 exp(2πiφ u k 2 t/2 t1 k 0 ) k t 1 k 0 u k 0 k t 1 B t F on 1 φ u. Theorem = 1 2 t 1 exp(2πiφ uk) k u. 2 t/2 2 t/2 2 t 1 k=0 k=0 exp(2πiφ uk) k : good rational approximation b/2 t of For every ε > 0, there exists integer p = p(ε) > 0 s.t. t = n + p P F ψ( b 2 t φ u < 1 2 n ) 1 ε.

18 Quantum phase estimation Algorithm Algorithm Require: Black boxes C(U 2j ), eigenvector u o U, precision level ε, t = n + log( ε qubits initialised at 0. Ensure: Estimation of φ u precise up to t bits. Initialise 0 t u. Act as in figure. Apply F on register of t first qubits to obtain φ u. Measure register of t first qubits to obtain estimation φ u.

19 Order finding Definition x, N fixed > 1 integer verifying pgcd(x, N) = 1. Order: ord(x, N) = inf{r > 0 : x r = 1 mod N}. Example (ord(x = 5, N = 7) = 6) r r r mod Order finding, conjectured to be algorithmically hard. If L = log N, no known classical algorithm solving the problem in polynomial time in L. Define unitary U y = xy mod N. For y B L, N y 2 L 1, xy on 0 y N 1. mod N = y U acts non trivially solely

20 Order finding Principle of the algorithm Lemma Let r := ord(x, N) N. For s = 0,..., r 1, U u s = exp(2πi s r ) u s, where u s = 1 r r 1 k=0 exp( 2πik s r ) x k mod N. Problem: vector u s needed in previous lemma is an eigenvector of U but its construction presupposes knowledge of r.

21 Order finding Essential technical lemma Lemma 1 r 1 u s = 1. r s=0 Instead of initialising circuit with u s, initialise with 1.

22 Order finding Continued fraction expansion Associate with every α R + sequence o (a 0 ; a 1, a 2, ) α = [a 0 ; a 1, a 2, a 3,...] = a a a 2 + a If α Q +, then α = [a 0 ; a 1..., a M ]. If α (R + \ Q), ithen α = [a 0 ; a 1, a 2, a 3,...], with a i > 0 for all i 1.

23 Continued fraction expansion If α = [a 0 ; a 1, a 2,...], then truncated (at order m) expansion [a 0 ; a 1..., a m ] is a rational approximation of α. [a 0 ; a 1..., a m ] = p m(α) q m (α), p m = a m p m 1 + p m 2 and q m = a m q m 1 + q m 2, m 1, and p 0 = a 0, q 0 = 1, p 1 = 1, and q 1 = 0. Lemma let α m = [a 0 ; a 1..., a m ] = p m /q m the sequence of principal convergents. 1 α 0 α 2m α 2m+2... α... α 2m+1 α 2m 1... α 1 and p lim m α m := lim m(α) m q = α. m(α) 2 Let p q be an irreducible fraction with q > 0. If α p q 1 2q 2 then exists an M s.t. α pm q m 1 2q 2 m for m N >.

24 Order finding Continued fraction expansion routine Algorithm Continued fraction expansion (CFE) Require: real α > 0, integer M > 0. Ensure: a 0,..., a M with a i > 0 for 1 i M. Initialise m 0. repeat a m α. β {α}. m m + 1. if β 0 then α 1 β else α = 0 end if until m > M.

25 Order finding Main programme Algorithm Order finding algorithm (OFA) Require: Integer N with L bits, x comprime with N, precision threshold ε, t = L + log 2 ε qubits initialised at 0, L qubits initialised at 1, implementation of unitary U N,x : H t H L H t H L, CFE algorithm. Ensure: ord(x, N) with probability 1 ε within O(L 3 ) steps. Act as in figure to get state ψ D. Measure in state ψ D to get L-bit approximation θ of the phase. a := [a 0; a 1,..., a n] CFE(θ). s r pn(a) qn(a). if x r mod N = 1 then return r else The algorithm fails. end if

26 Achievement of order finding algorithm Theorem Let r be the value returned by the OFA. Then P(r is the correct order ) 1 4.

27 Factoring Shor s algorithm Algorithm Require: Integer N of L bits, x coprime with N, precision level ε, t = 2L log( ε qubits initialised at 0, U N,x : H t H L H t H L unitary, FractionContinue. Ensure: ord(x, N) with probability 1 ε in O(L 3 ) steps. Let H t I L act on 0 1 H t H L. Act as in figure. Apply F on register of t first qubits to obtain φ u. Measure register of t first qubits to obtain estimation φ u.

28 Factoring Idea of the algorithm Theorem Suppose N is an L-bit composite integer and x a non-trivial a solution to the equation x 2 = 1 mod N for 1 x N. Then at least one of gcd(x 1, N), gcd(x + 1, N) is a non-trivial factor of N. a i.e. neither x = 1 mod N nor x = (N 1) mod N = 1 mod N. Theorem Suppose N = p α1 1 pαm m and x an integer randomly chosen in 1 x N 1 that is coprime with N. Let r = ord(x, N). Then P(r is even and x r/2 = 1 mod N) m. Combine two theorems to give algorithm returning with high probability a non-trivial factor of N. All steps can be performed efficiently on a classical computer except the order finding.

29 Shor s algorithm Scalability arguments Resource scaling O(L 3 ) provided flawless functioning of quantum gates. To factor an L-bit integer N with error corrections 5L + 1 qubits, 72L 3 quantum gates. A simple numerical application: L = 4: 21 qubits, 4608 gates, L = 100: 501 qubits, gates, L = 4096: qubits, gates.

30 Shor s algorithm Achievements Shor s algorithm: Optimisation: 15 = 3 5 (k = 4). Factored by using 7 qubits. 21 = 3 7 (k = 5). Factored by using 10 qubits. 143 = (with 4 qubits) and = (with 4 qubits). Foreseen not yet implemented factoring of = with 6 qubits.