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import pennylane as qml
from pennylane import numpy as np
from rich.progress import track
n_vertices = 5
n_wires = 2*n_vertices
n_layers = 3
graph = [(0,1),(0,2),(0,3),(0,4),(1,2),(1,3),(2,3),(2,4),(3,4)]
cost_hamiltonian = sum(qml.Hamiltonian([1,1,1], [
qml.PauliZ(2*edge[0]) @ qml.PauliZ(2*edge[1]),
qml.PauliZ(2*edge[0]+1) @ qml.PauliZ(2*edge[1]+1),
qml.PauliZ(2*edge[0]) @ qml.PauliZ(2*edge[0]+1) @ qml.PauliZ(2*edge[1]) @ qml.PauliZ(2*edge[1]+1)
]) for edge in graph)
# unitary operator U_B with parameter beta
def U_B(beta):
for wire in range(n_wires):
qml.RX(2 * beta, wires=wire)
# unitary operator U_C with parameter gamma
def U_C(gamma):
for edge in graph:
wire1 = 2*edge[0]
wire2 = wire1+1
wire3 = 2*edge[1]
wire4 = wire3+1
qml.CNOT(wires=[wire1, wire3])
qml.RZ(gamma, wires=wire3)
qml.CNOT(wires=[wire1, wire3])
qml.CNOT(wires=[wire2, wire4])
qml.RZ(gamma, wires=wire4)
qml.CNOT(wires=[wire2, wire4])
qml.CNOT(wires=[wire1, wire4])
qml.CNOT(wires=[wire2, wire4])
qml.CNOT(wires=[wire3, wire4])
qml.RZ(gamma, wires=wire4)
qml.CNOT(wires=[wire3, wire4])
qml.CNOT(wires=[wire2, wire4])
qml.CNOT(wires=[wire1, wire4])
def circuit(gammas, betas):
for wire in range(n_wires):
qml.Hadamard(wires=wire)
for i in range(n_layers):
U_C(gammas[i])
U_B(betas[i])
dev = qml.device("default.qubit", wires=n_wires)
@qml.qnode(dev)
def cost_function(params):
circuit(params[0], params[1])
return qml.expval(cost_hamiltonian)
@qml.qnode(dev)
def probability_circuit(params):
circuit(params[0], params[1])
return qml.probs()
def qaoa_color():
params = np.random.rand(2, n_layers, requires_grad=True)
print(params)
opt = qml.QNSPSAOptimizer()
steps = 200
for i in track(range(steps)):
params, cost = opt.step_and_cost(cost_function, params)
probs = probability_circuit(params)
print("{: .7f}, {:010b}".format(cost, np.argmax(probs)))
return params
@qml.qnode(dev)
def h_exp():
return qml.expval(cost_hamiltonian)
params = qaoa_color()
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