I have run this code on local computer, collab, and Kaggle and everywhere the kernel does on running below line sof code. Could anyone help where and how can I run this code:

epsilon = 0.01
alpha = 0.05

problem = EstimationProblem(
    state_preparation=state_preparation,
    objective_qubits=[len(qr_state)],
    post_processing=objective.post_processing,
)
# construct amplitude estimation
ae = IterativeAmplitudeEstimation(
    epsilon_target=epsilon, alpha=alpha, sampler=Sampler(run_options={"shots": 10})
)
result = ae.estimate(problem)

link: .ipynb

I have run this code on local computer, collab, and Kaggle and everywhere the kernel does on running below line sof code. Could anyone help where and how can I run this code:

epsilon = 0.01
alpha = 0.05

problem = EstimationProblem(
    state_preparation=state_preparation,
    objective_qubits=[len(qr_state)],
    post_processing=objective.post_processing,
)
# construct amplitude estimation
ae = IterativeAmplitudeEstimation(
    epsilon_target=epsilon, alpha=alpha, sampler=Sampler(run_options={"shots": 10})
)
result = ae.estimate(problem)

link: https://github/qiskit-community/qiskit-finance/blob/main/docs/tutorials/09_credit_risk_analysis.ipynb

• qiskit

1 Answer 1

We need to define a GroverOperator:

from qiskit.circuit.library import GroverOperator
grover_op = GroverOperator(state_preparation, insert_barriers=True)

and input the grover_operator into EstimationProblem:

problem = EstimationProblem(
    state_preparation=state_preparation,
    objective_qubits=[len(qr_state)],
    grover_operator=grover_op,
    post_processing=objective.post_processing,
)

Outputs (I'm running the entire GitHub linked code up to your issue):

Expected Loss E[L]:                0.6289
Value at Risk VaR[L]:              2.0000
P[L <= VaR[L]]:                    0.9590
Conditional Value at Risk CVaR[L]: 3.0000

Exact Expected Loss:   0.6289
Exact Operator Value:  0.3711
Mapped Operator value: 0.5152
Exact value:        0.6289
Estimated value:    0.0619
Confidence interval:    [0.0410, 0.0829]

Full Code To Replicate:

import numpy as np
from qiskit import QuantumRegister, QuantumCircuit
from qiskit.circuit.library import IntegerComparator
from qiskit_algorithms import IterativeAmplitudeEstimation, EstimationProblem, IterativeAmplitudeEstimationResult
from qiskit_aer.primitives import Sampler

n_z = 2
z_max = 2
z_values = np.linspace(-z_max, z_max, 2**n_z)
p_zeros = [0.15, 0.25]
rhos = [0.1, 0.05]
lgd = [1, 2]
K = len(p_zeros)
alpha = 0.05

from qiskit_finance.circuit.library import GaussianConditionalIndependenceModel as GCI
u = GCI(n_z, z_max, p_zeros, rhos)
u.draw()

u_measure = u.measure_all(inplace=False)
sampler = Sampler()
job = sampler.run(u_measure)
binary_probabilities = job.result().quasi_dists[0].binary_probabilities()

p_z = np.zeros(2**n_z)
p_default = np.zeros(K)
values = []
probabilities = []
num_qubits = u.num_qubits

for i, prob in binary_probabilities.items():
    # extract value of Z and corresponding probability
    i_normal = int(i[-n_z:], 2)
    p_z[i_normal] += prob

    # determine overall default probability for k
    loss = 0
    for k in range(K):
        if i[K - k - 1] == "1":
            p_default[k] += prob
            loss += lgd[k]

    values += [loss]
    probabilities += [prob]


values = np.array(values)
probabilities = np.array(probabilities)

expected_loss = np.dot(values, probabilities)
losses = np.sort(np.unique(values))
pdf = np.zeros(len(losses))
for i, v in enumerate(losses):
    pdf[i] += sum(probabilities[values == v])
cdf = np.cumsum(pdf)

i_var = np.argmax(cdf >= 1 - alpha)
exact_var = losses[i_var]
exact_cvar = np.dot(pdf[(i_var + 1) :], losses[(i_var + 1) :]) / sum(pdf[(i_var + 1) :])

print("Expected Loss E[L]:                %.4f" % expected_loss)
print("Value at Risk VaR[L]:              %.4f" % exact_var)
print("P[L <= VaR[L]]:                    %.4f" % cdf[exact_var])
print("Conditional Value at Risk CVaR[L]: %.4f" % exact_cvar)

from matplotlib import pyplot as plt
plt.bar(losses, pdf)
plt.axvline(expected_loss, color="green", linestyle="--", label="E[L]")
plt.axvline(exact_var, color="orange", linestyle="--", label="VaR(L)")
plt.axvline(exact_cvar, color="red", linestyle="--", label="CVaR(L)")
plt.legend(fontsize=15)
plt.xlabel("Loss L ($)", size=15)
plt.ylabel("probability (%)", size=15)
plt.title("Loss Distribution", size=20)
plt.xticks(size=15)
plt.yticks(size=15)
plt.show()

from qiskit.circuit.library import WeightedAdder
agg = WeightedAdder(n_z + K, [0] * n_z + lgd)

from qiskit.circuit.library import LinearAmplitudeFunction
# define linear objective function
breakpoints = [0]
slopes = [1]
offsets = [0]
f_min = 0
f_max = sum(lgd)
c_approx = 0.25

objective = LinearAmplitudeFunction(
    agg.num_sum_qubits,
    slope=slopes,
    offset=offsets,
    # max value that can be reached by the qubit register (will not always be reached)
    domain=(0, 2**agg.num_sum_qubits - 1),
    image=(f_min, f_max),
    rescaling_factor=c_approx,
    breakpoints=breakpoints,
)

qr_state = QuantumRegister(u.num_qubits, "state")
qr_sum = QuantumRegister(agg.num_sum_qubits, "sum")
qr_carry = QuantumRegister(agg.num_carry_qubits, "carry")
qr_obj = QuantumRegister(1, "objective")

# define the circuit
state_preparation = QuantumCircuit(qr_state, qr_obj, qr_sum, qr_carry, name="A")

# load the random variable
state_preparation.append(u.to_gate(), qr_state)

# aggregate
state_preparation.append(agg.to_gate(), qr_state[:] + qr_sum[:] + qr_carry[:])

# linear objective function
state_preparation.append(objective.to_gate(), qr_sum[:] + qr_obj[:])

# uncompute aggregation
state_preparation.append(agg.to_gate().inverse(), qr_state[:] + qr_sum[:] + qr_carry[:])

# draw the circuit
state_preparation.draw()

state_preparation_measure = state_preparation.measure_all(inplace=False)
sampler = Sampler()
job = sampler.run(state_preparation_measure)
binary_probabilities = job.result().quasi_dists[0].binary_probabilities()

value = 0
for i, prob in binary_probabilities.items():
    if prob > 1e-6 and i[-(len(qr_state) + 1) :][0] == "1":
        value += prob

print("Exact Expected Loss:   %.4f" % expected_loss)
print("Exact Operator Value:  %.4f" % value)
print("Mapped Operator value: %.4f" % objective.post_processing(value))

epsilon = 0.01
alpha = 0.05


from qiskit.circuit.library import GroverOperator
grover_op = GroverOperator(state_preparation, insert_barriers=True)
problem = EstimationProblem(
    state_preparation=state_preparation,
    objective_qubits=[len(qr_state)],
    grover_operator=grover_op,
    post_processing=objective.post_processing,
)
# construct amplitude estimation
ae = IterativeAmplitudeEstimation(
    epsilon_target=epsilon, alpha=alpha, sampler=Sampler(run_options={"shots": 100, "seed": 75})
)

result = ae.estimate(problem)

conf_int = np.array(result.confidence_interval_processed)
print("Exact value:    \t%.4f" % expected_loss)
print("Estimated value:\t%.4f" % result.estimation_processed)
print("Confidence interval: \t[%.4f, %.4f]" % tuple(conf_int))