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p cnf 3 3
1 -2 3 0
1 2 -3 0
-1 -2 3 0

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from constraint import Problem

problem = Problem()

problem.addVariable('x', range(1, 10))
problem.addVariable('y', range(1, 10))
problem.addVariable('z', range(1, 10))


def constraintCheck(x, y, z):

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marekyggdrasil

#!/usr/bin/env python
# -*- coding:utf-8 vi:ts=4:noexpandtab
# Simple RTSP server. Run as-is or with a command-line to replace the default pipeline

import time
import sys
import abc
import numpy as np
import typing as typ
from fractions import Fraction

marekyggdrasil

This is code repository for Quantum Adiabatic Optimization article.

Start by running instance.py that will build list-based definiton of the graph from its set-based definition, this will let you keep consistent indices between all the runs.

The run.py is the adiabatic optimization code.

Energy spectrum can be examined by running eigenenergies.py with single command line argument being one of HMVC, HMVC_, HMIS or HMIS_. I don't suggest running all the diagonalizations in single run, graphs are big and you are likely to get a segmentation fault error. This is why it is good to keep consistent indices and store your graph using lists and not sets.

You can plot figures using plot.py.

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To claim this, I am signing this object:

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import numpy as np

import itertools

from qutip import basis, tensor, rand_ket, snot, cnot, rx, rz, qeye

# create random state
psi = rand_ket(2)

# initial state

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