# Solving a simple QUBO with QAOA¶

This section shows how QAOA can be used to solve a simple quadratic unconstrained binary optimisation (QUBO) problem. QUBOs have received significant interest in recent years as a way of tackling discrete combinatorial optimisation tasks, which are widespread in the logistics industry, amongst others. They have also attracted attention for their apparent amenability to implementation on near-term quantum hardware. For a good review of QUBOs and how to formulate them, see Ref 1.

import numpy as np
import matplotlib.pyplot as plt

from pyquil.paulis import PauliSum
from pyquil.api import WavefunctionSimulator
from pyquil.unitary_tools import lifted_pauli

from entropica_qaoa.qaoa.parameters import StandardParams
from entropica_qaoa.qaoa.cost_function import QAOACostFunctionOnWFSim

from scipy.optimize import minimize


## Defining the problem¶

Here we will tackle the simple problem given in Section 2 of Ref 1. We seek the minimum function value, and corresponding configuration of variables, of

$y = -5x_1 -3x_2 -8x_3 -6x_4 + 4x_1x_2 + 8x_1x_3 + 2x_2x_3 + 10x_3x_4$

Here the variables $$x_i$$, $$i = 1,...,4$$ are binary. i.e. they can take the value 0 or 1. Observe that in the linear part (the first 4 terms), all of the variables would ideally be equal to 1 in order to minimise the function. However, the quadratic part (the second 4 terms) encodes penalties for having different pairs of variables equal to 1.

Note that in the linear part, we can simply square all of the variables, since $$x_i = x_i^2$$ for binary variables. We then transform the problem to minimisation of

$\begin{split}y^{\prime} = -5x_1^2 -3x_2^2 -8x_3^2 -6x_4^2 + 4x_1x_2 + 8x_1x_3 + 2x_2x_3 + 10x_3x_4 \\ = \left(\begin{matrix} x_4 & x_3 & x_2 & x_1\end{matrix}\right) \left(\begin{matrix} -6 & 5 & 0 & 0 \\ 5 & -8 & 1 & 4 \\ 0 & 1 & -3 & 2 \\ 0 & 4 & 2 & -5 \end{matrix} \right) \left(\begin{matrix} x_4\\ x_3 \\ x_2 \\ x_1 \end{matrix}\right)\end{split}$

We have ordered the variables in the binary strings such that $$x_1$$ appears as the least significant bit (i.e. on the right of the register), which corresponds to a common convention in the quantum computing community, and in particular for Rigetti’s Pyquil: see here for more information. Let’s plot the function to see what the energy landscape looks like.

def func_y(config):

matr = np.array([[-6, 5, 0, 0], [5, -8, 1, 4], [0, 1, -3, 2], [0, 4, 2, -5]])

return config @ matr @ config

def num_2_bin(num):

bin_str = np.binary_repr(num, width=4)
bin_arr = np.array([int(x) for x in bin_str])

return bin_arr

y_vals = [func_y(num_2_bin(i)) for i in range(16)]
bin_strings = ['{0:04b}'.format(i) for i in range(16)]

plt.plot(y_vals,'o-')
plt.xticks(range(16), bin_strings, rotation=70);
plt.ylabel('y')
plt.show()


The optimal solution is $$x_1 = x_4 = 1$$, $$x_2 = x_3 = 0$$, i.e. the bitstring 1001 (the binary representation of the number 9). Its corresponding function value is $$y =-11$$.

## Mapping to QAOA¶

To solve the problem using QAOA, we can associate the variable $$x_i$$ with the $$i^{th}$$ qubit of our quantum register. If the variable takes value 0, this corresponds to the qubit state being in state $$|0\rangle$$; likewise, the value 1 corresponds to the qubit being in state $$|1\rangle$$.

On a quantum computer we measure the state of a qubit in the $$\{|0\rangle, |1\rangle\}$$ basis by applying the $$Z$$ operator. However, the eigenvalues of this operator are $$+1$$ for the state $$|0\rangle$$, and $$-1$$ for the state $$|1\rangle$$. In order to map this to the 0,1 values of binary variables, we need to modify the measurement operator to be

$x_i \leftrightarrow \frac{\mathbb{I} - Z_i}{2}$

where $$\mathbb{I}$$ is the identity operator. With these considerations, the Hamiltonian $$H_y$$ encoding our objective function $$y$$ is:

$\begin{split}H_y = -\frac{1}{4}\left[5(\mathbb{I} - Z_1)^2 + 3(\mathbb{I} - Z_2)^2 + 8(\mathbb{I} - Z_3)^2 + 6(\mathbb{I} - Z_4)^2\right] \\ + \frac{1}{4}\left[4(\mathbb{I} - Z_1)(\mathbb{I} - Z_2) + 8(\mathbb{I} - Z_1)(\mathbb{I} - Z_3) + 2(\mathbb{I} - Z_2)(\mathbb{I} - Z_3) + 10(\mathbb{I} - Z_3)(\mathbb{I} - Z_4)\right]\end{split}$

Let’s now set the problem up and proceed to solve it using QAOA.

# Linear part of the Hamiltonian
# For simplicity here we do not square each term, as this does not affect the outcome
ham_lin = -0.5*PauliSum.from_compact_str('(-5)*Z1 + (-3)*Z2 + (-8)*Z3 + (-6)*Z4 + 22*I')

# Quadratic part of the Hamiltonian
ham_poly = 0.25*PauliSum.from_compact_str('4*Z1*Z2 + 4*I + (-4)*Z1 + (-4)*Z2 + 8*Z1*Z3 + 8*I + (-8)*Z1 + (-8)*Z3')
ham_poly += 0.25*PauliSum.from_compact_str('2*Z2*Z3 + 2*I + (-2)*Z2 + (-2)*Z3 + 10*Z3*Z4 + 10*I + (-10)*Z3 + (-10)*Z4')

ham = ham_lin + ham_poly


We can check that the eigenvalues of the Hamiltonian ham correspond to the function values we plotted above:

ham_matrix = lifted_pauli(ham, ham.get_qubits())
print('Eigenvalues: ', np.diag(ham_matrix)) # NB: ham is diagonal in the Z basis
print('')
print('Function values: ', y_vals)

Eigenvalues:  [  0.+0.j  -5.+0.j  -3.+0.j  -4.+0.j  -8.+0.j  -5.+0.j  -9.+0.j  -2.+0.j
-6.+0.j -11.+0.j  -9.+0.j -10.+0.j  -4.+0.j  -1.+0.j  -5.+0.j   2.+0.j]

Function values:  [0, -5, -3, -4, -8, -5, -9, -2, -6, -11, -9, -10, -4, -1, -5, 2]


## Solution with StandardParams¶

Let’s tackle the problem using the StandardParams parametrisation. We’ll see how we do with $$p=3$$ timesteps, using Cobyla as our optimiser, and the linear_ramp_from_hamiltonian function to set up our inital parameters.

p = 3
standard_params = StandardParams.linear_ramp_from_hamiltonian(ham,p)

# Set up cost function and run optimisation
cost_std = QAOACostFunctionOnWFSim(ham,standard_params)
res_std = minimize(cost_std, standard_params.raw(), method = 'Cobyla')

# Print the output
res_std

    fun: -9.481743378102294
maxcv: 0.0
message: 'Optimization terminated successfully.'
nfev: 229
status: 1
success: True
x: array([0.57705579, 0.36725195, 0.17168154, 0.13192841, 0.27705965,
0.31679062])


The expectation value of the cost Hamiltonian ham is -9.48, however we know the true solution to have cost -11. Evidently, our QAOA circuit has not returned the correct solution, however we can examine the probability distribution of the output quantum state:

state = cost_std.get_wavefunction(res_std.x)
probs = state.probabilities()
labels = [r'$\left|{0:04b}\right>$'.format(i) for i in range(16)]
plt.bar(range(16),probs)
plt.xticks(range(16), labels, rotation=70);


Observe that the most probable state is $$|1001\rangle$$, which does indeed correspond to the minimum energy configuration of the variables. However, if we were to sample from a quantum computer, the above distribution would not allow us to conclude with high confidence that we have found the solution: there are several other bit strings with relatively high probabilities too.

Let’s try increasing the parameter $$p$$ (the circuit depth) to see how more likely we are to obtain the optimal solution. To do this, we’ll define a simple convenience function.

def state_prob_p(p):

"""
Applies the QAOA circuit and returns the probability of measuring the bitstring 1001
"""

params_std = StandardParams.linear_ramp_from_hamiltonian(ham,p)
cost_std = QAOACostFunctionOnWFSim(ham,params_std)
res_std = minimize(cost_std, params_std.raw(), method = 'Cobyla')
state = cost_std.get_wavefunction(res_std.x)
probs = state.probabilities()

return probs[9] # The bitstring 1001 is the 9th entry of the vector of probabilities


We will run the above function for different values of $$p$$, and see how the probability of the optimal bistring 1001 increases.

# Running this cell should take 2 or 3 minutes.
p_vals = np.arange(1,13)
probability_opt_state = [state_prob_p(i) for i in p_vals]

# Plot the probabilities
plt.plot(p_vals,probability_opt_state,'o-')
plt.xlabel('p')
plt.ylabel('Prob. of ' + r'$|1001\rangle$')
plt.show()


Clearly the optimal bit string probability approaches unity with increasing $$p$$.

## References¶

1. F. Glover et al, A Tutorial on Formulating and Using QUBO Models.