If you can write it down,
Quicopt can optimize it.

import numpy as np
import pyomo.environ as pyo
from quicopt import Client

# N binary variables, QUBO matrix Q — given
m = pyo.ConcreteModel()
m.x = pyo.Var(range(N), domain=pyo.Binary)
x = np.array([m.x[i] for i in range(N)])
m.obj = pyo.Objective(expr=x @ Q @ x, sense=pyo.minimize)

client = Client()             # the free tier, no signup
result = client.solve(m)
result.objective              # minimized xᵀQx

using QuicoptClient, JuMP

# N binary variables, QUBO matrix Q — given
m = Model()
@variable(m, x[1:N], Bin)
@objective(m, Min, x' * Q * x)

result = solve(m)             # the free tier, no signup
result.objective              # minimized xᵀQx

require "quicopt"

# N binary variables, QUBO matrix Q — given
m = Quicopt::Model.new
x = Array.new(N) { |i| m.bin_var("x#{i}") }
pairs = (0...N).to_a.product((0...N).to_a)
m.minimize(pairs.sum { |i, j| Q[i][j] * x[i] * x[j] })

client = Quicopt::Client.new  # the free tier, no signup
result = client.solve(m)
result.objective              # minimized xᵀQx