qubovert 0.0.6

A package for converting common problems to QUBO/Ising form

master branch

dev branch

pypi distribution

Please see the Repository and Docs. For examples/tutorials, see the notebooks.

Installation

For the stable release.

pip install qubovert

To install from source:

git clone https://github.com/jiosue/qubovert.git
cd qubovert
pip install -e .

Then you can use it in Python versions 3.6 and above with

import qubovert

# get info
help(qubovert)

# see the main functionality
print(qubovert.__all__)

# see the utilities defined
help(qubovert.utils)
print(qubovert.utils.__all__)

# see the satisfiability library
help(qubovert.sat)
print(qubovert.sat.__all__)

# see all the probles defined
print(qubovert.problems.__all__)

# to see specifically the np problems:
help(qubovert.problems.np)
print(qubovert.problems.np.__all__)

# to see specifically the benchmarking problems:
help(qubovert.problems.benchmarking)
print(qubovert.problems.benchmarking.__all__)

# etc ...

Managing QUBO, Ising, PUBO, HIsing, HOBO, and HOIO formulations

See qubovert.__all__.

• QUBO: Quadratic Unconstrained Binary Optimization

• Ising: quadratic unconstrained spin-1/2 Hamiltonian

• PUBO: Polynomial Unconstrained Binary Optimization

• HIsing: Higher order unconstrained spin-1/2 Hamiltonian

• HOBO: Higher Order Binary Optimization

• HOIO: Higher Order Ising Optimization

See the docstrings for qubovert.HOBO, qubovert.HOIO, qubovert.QUBO, qubovert.Ising, qubovert.PUBO, and qubovert.HIsing.

See the following HOBO examples (much of the same functionality can be used with HOIO problems).

from qubovert import HOBO
from any_module import qubo_solver
# or from qubovert.utils import solve_qubo_bruteforce as qubo_solver

H = HOBO()
H.add_constraint_eq_zero({('a', 1): 2, (1, 2): -1, (): -1})
print(H)
# {('a', 1, 2): -4, (1, 2): 3, (): 1}
H -= 1
print(H)
# {('a', 1, 2): -4, (1, 2): 3}

from qubovert import binary_var

x0, x1, x2 = binary_var("x0"), binary_var("x1"), binary_var("x2")
H = x0 + 2 * x1 * x2 - 3 + x2
print(H)
# {('x0',): 1, ('x1', 'x2'): 2, (): -3, ('x2',): 1}

H = HOBO()

# minimize -x_0 - x_1 - x_2
for i in (0, 1, 2):
    H[(i,)] -= 1

# subject to constraints
H.add_constraint_eq_zero(  # enforce that x_0 x_1 - x_2 == 0
    {(0, 1): 1, (2,): -1}
).add_constraint_lt_zero(  # enforce that x_1 x_2 + x_0 < 1
    {(1, 2): 1, (0,): 1, (): -1}
)
print(H)
# {(1,): -2, (2,): -1, (0, 1): 2, (1, 2): 2, (0, 1, 2): 2}

print(H.solve_bruteforce(all_solutions=True))
# [{0: 0, 1: 1, 2: 0}]

Q = H.to_qubo()
solutions = [H.convert_solution(sol)
             for sol in Q.solve_bruteforce(all_solutions=True)]
print(solutions)
# [{0: 0, 1: 1, 2: 0}]  # matches the HOBO solution!

L = H.to_ising()
solutions = [H.convert_solution(sol)
             for sol in L.solve_bruteforce(all_solutions=True)]
print(solutions)
# [{0: 0, 1: 1, 2: 0}]  # matches the HOBO solution!

# enforce that c == a AND b
H = HOBO().add_constraint_eq_AND('c', 'a', 'b')
print(H)
# {('c',): 3, ('b', 'a'): 1, ('c', 'a'): -2, ('c', 'b'): -2}

H = HOBO()
# make it favorable to AND variables a and b, and variables b and c
H.add_constraint_AND('a', 'b').add_constraint_AND('b', 'c')

# make it favorable to OR variables b and c
H.add_constraint_OR('b', 'c')

# make it favorable to (a AND b) OR (c AND d) OR e
H.add_constraint_OR(['a', 'b'], ['c', 'd'], 'e')

# enforce that 'b' = NOR('a', 'c', 'd')
H.add_constraint_eq_NOR('b', 'a', 'c', 'd')

print(H)
# {(): 5, ('c',): -2, ('c', 'a', 'b', 'd'): 1, ('a', 'e', 'b'): 1,
#  ('c', 'e', 'd'): 1, ('e',): -1, ('a',): -1, ('c', 'a'): 1,
#  ('a', 'd'): 1, ('c', 'b'): 2, ('d',): -1, ('b', 'd'): 2}
Q = H.to_qubo()
print(Q)
# {(): 5, (2,): -2, (5,): 12, (0, 1): 4, (0, 5): -8, (1, 5): -8,
#  (6,): 12, (2, 3): 4, (2, 6): -8, (3, 6): -8, (5, 6): 1, (4, 5): 1,
#  (4, 6): 1, (4,): -1, (0,): -1, (0, 2): 1, (0, 3): 1, (1, 2): 2,
#  (3,): -1, (1, 3): 2}
obj_value, sol = qubo_solver(Q)
print(sol)
# {0: 0, 1: 0, 2: 1, 3: 0, 4: 1, 5: 0, 6: 0}
solution = H.convert_solution(sol)
print(solution)
# {'a': 0, 'b': 0, 'c': 1, 'd': 0, 'e': 1}

See the following PUBO example.

from qubovert import PUBO
from any_module import qubo_solver
# or you can use my bruteforce solver...
# from qubovert.utils import solve_qubo_bruteforce as qubo_solver

pubo = PUBO()
pubo[('a', 'b', 'c', 'd')] -= 3
pubo[('a', 'b', 'c')] += 1
pubo[('c', 'd')] -= 2
pubo[('a',)] += 1
pubo -= 3  # equivalent to pubo[()] -= 3
pubo **= 4
pubo *= 2

Q = pubo.to_qubo()
obj, sol = qubo_solver(Q)
solution = pubo.convert_solution(sol)
print((obj, solution))
# (2, {'a': 1, 'b': 1, 'c': 1, 'd': 0})

Symbols can also be used, for example:

from qubovert import HOIO
from sympy import Symbol

a, b = Symbol('a'), Symbol('b')

# enforce that z_0 + z_1 == 0 with penalty a
H = HOIO().add_constraint_eq_zero({(0,): 1, (1,): 1}, lam=a)
print(H)
# {(): 2*a, (0, 1): 2*a}
H[(0, 1)] += b
print(H)
# {(): 2*a, (0, 1): 2*a + b}
H_subs = H.subs({a: 2})
print(H_subs)
# {(): 4, (0, 1): 4 + b}

H_subs = H.subs({a: 2, b: 3})
print(H_subs)
# {(): 4, (0, 1): 7}

Please note that H.mapping is not necessarily equal to H.subs(...).mapping. Thus, when using the HOBO.convert_solution function, make sure that you use the correct HOBO instance!

The convension used is that () elements of every dictionary corresponds to offsets. Note that some QUBO solvers accept QUBOs where each key is a two element tuple (since for a QUBO {(0, 0): 1} is the same as {(0,): 1}). To get this standard form from our QUBOMatrix object, just access the property Q. Similar for the IsingMatrix. For example:

from qubovert.utils import QUBOMatrix
Q = QUBOMatrix()
Q += 3
Q[(0,)] -= 1
Q[(0, 1)] += 2
Q[(1, 1)] -= 3
print(Q)
# {(): 3, (0,): -1, (0, 1): 2, (1,): -3}
print(Q.Q)
# {(0, 0): -1, (0, 1): 2, (1, 1): -3}
print(Q.offset)
# 3

from qubovert.utils import IsingMatrix
L = IsingMatrix()
L += 3
L[(0, 1, 1)] -= 1
L[(0, 1)] += 2
L[(1, 1)] -= 3
print(L)
# {(0,): -1, (0, 1): 2}
print(L.h)
# {0: -1}
print(L.J)
# {(0, 1): 2}
print(L.offset)
# 0

Common binary optimization utilities (the utils library)

See qubovert.utils.__all__.

We implement various utility functions, including

• solve_pubo_bruteforce,

• solve_hising_bruteforce,

• pubo_value,

• hising_value,

• pubo_to_hising,

• hising_to_pubo,

• subgraph,

and more.

Converting SAT problems (the sat library)

See qubovert.sat.__all__.

Consider the following 3-SAT example.

from qubovert.sat import AND, NOT, OR
from anywhere import qubo_solver

C = AND(OR(0, 1, 2), OR(NOT(0), 2, NOT(3)), OR(NOT(1), NOT(2), 3))

# C is 1 for a satisfying assignment, else 0
# So minimizing P will solve it.
P = -C

# P is a PUBO
Q = P.to_qubo()
solution = qubo_solver(Q)

print(solution)  # {0: 0, 1: 0, 2: 0, 3: 1, 4: 0, 5: 0, 6: 0}
converted_sol = P.convert_solution(solution)
print(converted_sol) # {0: 0, 3: 0, 1: 0, 2: 1}

print(C.value(converted_sol))  # will print 1 because it satisfies C

Convert common problems to QUBO form (the problems library)

See qubovert.problems.__all__.

So far we have just implemented some of the formulations from [Lucas]. The goal of QUBOVert is to become a large collection of problems mapped to QUBO and Ising forms in order to aid the recent increase in study of these problems due to quantum optimization algorithms. Use Python’s help function! I have very descriptive doc strings on all the functions and classes.

See the following Set Cover example. All other problems can be used in a similar way.

from qubovert.problems import SetCover
from any_module import qubo_solver
# or you can use my bruteforce solver...
# from qubovert.utils import solve_qubo_bruteforce as qubo_solver

U = {"a", "b", "c", "d"}
V = [{"a", "b"}, {"a", "c"}, {"c", "d"}]

problem = SetCover(U, V)
Q = problem.to_qubo()

obj, sol = qubo_solver(Q)

solution = problem.convert_solution(sol)

print(solution)
# {0, 2}
print(problem.is_solution_valid(solution))
# will print True, since V[0] + V[2] covers all of U
print(obj == len(solution))
# will print True

To use the Ising formulation instead:

from qubovert.problems import SetCover
from any_module import ising_solver
# or you can use my bruteforce solver...
# from qubovert.utils import solve_ising_bruteforce as ising_solver

U = {"a", "b", "c", "d"}
V = [{"a", "b"}, {"a", "c"}, {"c", "d"}]

problem = SetCover(U, V)
L = problem.to_ising()

obj, sol = ising_solver(L)

solution = problem.convert_solution(sol)

print(solution)
# {0, 2}
print(problem.is_solution_valid(solution))
# will print True, since V[0] + V[2] covers all of U
print(obj == len(solution))
# will print True

To see problem specifics, run

help(qubovert.problems.SetCover)
help(qubovert.problems.VertexCover)
# etc

I have very descriptive doc strings that should explain everything you need to know to use each problem class.

References

[Lucas]

Andrew Lucas. Ising formulations of many np problems. Frontiers in Physics, 2:5, 2014.