1. Short course on quantum computing Andris Ambainis University of Latvia

2. Lecture 2 Quantum algorithms and factoring

3. Factoring Input: composite N. Output: p, q  {2, …, N-1} s.t. pq=N. Hard for classical computers. Factoring large integers would break RSA.

4. Factoring Quantum computers can factor integers in polynomial (quadratic) time [Shor’94]. Similar approach also solves discrete logarithm by quantum algorithm. Today: Shor’s algorithm.

5. Outline 1) Computational model. 2) Quantum parallelism and quantum interference. 3) Simon’s algorithm. 4) Shor’s algorithm.

6. Basic ideas State space consisting of n (quantum) bits. Elementary gates on 1 or 2 (qu)bits. Efficiently computable = poly-size circuits.

7. Classical circuits X1X1 X2X2  ^ X5X5 X3X3 ^  Result

8. Quantum circuit H HH H Gates on quantum bits

9. Elementary gates (1) Hadamard gate Phase shift

10. Elementary gates (2) Rotation by angle  Controlled NOT

11. Universality Any quantum computation can be performed by a circuit consisting of Hadamard, phase, rotation by  /8 and controlled NOT gates.

12. Classical vs. quantum circuits We have a classical circuit. Can we construct a quantum circuit that computes the same function?

13. Reversibility Assume f(x)=f(y)=z. If then U not unitary.

14. Reversibility |x> |0> |x> |F(x)> F Add extra input initialized to 0. We can transform a classical circuit for F to quantum circuit.

15. Example yx ^ Classical Quantum |x> |y> |0> |x> |y> |x  y> |a> |a  (x  y)> Toffoli gate.

16. Quantum parallelism By linearity, Many evaluations of f in unit time. |x> |0> |x> |f(x)>  |x> |f(x)>  |x> |0> xx

17. Quantum parallelism Once we measure we get one particular x and f(x). Same as if we evaluated f on a random x.  |x> |f(x)> x

18. Quantum parallelism Is it useful? We cannot obtain all values f(x) from because quantum states cannot be measured completely. We can obtain quantities that depend on many f(x).  |x> |f(x)> x

19. Quantum interference Hadamard transform:

20. Quantum interference Negative interference: |1> and -|1> cancel out one another. Positive interference: |0> and |0> add up to a higher probability.

21. Parallelism+interference Use quantum parallelism to compute many f(x). Use interference to obtain information that depends on many values f(x). Requires algebraic structure. Ideal for number-theoretic problems (factoring).

22. Order finding The order of a  Z N * modulo N is the smallest integer r>0 such that a r  1 (mod N) For example, order of 4 mod 7 is 3: 4 1  4, 4 2 =16  2, 4 3 =64  1 (mod 7). Factoring reduces to order-finding.

23. Reduction If a r  1(mod N), then N divides a r -1. If r even, a r -1=(a r/2 -1)(a r/2 +1). If N is product of two or more primes, gcd(a r/2 -1, N) is a nontrivial factor of N with probability at least 1/2.

24. Shor’s algorithm Repeat O(log n) times: Generate random a  {1, …, N-1}; Check if (a, N)=1; r = order(a); If r even, check (a r/2 -1, N).

25. Period finding Function F:N  N such that F(x)=F(x+r) for all x. Find smallest r. |x> |0> |x> |F(x)> F

26. Simon’s problem Function F:{0, 1} n  {0, 1} n. F(x+y)=F(x) for all x, + bitwise addition. Find y. |x> |0> |x> |F(x)> F

27. Algorithm [Simon, 1994] |0> |y> |f(x)> H H H H H H F Repeat n times and combine results y 1,..., y n.

28. Hadamard transform

29. Hadamard on n qubits H H |0>

30. Simon’s algorithm step-by-step |0> |y> |F(x)> H H H H H H F

31. Simon’s algorithm step-by-step Transformations on different qubits commute. We can first measure the last n qubits and then perform Hadamard on first n qubits. Makes calculations simpler.

32. Measuring F(x) Partial measurement. We get some value y=F(x). The state collapses to part consistent with y=F(x).

33. Last step We now have the state How do we get z? Measuring the first register would give only one of x and x+z.

34. Simon’s algorithm |0> |y> |f(x)> H H H H H H F

35. Hadamard transform

36. H H H |x 1 > |x 2 > |x n >...

37. Hadamard transform Signs are the same iff  z i y i = 0 mod 2.

38. Summary Measuring the final state gives a vector y such that n-1 such constraints uniquely determine z, with high probability.

39. Summary Quantum parallelism: computing F for many values simultaneously. Quantum interference: Hadamard transform.

40. Period finding Function F:N  N such that F(x)=F(x+r) for all x. Find r. |x> |0> |x> |F(x)> F

41. Algorithm [Simon, 1994] |0> H H H F Repeat n times and combine results y 1,..., y n. H H H

42. Algorithm [Shor, 1994] |0> F Find factor by continued fraction expansion. QFT

43. Shor’s algorithm step-by-step |0> F QFT

44. Shor’s algorithm step by step Measuring the second register leaves the first register in a state consisting of all x with the same F(x): |d>+|d+r>+…+|d+ir>

45. Quantum Fourier transform If M=2, this is Hadamard transform.

46. QFT detects periods Assume r divides M. Then, If j relatively prime with r,

47. QFT detects periods Assume r does not divide M. Then, most of T|  consists of |k> with

48. QFT detects periods 00 r divides M r does not divide M Can we find r?

49. Continued fraction expansion Number theory algorithm. Given k, M, finds j, r such that is smallest among all j and r  r 0. If M=  (r 2 ), correct w.h.p.

50. Summary of Shor’s factoring Reduce factoring to period-finding. Generate a quantum state with period r. In the easy case, QFT transforms a state with period r into multiples of M/r. General case: same but approximately. Continued fraction algorithm finds the closest multiple of M/r.

51. Hidden subgroup Function F:G  S such that F(g)=F(hg) iff h  H. Find H. |x> |0> |x> |F(x)> F

52. Hidden subgroup Captures a lot of problems. Simon’s problem: G={0, 1} n, H={0 n, z}. Shor’s period-finding: G=Z, H=rZ (multiples of r). Discrete logarithm: G=Z 2. Pell’s equation [Hallgren, 2002]: G=R.

53. Discrete log Given N, g and x, compute r such that g r  x (mod N). Another hard problem relevant to crypto (Diffie-Hellman).

54. Discrete log Define F(y, z)=g y x z mod N. G=Z 2. H={y,z | y+zr =0 mod N-1} because g y x z =g y+rz and g N-1 =1.

55. Status of hidden subgroup Quantum polynomial time for Abelian G. Open for non-Abelian G (except a few groups G with simple structure).

56. Graph Isomorphism  ? G1G1 G2G2

57. G: all permutations of vertices. F(  ) =  (G). H - permutations that fix G.

58. Hidden subgroup Graph Isomorphism reduces to hidden subgroup for non-Abelian groups. Approximating shortest vector in lattice also reduces to HSP. Solving HSP by quantum algorithm remains open for almost all non-Abelian groups.