Error when the objective function includes a linear term

[eegzheng]

Hello,

I am trying a toy example by adapting the code from your max-cut example and is shown below, but I found as long as there is a linear term in the objective function, it will shows error below:
... \SimCIM.py", line 86, in update
(self.J.dot(self.x) + self.h) * self.dt
ValueError: operands could not be broadcast together with shapes (8,100) (8,)

Without h or without s.update(), it works fine.

##########################################################
import numpy as np
from mindquantum.algorithm.qaia import ASB, BSB, DSB, SimCIM, NMFA

N = 8 # number of spins

QUBO problem formulation

min x'Jx + h'*x, x is a spin vector variable

J = np.array([[ 0, 50, 0, 0, -50, -100, 0, 0],
[ 50, 0, 0, 0, -50, -100, 0, 0],
[ 0, 0, 0, 50, 0, 0, -50, -100],
[ 0, 0, 50, 0, 0, 0, -50, -100],
[ -50, -50, 0, 0, 0, 100, -2, 0],
[-100, -100, 0, 0, 100, 0, 0, 0],
[ 0, 0, -50, -50, -2, 0, 0, 100],
[ 0, 0, -100, -100, 0, 0, 100, 0]])
h = np.array([ 103, 100, 100, 100, -100, -200, -100, -200])

sample_size=100
n_iter_list = [10, 25, 50]
cut_value_list = []
for n_iter in n_iter_list:
s = SimCIM(-2*J,-h,batch_size=sample_size, n_iter=n_iter)
s.update()
cut_value = s.calc_energy()
cut_value_list.append(cut_value)

I also wonder how to extra the desired solution and the associated objective value. Is x = np.sign(s.x) the way to find all sample solutions, and the associated solutions are stored in s.calc_energy()?

Many thanks!

[dsdsdshe]

We are very sorry. Since our main repository is on Gitee and the GitHub repository is only automatically synchronized, we previously overlooked the issues on GitHub. It has been verified that this error has been resolved, and cases involving linear terms can now be calculated normally. Additionally, the corresponding solution and related objective values are s.x, s.cul_cut(), and s.cul_energy().