Source code for sympy.physics.quantum.shor

"""Shor's algorithm and helper functions.

Todo:

* Get the CMod gate working again using the new Gate API.
* Fix everything.
* Update docstrings and reformat.
* Remove print statements. We may want to think about a better API for this.
"""
from __future__ import print_function, division

import math
import random

from sympy import Mul, S
from sympy import log, sqrt
from sympy.core.numbers import igcd
from sympy.utilities.iterables import variations

from sympy.physics.quantum.gate import Gate
from sympy.physics.quantum.qubit import Qubit, measure_partial_oneshot
from sympy.physics.quantum.qapply import qapply
from sympy.physics.quantum.qft import QFT
from sympy.physics.quantum.qexpr import QuantumError


class OrderFindingException(QuantumError):
    pass


[docs]class CMod(Gate): """A controlled mod gate. This is black box controlled Mod function for use by shor's algorithm. TODO implement a decompose property that returns how to do this in terms of elementary gates """ @classmethod def _eval_args(cls, args): # t = args[0] # a = args[1] # N = args[2] raise NotImplementedError('The CMod gate has not been completed.') @property
[docs] def t(self): """Size of 1/2 input register. First 1/2 holds output.""" return self.label[0]
@property
[docs] def a(self): """Base of the controlled mod function.""" return self.label[1]
@property
[docs] def N(self): """N is the type of modular arithmetic we are doing.""" return self.label[2]
def _apply_operator_Qubit(self, qubits, **options): """ This directly calculates the controlled mod of the second half of the register and puts it in the second This will look pretty when we get Tensor Symbolically working """ n = 1 k = 0 # Determine the value stored in high memory. for i in range(self.t): k = k + n*qubits[self.t + i] n = n*2 # The value to go in low memory will be out. out = int(self.a**k % self.N) # Create array for new qbit-ket which will have high memory unaffected outarray = list(qubits.args[0][:self.t]) # Place out in low memory for i in reversed(range(self.t)): outarray.append((out >> i) & 1) return Qubit(*outarray)
[docs]def shor(N): """This function implements Shor's factoring algorithm on the Integer N The algorithm starts by picking a random number (a) and seeing if it is coprime with N. If it isn't, then the gcd of the two numbers is a factor and we are done. Otherwise, it begins the period_finding subroutine which finds the period of a in modulo N arithmetic. This period, if even, can be used to calculate factors by taking a**(r/2)-1 and a**(r/2)+1. These values are returned. """ a = random.randrange(N - 2) + 2 if igcd(N, a) != 1: print("got lucky with rand") return igcd(N, a) print("a= ", a) print("N= ", N) r = period_find(a, N) print("r= ", r) if r % 2 == 1: print("r is not even, begin again") shor(N) answer = (igcd(a**(r/2) - 1, N), igcd(a**(r/2) + 1, N)) return answer
def getr(x, y, N): fraction = continued_fraction(x, y) # Now convert into r total = ratioize(fraction, N) return total def ratioize(list, N): if list[0] > N: return S.Zero if len(list) == 1: return list[0] return list[0] + ratioize(list[1:], N)
[docs]def continued_fraction(x, y): """This applies the continued fraction expansion to two numbers x/y x is the numerator and y is the denominator >>> from sympy.physics.quantum.shor import continued_fraction >>> continued_fraction(3, 8) [0, 2, 1, 2] """ x = int(x) y = int(y) temp = x//y if temp*y == x: return [temp, ] list = continued_fraction(y, x - temp*y) list.insert(0, temp) return list
[docs]def period_find(a, N): """Finds the period of a in modulo N arithmetic This is quantum part of Shor's algorithm.It takes two registers, puts first in superposition of states with Hadamards so: ``|k>|0>`` with k being all possible choices. It then does a controlled mod and a QFT to determine the order of a. """ epsilon = .5 #picks out t's such that maintains accuracy within epsilon t = int(2*math.ceil(log(N, 2))) # make the first half of register be 0's |000...000> start = [0 for x in range(t)] #Put second half into superposition of states so we have |1>x|0> + |2>x|0> + ... |k>x>|0> + ... + |2**n-1>x|0> factor = 1/sqrt(2**t) qubits = 0 for arr in variations(range(2), t, repetition=True): qbitArray = arr + start qubits = qubits + Qubit(*qbitArray) circuit = (factor*qubits).expand() #Controlled second half of register so that we have: # |1>x|a**1 %N> + |2>x|a**2 %N> + ... + |k>x|a**k %N >+ ... + |2**n-1=k>x|a**k % n> circuit = CMod(t, a, N)*circuit #will measure first half of register giving one of the a**k%N's circuit = qapply(circuit) print("controlled Mod'd") for i in range(t): circuit = measure_partial_oneshot(circuit, i) # circuit = measure(i)*circuit # circuit = qapply(circuit) print("measured 1") #Now apply Inverse Quantum Fourier Transform on the second half of the register circuit = qapply(QFT(t, t*2).decompose()*circuit, floatingPoint=True) print("QFT'd") for i in range(t): circuit = measure_partial_oneshot(circuit, i + t) # circuit = measure(i+t)*circuit # circuit = qapply(circuit) print(circuit) if isinstance(circuit, Qubit): register = circuit elif isinstance(circuit, Mul): register = circuit.args[-1] else: register = circuit.args[-1].args[-1] print(register) n = 1 answer = 0 for i in range(len(register)/2): answer += n*register[i + t] n = n << 1 if answer == 0: raise OrderFindingException( "Order finder returned 0. Happens with chance %f" % epsilon) #turn answer into r using continued fractions g = getr(answer, 2**t, N) print(g) return g