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import pennylane as qml
from pennylane import numpy as np
from rich.progress import track
def generate_hamiltonian(graph):
cost_hamiltonian = sum(qml.Hamiltonian([1/4,1/4,1/4], [
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)
return cost_hamiltonian
class QC:
def __init__(self, graph, n_vertices, n_layers):
self.graph = graph
self.n_vertices = n_vertices
self.n_wires = 2*n_vertices
self.n_layers = n_layers
self.hamiltonian = generate_hamiltonian(graph)
self.dev = qml.device("default.qubit", wires=self.n_wires)
# unitary operator U_B with parameter beta
def U_B(self, beta):
for wire in range(self.n_wires):
qml.RX(beta, wires=wire)
# unitary operator U_C with parameter gamma
def U_C(self, gamma):
for edge in self.graph:
wire1 = 2*edge[0]
wire2 = wire1+1
wire3 = 2*edge[1]
wire4 = wire3+1
qml.CNOT(wires=[wire1, wire3])
qml.RZ(gamma/4, wires=wire3)
qml.CNOT(wires=[wire1, wire3])
qml.CNOT(wires=[wire2, wire4])
qml.RZ(gamma/4, 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/4, wires=wire4)
qml.CNOT(wires=[wire3, wire4])
qml.CNOT(wires=[wire2, wire4])
qml.CNOT(wires=[wire1, wire4])
def circuit(self, gammas, betas):
for wire in range(self.n_wires):
qml.PauliX(wire)
qml.Hadamard(wires=wire)
for i in range(self.n_layers):
self.U_C(gammas[i])
self.U_B(betas[i])
def cost_function(self):
@qml.qnode(self.dev)
def _cost_function(params):
self.circuit(params[0], params[1])
return qml.expval(self.hamiltonian)
return _cost_function
def probability_circuit(self, params):
@qml.qnode(self.dev)
def _probability_circuit(params):
self.circuit(params[0], params[1])
return qml.probs()
return _probability_circuit(params)
def h_exp(self):
@qml.qnode(self.dev)
def _h_exp():
return qml.expval(self.hamiltonian)
return _h_exp()
def qaoa_color(self):
gammas = np.linspace(0, 1, num=self.n_layers)
betas = np.linspace(1, 0, num=self.n_layers)
params = np.array([gammas,betas])
opt = qml.QNSPSAOptimizer()
steps = 10
for i in track(range(steps)):
params, cost = opt.step_and_cost(self.cost_function(), params)
probs = self.probability_circuit(params)
print(f"{cost: .7f}, {np.argmax(probs):0{self.n_wires}b}")
return params
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