QAOA for MaxCut

In the Pennylane instance, a Max cut problem of 4 qubits was shown. Here, I want to try to study 5 qubits, but there is a problem in the output results. The results are still four qubit results, and I am trying to change【xtick_labels = list(map(lambda x: format(x, “04b”), xticks))】to【xtick_labels = list(map(lambda x: format(x, “05b”), xticks))】But it’s just adding a |0> before each bit, which is problematic. How should I modify it?

Best wishes!

# Put code here
import pennylane as qml
from pennylane import numpy as np


n_wires = 5
graph = [(0, 1), (0, 3), (0, 2), (0, 4)]

# unitary operator U_B with parameter beta
def U_B(beta):
    for wire in range(n_wires):
        qml.RX(2 * beta, wires=wire)
def U_C(gamma):
    for edge in graph:
        wire1 = edge[0]
        wire2 = edge[1]
        qml.CNOT(wires=[wire1, wire2])
        qml.RZ(2 * gamma, wires=wire2)
        qml.CNOT(wires=[wire1, wire2])

def bitstring_to_int(bit_string_sample):
    bit_string = "".join(str(bs) for bs in bit_string_sample)
    return int(bit_string, base=2)

dev = qml.device("lightning.qubit", wires=n_wires, shots=1)

def circuit(gammas, betas, edge=None, n_layers=1):
    # apply Hadamards to get the n qubit |+> state
    for wire in range(n_wires):
    # p instances of unitary operators
    for i in range(n_layers):
    if edge is None:
        # measurement phase
        return qml.sample()
    # during the optimization phase we are evaluating a term
    # in the objective using expval
    H = qml.PauliZ(edge[0]) @ qml.PauliZ(edge[1])
    return qml.expval(H) 
def qaoa_maxcut(n_layers=1):

    # initialize the parameters near zero
    init_params = 0.01 * np.random.rand(2, n_layers, requires_grad=True)

    # minimize the negative of the objective function
    def objective(params):
        gammas = params[0]
        betas = params[1]
        neg_obj = 0
        for edge in graph:
            # objective for the MaxCut problem
            neg_obj -= 0.5 * (1 - circuit(gammas, betas, edge=edge, n_layers=n_layers))
        return neg_obj

    # initialize optimizer: Adagrad works well empirically
    opt = qml.AdagradOptimizer(stepsize=0.5)

    # optimize parameters in objective
    params = init_params
    steps = 30
    for i in range(steps):
        params = opt.step(objective, params)
        if (i + 1) % 5 == 0:
            print("Objective after step {:5d}: {: .7f}".format(i + 1, -objective(params)))

    # sample measured bitstrings 100 times
    bit_strings = []
    n_samples = 100
    for i in range(0, n_samples):
        bit_strings.append(bitstring_to_int(circuit(params[0], params[1], edge=None, n_layers=n_layers)))

    # print optimal parameters and most frequently sampled bitstring
    counts = np.bincount(np.array(bit_strings))
    most_freq_bit_string = np.argmax(counts)
    print("Optimized (gamma, beta) vectors:\n{}".format(params[:, :n_layers]))
    print("Most frequently sampled bit string is: {:04b}".format(most_freq_bit_string))

    return -objective(params), bit_strings
bitstrings1 = qaoa_maxcut(n_layers=1)[1]
bitstrings2 = qaoa_maxcut(n_layers=2)[1]
import matplotlib.pyplot as plt

xticks = range(0, 16)
xtick_labels = list(map(lambda x: format(x, "05b"), xticks))
bins = np.arange(0, 17) - 0.5

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
plt.subplot(1, 2, 1)
plt.xticks(xticks, xtick_labels, rotation="vertical")
plt.hist(bitstrings1, bins=bins)

plt.subplot(1, 2, 2)
plt.xticks(xticks, xtick_labels, rotation="vertical")
plt.hist(bitstrings2, bins=bins)

# Put full error message here

Hello @ming !

I checked your script and it seems that the results are compatible with a 5 qubits problem. However, on this line, the variable most_freq_bit_string is being formatted as a length-4 binary string by writing :04b, as Isaac pointed out in a previous post you made:

I think you are getting the impression the result is 4 qubits from here. I would suggest as debug exercise to check out in the decimal form and see if makes sense :slight_smile:

Another problem I’ve found in you code is this part:

The number of ticks is not compatible with the range of your results, since you have 2^5 possible binary string outcomes. Try fixing the range of the bar plot to match the dimension of your problem I am sure this will help to better visualize your results. :wink: