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Diffstat (limited to 'main.py')
| -rw-r--r-- | main.py | 126 |
1 files changed, 126 insertions, 0 deletions
@@ -0,0 +1,126 @@ +import pennylane as qml +from pennylane import numpy as np + +n_vertices = 5 +n_wires = 2*n_vertices + +graph = [(0,1),(0,2),(0,3),(0,4),(1,2),(1,3),(2,3),(2,4),(3,4)] +# graph = [(0,2)] + +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 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("default.qubit", wires=n_wires, shots=1) + +@qml.qnode(dev) +def circuit(gammas, betas, sample=False, n_layers=1): + # apply Hadamards to get the n qubit |+> state + for wire in range(n_wires): + qml.Hadamard(wires=wire) + # p instances of unitary operators + for i in range(n_layers): + U_C(gammas[i]) + U_B(betas[i]) + if sample: + # measurement phase + return qml.sample() + + return qml.expval(cost_hamiltonian) + +def qaoa_maxcut(n_layers=1): + print("\np={:d}".format(n_layers)) + + # initialize the parameters near zero + init_gammas = 0.01 * np.random.rand(n_layers, requires_grad=True) + init_betas = 0.01 * np.random.rand(n_layers, requires_grad=True) + + # initialize optimizer: Adagrad works well empirically + opt = qml.QNSPSAOptimizer() + + # optimize parameters in objective + gammas = init_gammas + betas = init_betas + steps = 200 + for i in range(steps): + params, cost = opt.step_and_cost(circuit, gammas, betas, n_layers=n_layers) + gammas = params[0] + betas = params[1] + if (i + 1) % 5 == 0: + print("Objective after step {:5d}: {: .7f}".format(i + 1, cost)) + + # sample measured bitstrings 100 times + bit_strings = [] + n_samples = 1000 + for i in range(0, n_samples): + bit_strings.append(bitstring_to_int(circuit(gammas, betas, sample=True, 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(gammas, betas)) + print("Most frequently sampled bit string is: {:010b}".format(most_freq_bit_string)) + + return circuit(gammas, betas, n_layers=n_layers), bit_strings + +bitstrings1 = qaoa_maxcut(n_layers=2)[1] + +# import matplotlib.pyplot as plt +# matplotlib.use('module://matplotlib-backend-sixel') +# +# xticks = range(0, 16) +# xtick_labels = list(map(lambda x: format(x, "04b"), xticks)) +# bins = np.arange(0, 17) - 0.5 +# +# fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4)) +# plt.subplot(1, 2, 1) +# plt.title("n_layers=1") +# plt.xlabel("bitstrings") +# plt.ylabel("freq.") +# plt.xticks(xticks, xtick_labels, rotation="vertical") +# plt.hist(bitstrings1, bins=bins) +# plt.subplot(1, 2, 2) +# plt.title("n_layers=2") +# plt.xlabel("bitstrings") +# plt.ylabel("freq.") +# plt.xticks(xticks, xtick_labels, rotation="vertical") +# plt.hist(bitstrings2, bins=bins) +# plt.tight_layout() +# plt.show() |