About CQM model solving

Hi，

I had some problems with the D-Wave CQM solution. Under the condition of constraint is seldom, still can't find the optimal solution (using the classic solver validated results). Here is my objective function:

x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15

Here is the file contents of my constraints:

1*x0+1*x5+1*x10+1*x15+1*x16+1*x17+1*x18+1*x19-5*d0>=0

1*d0-1*x0>=0

1*d0-1*x5>=0

1*d0-1*x10>=0

1*d0-1*x15>=0

1*d0-1*x16>=0

1*d0-1*x17>=0

1*d0-1*x18>=0

1*d0-1*x19>=0

1*x1+1*x6+1*x11+1*x12+1*x20+1*x21+1*x22+1*x23-5*d1>=0

1*d1-1*x1>=0

1*d1-1*x6>=0

1*d1-1*x11>=0

1*d1-1*x12>=0

1*d1-1*x20>=0

1*d1-1*x21>=0

1*d1-1*x22>=0

1*d1-1*x23>=0

1*x2+1*x7+1*x8+1*x13+1*x24+1*x25+1*x26+1*x27-5*d2>=0

1*d2-1*x2>=0

1*d2-1*x7>=0

1*d2-1*x8>=0

1*d2-1*x13>=0

1*d2-1*x24>=0

1*d2-1*x25>=0

1*d2-1*x26>=0

1*d2-1*x27>=0

1*x3+1*x4+1*x9+1*x14+1*x28+1*x29+1*x30+1*x31-5*d3>=0

1*d3-1*x3>=0

1*d3-1*x4>=0

1*d3-1*x9>=0

1*d3-1*x14>=0

1*d3-1*x28>=0

1*d3-1*x29>=0

1*d3-1*x30>=0

1*d3-1*x31>=0

1*x0+1*x1+1*x2+1*x3+1*x4+1*x5+1*x6+1*x7+1*x8+1*x9+1*x10+1*x11+1*x12+1*x13+1*x14+x15>=1

Here is my using quantum annealing to solve the objective function of the python code snippet:

variable_num_x = Rounds * 32
variable_num_d = Rounds * 4
for i in range(variable_num_x):
    exec('x' + str(i) + '=Binary(' + str(i) + ')')
for i in range(variable_num_d):
    exec('d' + str(i) + '=Binary(' + str(i) + ')')
for i in range(Rounds * 16):
    fun += eval('x' + str(i))
cqm.set_objective(fun)
with open('./AES_quantum.lp', 'r') as file:
    for line in file:
        line_ = eval(line.replace("\n", ""))
        cqm.add_constraint(line_)
result = set()
print('---------Annealing---------')
for i in range(1000):
    response = LeapHybridCQMSampler(profile=num_dwave).sample_cqm(cqm)
for sample, satisfied, energy in response.data(['sample', 'is_feasible', 'energy']):
    if not satisfied:
        continue
    if energy < minenergy:
        minenergy = energy
        result.clear()
        temp = []
        for i in range(1, Rounds*16 + 1):
            temp.append((2 * int(sample[i]) - 1) * i)
        lenth = len(result)
        result.add(tuple(temp))
        if lenth < len(result) and minenergy == 1:
            print(temp)
    elif energy == minenergy:
        temp = []
        for i in range(1, Rounds*16 + 1):
            temp.append((2 * int(sample[i]) - 1) * i)
        lenth = len(result)
        result.add(tuple(temp))
        if lenth < len(result) and minenergy == 1:
            print(temp)

I knew that D-Wave was known for solving large scale problems, and I was wondering if the situation I was experiencing was a problem with my code (e.g., a problem with the format that D-Wave solves), or how I could get the right answer. Your answer is urgently needed.