def test_qaoa(self, w, prob, m, solutions): """ QAOA test """ seed = 0 aqua_globals.random_seed = seed os.environ.pop('QISKIT_AQUA_CIRCUIT_CACHE', None) self.log.debug('Testing %s-step QAOA with MaxCut on graph\n%s', prob, w) backend = BasicAer.get_backend('statevector_simulator') optimizer = COBYLA() qubit_op, offset = max_cut.get_qubit_op(w) qaoa = QAOA(qubit_op, optimizer, prob, mixer=m) # TODO: cache fails for QAOA since we construct the evolution circuit via instruction quantum_instance = QuantumInstance(backend, circuit_caching=False, seed_simulator=seed, seed_transpiler=seed) result = qaoa.run(quantum_instance) x = sample_most_likely(result['eigvecs'][0]) graph_solution = max_cut.get_graph_solution(x) self.log.debug('energy: %s', result['energy']) self.log.debug('time: %s', result['eval_time']) self.log.debug('maxcut objective: %s', result['energy'] + offset) self.log.debug('solution: %s', graph_solution) self.log.debug('solution objective: %s', max_cut.max_cut_value(x, w)) self.assertIn(''.join([str(int(i)) for i in graph_solution]), solutions) if quantum_instance.has_circuit_caching: self.assertLess(quantum_instance._circuit_cache.misses, 3)
def test_qaoa(self, w, p, m, solutions): os.environ.pop('QISKIT_AQUA_CIRCUIT_CACHE', None) self.log.debug('Testing {}-step QAOA with MaxCut on graph\n{}'.format( p, w)) np.random.seed(0) backend = BasicAer.get_backend('statevector_simulator') optimizer = COBYLA() qubit_op, offset = max_cut.get_max_cut_qubitops(w) qaoa = QAOA(qubit_op, optimizer, p, mixer=m) # TODO: cache fails for QAOA since we construct the evolution circuit via instruction quantum_instance = QuantumInstance(backend, circuit_caching=False) result = qaoa.run(quantum_instance) x = max_cut.sample_most_likely(result['eigvecs'][0]) graph_solution = max_cut.get_graph_solution(x) self.log.debug('energy: {}'.format(result['energy'])) self.log.debug('time: {}'.format(result['eval_time'])) self.log.debug('maxcut objective: {}'.format(result['energy'] + offset)) self.log.debug('solution: {}'.format(graph_solution)) self.log.debug('solution objective: {}'.format( max_cut.max_cut_value(x, w))) self.assertIn(''.join([str(int(i)) for i in graph_solution]), solutions) if quantum_instance.has_circuit_caching: self.assertLess(quantum_instance._circuit_cache.misses, 3)
def test_change_operator_size(self): """ QAOA test """ backend = BasicAer.get_backend('statevector_simulator') optimizer = COBYLA(maxiter=2) qubit_op, _ = max_cut.get_operator(W1) qubit_op = qubit_op.to_opflow().to_matrix_op() seed = 0 aqua_globals.random_seed = seed qaoa = QAOA(qubit_op, optimizer, P1) quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed) qaoa.run(quantum_instance) qaoa.operator = (X ^ qubit_op ^ Z) qaoa.run()
def test_qaoa_qc_mixer_many_parameters(self): """ QAOA test with a mixer as a parameterized circuit with the num of parameters > 1. """ seed = 0 aqua_globals.random_seed = seed optimizer = COBYLA() qubit_op, _ = max_cut.get_operator(W1) qubit_op = qubit_op.to_opflow() num_qubits = qubit_op.num_qubits mixer = QuantumCircuit(num_qubits) for i in range(num_qubits): theta = Parameter('θ' + str(i)) mixer.rx(theta, range(num_qubits)) qaoa = QAOA(qubit_op, optimizer=optimizer, p=2, mixer=mixer) backend = BasicAer.get_backend('statevector_simulator') quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed) result = qaoa.run(quantum_instance) x = sample_most_likely(result.eigenstate) print(x) graph_solution = max_cut.get_graph_solution(x) self.assertIn(''.join([str(int(i)) for i in graph_solution]), S1)
def test_qaoa_qc_mixer(self, w, prob, solutions, convert_to_matrix_op): """ QAOA test with a mixer as a parameterized circuit""" seed = 0 aqua_globals.random_seed = seed self.log.debug( 'Testing %s-step QAOA with MaxCut on graph with ' 'a mixer as a parameterized circuit\n%s', prob, w) backend = BasicAer.get_backend('statevector_simulator') optimizer = COBYLA() qubit_op, _ = max_cut.get_operator(w) qubit_op = qubit_op.to_opflow() if convert_to_matrix_op: qubit_op = qubit_op.to_matrix_op() num_qubits = qubit_op.num_qubits mixer = QuantumCircuit(num_qubits) theta = Parameter('θ') mixer.rx(theta, range(num_qubits)) qaoa = QAOA(qubit_op, optimizer, prob, mixer=mixer) quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed) result = qaoa.run(quantum_instance) x = sample_most_likely(result.eigenstate) graph_solution = max_cut.get_graph_solution(x) self.assertIn(''.join([str(int(i)) for i in graph_solution]), solutions)
def run_IBM(H=None, backend=None, num_samples=100, qaoa_steps=3, variables=None): num_vars = len(list(H.linear)) qubit_op, offset = get_ising_opt_qubitops(H, variables) if backend == None: backend = BasicAer.get_backend('qasm_simulator') quantum_instance = QuantumInstance(backend) spsa = SPSA(max_trials=10) qaoa = QAOA(qubit_op, spsa, qaoa_steps) result = qaoa.run(quantum_instance) circ = qaoa.get_optimal_circuit() q = circ.qregs[0] c = ClassicalRegister(num_vars, 'c') circ.cregs.append(c) circ.measure(q, c) job = execute(circ, backend, shots=num_samples, memory=True) return job.result().get_counts(circ)
def test_qaoa(self, w, prob, m, solutions, convert_to_matrix_op): """ QAOA test """ seed = 0 aqua_globals.random_seed = seed self.log.debug('Testing %s-step QAOA with MaxCut on graph\n%s', prob, w) backend = BasicAer.get_backend('statevector_simulator') optimizer = COBYLA() qubit_op, offset = max_cut.get_operator(w) qubit_op = qubit_op.to_opflow() if convert_to_matrix_op: qubit_op = qubit_op.to_matrix_op() qaoa = QAOA(qubit_op, optimizer, prob, mixer=m) quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed) result = qaoa.run(quantum_instance) x = sample_most_likely(result.eigenstate) graph_solution = max_cut.get_graph_solution(x) self.log.debug('energy: %s', result.eigenvalue.real) self.log.debug('time: %s', result.optimizer_time) self.log.debug('maxcut objective: %s', result.eigenvalue.real + offset) self.log.debug('solution: %s', graph_solution) self.log.debug('solution objective: %s', max_cut.max_cut_value(x, w)) self.assertIn(''.join([str(int(i)) for i in graph_solution]), solutions)
def test_qaoa(self, w, p, m, solutions): self.log.debug('Testing {}-step QAOA with MaxCut on graph\n{}'.format( p, w)) np.random.seed(0) backend = BasicAer.get_backend('statevector_simulator') optimizer = COBYLA() qubitOp, offset = max_cut.get_max_cut_qubitops(w) qaoa = QAOA(qubitOp, optimizer, p, operator_mode='matrix', mixer=m) quantum_instance = QuantumInstance(backend) result = qaoa.run(quantum_instance) x = max_cut.sample_most_likely(result['eigvecs'][0]) graph_solution = max_cut.get_graph_solution(x) self.log.debug('energy: {}'.format(result['energy'])) self.log.debug('time: {}'.format(result['eval_time'])) self.log.debug('maxcut objective: {}'.format(result['energy'] + offset)) self.log.debug('solution: {}'.format(graph_solution)) self.log.debug('solution objective: {}'.format( max_cut.max_cut_value(x, w))) self.assertIn(''.join([str(int(i)) for i in graph_solution]), solutions) if quantum_instance.has_circuit_caching: self.assertLess(quantum_instance._circuit_cache.misses, 3)
def run_simulation(self, backend): seed = int(os.environ.get("SEED", "40598")) n = int(os.environ.get("N", "4")) # # Random 3-regular graph with 12 nodes # graph = nx.random_regular_graph(3, n, seed=seed) for e in graph.edges(): graph[e[0]][e[1]]["weight"] = 1.0 # Compute the weight matrix from the graph w = np.zeros([n, n]) for i in range(n): for j in range(n): temp = graph.get_edge_data(i, j, default=0) if temp != 0: w[i, j] = temp["weight"] # Create an Ising Hamiltonian with docplex. mdl = Model(name="max_cut") mdl.node_vars = mdl.binary_var_list(list(range(n)), name="node") maxcut_func = mdl.sum(w[i, j] * mdl.node_vars[i] * (1 - mdl.node_vars[j]) for i in range(n) for j in range(n)) mdl.maximize(maxcut_func) qubit_op, offset = docplex.get_operator(mdl) aqua_globals.random_seed = seed # Run quantum algorithm QAOA on qasm simulator spsa = SPSA(max_trials=250) qaoa = QAOA(qubit_op, spsa, p=5, max_evals_grouped=4) quantum_instance = QuantumInstance( backend, shots=1024, seed_simulator=seed, seed_transpiler=seed, optimization_level=0, ) result = qaoa.run(quantum_instance) x = sample_most_likely(result["eigvecs"][0]) result["solution"] = max_cut.get_graph_solution(x) result["solution_objective"] = max_cut.max_cut_value(x, w) result["maxcut_objective"] = result["energy"] + offset """ print("energy:", result["energy"]) print("time:", result["eval_time"]) print("max-cut objective:", result["energy"] + offset) print("solution:", max_cut.get_graph_solution(x)) print("solution objective:", max_cut.max_cut_value(x, w)) """ return result
def test_change_operator_size(self): """ QAOA change operator size test """ aqua_globals.random_seed = 0 qubit_op, _ = max_cut.get_operator( np.array([[0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0]])) qaoa = QAOA(qubit_op.to_opflow(), COBYLA(), 1) quantum_instance = QuantumInstance( BasicAer.get_backend('statevector_simulator'), seed_simulator=aqua_globals.random_seed, seed_transpiler=aqua_globals.random_seed) result = qaoa.run(quantum_instance) x = sample_most_likely(result.eigenstate) graph_solution = max_cut.get_graph_solution(x) with self.subTest(msg='QAOA 4x4'): self.assertIn(''.join([str(int(i)) for i in graph_solution]), {'0101', '1010'}) try: qubit_op, _ = max_cut.get_operator( np.array([ [0, 1, 0, 1, 0, 1], [1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1], [1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1], [1, 0, 1, 0, 1, 0], ])) qaoa.operator = qubit_op.to_opflow() except Exception as ex: # pylint: disable=broad-except self.fail("Failed to change operator. Error: '{}'".format(str(ex))) return result = qaoa.run() x = sample_most_likely(result.eigenstate) graph_solution = max_cut.get_graph_solution(x) with self.subTest(msg='QAOA 6x6'): self.assertIn(''.join([str(int(i)) for i in graph_solution]), {'010101', '101010'})
def test_portfolio_qaoa(self): """ portfolio test with QAOA """ qaoa = QAOA(self.qubit_op, COBYLA(maxiter=500), initial_point=[0., 0.]) backend = BasicAer.get_backend('statevector_simulator') quantum_instance = QuantumInstance(backend=backend, seed_simulator=self.seed, seed_transpiler=self.seed) result = qaoa.run(quantum_instance) selection = sample_most_likely(result.eigenstate) value = portfolio.portfolio_value(selection, self.muu, self.sigma, self.risk, self.budget, self.penalty) np.testing.assert_array_equal(selection, [0, 1, 1, 0]) self.assertAlmostEqual(value, -0.00679917)
def test_qaoa_random_initial_point(self): """ QAOA random initial point """ aqua_globals.random_seed = 10598 w = nx.adjacency_matrix( nx.fast_gnp_random_graph(5, 0.5, seed=aqua_globals.random_seed)).toarray() qubit_op, _ = max_cut.get_operator(w) qaoa = QAOA(qubit_op, NELDER_MEAD(disp=True), 1) quantum_instance = QuantumInstance( BasicAer.get_backend('qasm_simulator'), seed_simulator=aqua_globals.random_seed, seed_transpiler=aqua_globals.random_seed, shots=4096) _ = qaoa.run(quantum_instance) np.testing.assert_almost_equal([2.5179, 0.3528], qaoa.optimal_params, decimal=4)
def test_qaoa_qc_mixer_no_parameters(self): """ QAOA test with a mixer as a parameterized circuit with zero parameters. """ seed = 0 aqua_globals.random_seed = seed qubit_op, _ = max_cut.get_operator(W1) qubit_op = qubit_op.to_opflow() num_qubits = qubit_op.num_qubits mixer = QuantumCircuit(num_qubits) # just arbitrary circuit mixer.rx(np.pi / 2, range(num_qubits)) qaoa = QAOA(qubit_op, optimizer=COBYLA(), p=1, mixer=mixer) backend = BasicAer.get_backend('statevector_simulator') quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed) result = qaoa.run(quantum_instance) # we just assert that we get a result, it is not meaningful. self.assertIsNotNone(result.eigenstate)
def simulate_optimize(n_shots, p_steps, qubitOp, G): backend = BasicAer.get_backend('qasm_simulator') quantum_instance = QuantumInstance(backend, shots=n_shots) qaoa = QAOA(qubitOp, ESCH(max_evals=100), p=p_steps) result = qaoa.run(quantum_instance) # QAOA converts the problem into finding the maximum eigenval/eigenvec pair solution = max_cut.sample_most_likely( result['eigvecs'][0]) #returns vector with highest counts #print('energy:', result['energy']) print('solution:', solution) # For p steps, there should be 2*p parameters (beta,gamma) #print('optimal parameters', result['opt_params']) cost = 0 for i in range(len(G.nodes)): for j in range(len(G.nodes)): cost = cost + adj[i, j] * solution[i] * (1 - solution[j]) print("Sample profit = {}".format(cost)) return result, solution
pos = nx.spring_layout(G) w = my_graphs.adjacency_matrix(G) print("\nAdjacency matrix\n", w, "\n") # setting p p = 1 # ... QAOA ... # Create an Ising Hamiltonian with docplex. mdl = Model(name='max_cut') mdl.node_vars = mdl.binary_var_list(list(range(n)), name='node') maxcut_func = mdl.sum(w[i, j] * mdl.node_vars[i] * (1 - mdl.node_vars[j]) for i in range(n) for j in range(n)) mdl.maximize(maxcut_func) qubit_op, offset = docplex.get_operator(mdl) # Run quantum algorithm QAOA on qasm simulator optimizer = NELDER_MEAD() qaoa = QAOA(qubit_op, optimizer, p=p) backend = Aer.get_backend('qasm_simulator') quantum_instance = QuantumInstance(backend, shots=1000) result = qaoa.run(quantum_instance) x = sample_most_likely(result.eigenstate) print('energy:', result.eigenvalue.real) print('time:', result.optimizer_time, 's') print('max-cut objective:', result.eigenvalue.real + offset) print('solution:', max_cut.get_graph_solution(x)) print('solution objective:', max_cut.max_cut_value(x, w)) print('angles:', result.optimal_point)
def run_experiment( G, p, optimizer, backend, n_shots=100, #important for running time, max is 8192 print_result=False, skip_qobj_validation=False, noise_model=None, coupling_map=None, basis_gates=None, ): '''Runs (Qiskit) QAOA experiment with the given parameters Inputs - G, graph - p, number of angles - optimizer, classical optimizer - backend - number of shots (n_shots) - Note, this parameter is important for running time but also affects accuracy if too low - ... Returns a dictionary with the following keys - energy - time (in seconds) - iterations - max-cut objective - solution - solution objective - eigenstate distribution - angles ''' n = len(G) # number of nodes w = adjacency_matrix(G) # calculating adjacency matrix from graph # ... QAOA ... # Create an Ising Hamiltonian with docplex. mdl = Model(name='max_cut') mdl.node_vars = mdl.binary_var_list(list(range(n)), name='node') maxcut_func = mdl.sum(w[i, j] * mdl.node_vars[i] * (1 - mdl.node_vars[j]) for i in range(n) for j in range(n)) mdl.maximize(maxcut_func) qubit_op, offset = docplex.get_operator(mdl) # Construct a circuit from the model qaoa = QAOA(qubit_op, optimizer, p=p) quantum_instance = QuantumInstance( backend, shots=n_shots, skip_qobj_validation=skip_qobj_validation, coupling_map=coupling_map, basis_gates=basis_gates, noise_model=noise_model) # Run quantum algorithm QAOA on the backend result = qaoa.run(quantum_instance) x = sample_most_likely(result.eigenstate) # Results energy = result.eigenvalue.real time = result.optimizer_time iterations = result.optimizer_evals objective = result.eigenvalue.real + offset # why offset? solution = max_cut.get_graph_solution(x) solution_objective = max_cut.max_cut_value(x, w) distribution = result.eigenstate angles = result.optimal_point if print_result: print('energy:', energy) print('time:', time, 's') print('max-cut objective:', objective) print('solution:', solution) print('solution objective:', solution_objective) print('angles:', angles) return { 'energy': energy, 'time': time, 'iterations': iterations, 'max-cut objective': objective, 'solution': solution, 'solution objective': solution_objective, 'distribution': distribution, 'angles': angles, 'n_shots': n_shots }
def test_readme_sample(self): """ readme sample test """ # pylint: disable=import-outside-toplevel,redefined-builtin def print(*args): """ overloads print to log values """ if args: self.log.debug(args[0], *args[1:]) # --- Exact copy of sample code ---------------------------------------- import networkx as nx import numpy as np from docplex.mp.model import Model from qiskit import BasicAer from qiskit.aqua import aqua_globals, QuantumInstance from qiskit.aqua.algorithms import QAOA from qiskit.aqua.components.optimizers import SPSA from qiskit.optimization.applications.ising import docplex, max_cut from qiskit.optimization.applications.ising.common import sample_most_likely # Generate a graph of 4 nodes n = 4 graph = nx.Graph() graph.add_nodes_from(np.arange(0, n, 1)) elist = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)] graph.add_weighted_edges_from(elist) # Compute the weight matrix from the graph w = np.zeros([n, n]) for i in range(n): for j in range(n): temp = graph.get_edge_data(i, j, default=0) if temp != 0: w[i, j] = temp['weight'] # Create an Ising Hamiltonian with docplex. mdl = Model(name='max_cut') mdl.node_vars = mdl.binary_var_list(list(range(n)), name='node') maxcut_func = mdl.sum(w[i, j] * mdl.node_vars[i] * (1 - mdl.node_vars[j]) for i in range(n) for j in range(n)) mdl.maximize(maxcut_func) qubit_op, offset = docplex.get_operator(mdl) # Run quantum algorithm QAOA on qasm simulator seed = 40598 aqua_globals.random_seed = seed spsa = SPSA(max_trials=250) qaoa = QAOA(qubit_op, spsa, p=5) backend = BasicAer.get_backend('qasm_simulator') quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed, seed_transpiler=seed) result = qaoa.run(quantum_instance) x = sample_most_likely(result.eigenstate) print('energy:', result.eigenvalue.real) print('time:', result.optimizer_time) print('max-cut objective:', result.eigenvalue.real + offset) print('solution:', max_cut.get_graph_solution(x)) print('solution objective:', max_cut.max_cut_value(x, w)) # ---------------------------------------------------------------------- self.assertListEqual( max_cut.get_graph_solution(x).tolist(), [1, 0, 1, 0]) self.assertAlmostEqual(max_cut.max_cut_value(x, w), 4.0)
# (("GBP", "EUR"), 1.), # (("EUR", "GBP"), 1.3), # no arb # (("EUR", "GBP"), 0.88), # (("GBP", "EUR"), 1.13), # (("EUR", "CAD"), 1.47), # (("CAD", "EUR"), 0.68), # (("GBP", "CAD"), 1.65), # (("CAD", "GBP"), 0.6), # GBP -> EUR -> CAD -> GBP makes you money. The other cycles do not. (("EUR", "GBP"), 0.88), (("GBP", "EUR"), 1.13), (("EUR", "CAD"), 1.58), (("CAD", "EUR"), 0.61), (("GBP", "CAD"), 1.65), (("CAD", "GBP"), 0.6), )) op = get_cost_hamiltonian(rates, 1, 1) #%% ee = ExactEigensolver(op) result = ee.run() print(bin(result['eigvecs'][0].argmax())) #%% p = 1 optimizer = COBYLA() qaoa = QAOA(op, optimizer, p) backend = BasicAer.get_backend('statevector_simulator') quantum_instance = QuantumInstance(backend) r2 = qaoa.run(quantum_instance) print(bin(np.absolute(r2['eigvecs'][0]).argmax()))
def libraryoptimize(qubo, edges, cedges, n, p, plotoptangles=False, modulo=False): if (qubo == "qubo1"): H = makeNPMatrix1(edges, cedges, n) if (qubo == "qubo2"): H = makeNPMatrix2(edges, cedges, n) if (qubo == "qubo3"): H = makeNPMatrix2(edges, cedges, n) optimizer = COBYLA() qaoa_mes = QAOA(H, p=p, optimizer=optimizer, quantum_instance=Aer.get_backend('statevector_simulator')) results = qaoa_mes.run() qc1 = qaoa_mes.get_optimal_circuit() print(type(results.optimal_parameters)) print(type(results.optimal_parameters.items())) i = 0 for key, value in results.optimal_parameters.items(): if (i == 0): gamma = value i += 1 else: beta = value print("beta", beta) print("gamma", gamma) print('optimal params: ', results.optimal_parameters) print('optimal value: ', results.optimal_value) qcl = qaoa_mes.get_optimal_circuit() optgammasbetas = [] isgamma = 0 for key, value in results.optimal_parameters.items(): if (modulo): if (isgamma < p): optgammasbetas.append(value % (2 * math.pi)) isgamma += 1 else: optgammasbetas.append(value % (math.pi)) else: optgammasbetas.append(value) if (plotoptangles): plottheoptangles(optgammasbetas, p) if (qubo == "qubo1"): qc = makeCircuit1(n, edges, cedges, optgammasbetas, p) if (qubo == "qubo2"): qc = makeCircuit2(n, edges, cedges, optgammasbetas, p) if (qubo == "qubo3"): qc = makeCircuit3(n, edges, cedges, optgammasbetas, p) ans = ClassicalRegister(n) sol = QuantumRegister(n) QAOA2 = QuantumCircuit(sol, ans) QAOA2.append(qc1, range(n)) return qc, optgammasbetas