Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
..
Failed to load latest commit information.
getting-started
README.md
__init__.py
maxcut_qaoa.py
numpartition_qaoa.py
qaoa.py
utils.py

README.md

pyqaoa

A python implementation of the Quantum Approximate Optimization Algorithm using pyQuil and the Rigetti Forest.

Structure

qaoa.py contains the base QAOA class and routines for finding the optimal rotation angles via the variational-quantum-eigensolver method, state preparation methods, storing results, and utilities for probabilities and collecting bitstrings after a state preparation.

maxcut_qaoa.py takes a graph defined with either NetworkX or a list of node pairs and implements the cost function for MAX-CUT problems.

numberpartiition_qaoa.py takes a list of numbers and set sup a QAOA instance for determining the equal biparitioning of the list.

Run

The simplest way to interact with the QAOA library is through the methods provided for each problem instance. For example, to run max cut import maxcut_qaoa from maxcut_qaoa.py and pass graph to the script. This function will return a QAOA instance. Calling get_angles() on the instance will start the variational-quantum-eigensolver loop in order to find the beta, gamma angles.

Examples using qaoa

import numpy as np
from grove.pyqaoa.maxcut_qaoa import maxcut_qaoa
import pyquil.api as api
qvm_connection = api.QVMConnection()
square_ring = [(0,1),(1,2),(2,3),(3,0)]
steps = 2; n_qubits = 4
betas = np.random.uniform(0, np.pi, p); gammas = np.random.uniform(0, 2*np.pi, p)
inst = maxcut_qaoa(square_ring, steps=steps)
inst.get_angles()

to see the final |beta,gamma> state we can rebuild the quil program that gives us |beta,gamma> and evaluate the wave function using the qvm

t = np.hstack((inst.betas, inst.gammas))
param_prog = inst.get_parameterized_program()
prog = param_prog(t)
wf = qvm_connection.wavefunction(prog)
wf = wf.amplitudes

wf is now a numpy array of complex-valued amplitudes for each computational basis state. To visualize the distribution iterate over the states and calculate the probability.

for state_index in range(2**inst.n_qubits):
    print inst.states[state_index], np.conj(wf[state_index])*wf[state_index]

You should then see that the algorithm converges on the expected solutions of 0101 and 1010!

    0000 (4.38395094039e-26+0j)
    0001 (5.26193287055e-15+0j)
    0010 (5.2619328789e-15+0j)
    0011 (1.52416449345e-13+0j)
    0100 (5.26193285935e-15+0j)
    0101 (0.5+0j)
    0110 (1.52416449362e-13+0j)
    0111 (5.26193286607e-15+0j)
    1000 (5.26193286607e-15+0j)
    1001 (1.52416449362e-13+0j)
    1010 (0.5+0j)
    1011 (5.26193285935e-15+0j)
    1100 (1.52416449345e-13+0j)
    1101 (5.2619328789e-15+0j)
    1110 (5.26193287055e-15+0j)
    1111 (4.38395094039e-26+0j)

Dependencies

  • Numpy
  • Scipy
  • pyQuil
  • Mock (for development testing)
  • NetworkX (for building and analyzing graphs)
  • Matplotlib (useful for plotting)

Building the Docs

To build the documentation run

cd docs/
make html

To view the docs navigate to the docs/_build directory in the pyQAOA root directory and open the index.html file a browser.