techwithshadab

f, ax = plt.subplots(figsize=(20, 8))
optimized_value = sns.barplot(state, probabilities)
for item in optimized_value.get_xticklabels():
    item.set_rotation(45)
plt.title("Probabilites of Each Possible Combination of Assets", fontsize=25) # Probability of Max Return at given risk level
plt.xlabel('Possible Combinations of Assets',fontsize =20)
plt.ylabel('Probability',fontsize = 20)
plt.show()

techwithshadab

%time
state, values, probabilities = qaoa(qubitOp) # ['FB.O', 'AAPL.O', 'AMZN.O', 'NFLX.O', 'GOOGL.O']

techwithshadab

def qaoa(qubitOp):
    backend = Aer.get_backend('statevector_simulator') # You can switch to different backends by providing the name of backend.
    seed = 50
    cobyla = COBYLA()
    cobyla.set_options(maxiter=1000)
    qaoa = QAOA(qubitOp, cobyla, 3)
    qaoa.random_seed = seed
    quantum_instance = QuantumInstance(backend=backend, seed_simulator=seed, seed_transpiler=seed)
    results= qaoa.run(quantum_instance)
    selection, state, values, probabilities = print_result(results)

techwithshadab

f, ax = plt.subplots(figsize=(20, 8))
optimized_value = sns.barplot(state, probabilities)
for item in optimized_value.get_xticklabels():
    item.set_rotation(45)
plt.title("Probabilites of Each Possible Combination of Assets", fontsize=25) # Probability of Max Return at given risk level
plt.xlabel('Possible Combinations of Assets',fontsize =20)
plt.ylabel('Probability',fontsize = 20)
plt.show()

techwithshadab

%time
state, values, probabilities = vqe(qubitOp) # ['FB.O', 'AAPL.O', 'AMZN.O', 'NFLX.O', 'GOOGL.O']

techwithshadab

def vqe(qubitOp):
    backend = Aer.get_backend('statevector_simulator') # You can switch to different backends by providing the name of backend.
    seed = 50
    cobyla = COBYLA()
    cobyla.set_options(maxiter=1000)
    ry = TwoLocal(qubitOp.num_qubits, 'ry', 'cz', reps=3, entanglement='full')
    vqe = VQE(qubitOp, ry, cobyla)
    vqe.random_seed = seed
    quantum_instance = QuantumInstance(backend=backend, seed_simulator=seed, seed_transpiler=seed)
    result = vqe.run(quantum_instance)

techwithshadab

f, ax = plt.subplots(figsize=(20, 8))
optimized_value = sns.barplot(state, probabilities)
for item in optimized_value.get_xticklabels():
    item.set_rotation(45)
plt.title("Probabilites of Each Possible Combination of Assets", fontsize=25) # Probability of Max Return at given risk level
plt.xlabel('Possible Combinations of Assets',fontsize =20)
plt.ylabel('Probability',fontsize = 20)
plt.show()

techwithshadab

%time
state, values, probabilities = numpyEigensolver(qubitOp) # ['FB.O', 'AAPL.O', 'AMZN.O', 'NFLX.O', 'GOOGL.O']

techwithshadab

def numpyEigensolver(qubitOp):
    selections = []
    exact_eigensolver = NumPyMinimumEigensolver(qubitOp)
    result = exact_eigensolver.run()
    selection, state, values, probabilities = print_result(result)
    print(selection_to_picks(num_assets, selection))
    return state, values, probabilities

techwithshadab

def print_result(result):
    selection = sample_most_likely(result.eigenstate)
    value = portfolio.portfolio_value(selection, mu, sigma, risk_factor, budget, penalty)
    print('Optimal: selection {}, value {:.4f}'.format(selection, value))
    eigenvector = result.eigenstate if isinstance(result.eigenstate, np.ndarray) else result.eigenstate.to_matrix()
    probabilities = np.abs(eigenvector)**2
    i_sorted = reversed(np.argsort(probabilities))
    print('\n----------------- Full result ---------------------')
    print('selection\tvalue\t\tprobability')
    print('---------------------------------------------------')