Segmentation fault when using Aer Sampler

[woodsp-ibm]

Informations

• Qiskit Aer version: 0.15.1 (and earlier versions too like 0.14)
• Python version: Seen on 3.8 and 3.12 in Finance CI and 3.9 locally when reproducing
• Operating system: Linux

What is the current behavior?

Qiskit Finance Notebook tests are failing, and the failing notebook exhibits the same behavior when running locally

Kernel died

Notebook error:
DeadKernelError in tutorials/03_european_call_option_pricing.ipynb:
Kernel died
make: *** [Makefile:71: html] Error 2

Steps to reproduce the problem

Run the following notebook having installed Qiskit Finance
https://github.com/qiskit-community/qiskit-finance/blob/main/docs/tutorials/03_european_call_option_pricing.ipynb

It fails in cell 9 when executing this result = ae.estimate(problem)

I also created this standalone code in a Python file when reproducing the issue locally

import numpy as np

from qiskit import QuantumCircuit
from qiskit_algorithms import IterativeAmplitudeEstimation, EstimationProblem
from qiskit.circuit.library import LinearAmplitudeFunction
from qiskit_aer.primitives import Sampler
from qiskit.primitives import Sampler as RefSampler
from qiskit_finance.circuit.library import LogNormalDistribution

# number of qubits to represent the uncertainty
num_uncertainty_qubits = 3

# parameters for considered random distribution
S = 2.0  # initial spot price
vol = 0.4  # volatility of 40%
r = 0.05  # annual interest rate of 4%
T = 40 / 365  # 40 days to maturity

# resulting parameters for log-normal distribution
mu = (r - 0.5 * vol**2) * T + np.log(S)
sigma = vol * np.sqrt(T)
mean = np.exp(mu + sigma**2 / 2)
variance = (np.exp(sigma**2) - 1) * np.exp(2 * mu + sigma**2)
stddev = np.sqrt(variance)

# lowest and highest value considered for the spot price; in between, an equidistant discretization is considered.
low = np.maximum(0, mean - 3 * stddev)
high = mean + 3 * stddev

# construct A operator for QAE for the payoff function by
# composing the uncertainty model and the objective
uncertainty_model = LogNormalDistribution(
    num_uncertainty_qubits, mu=mu, sigma=sigma**2, bounds=(low, high)
)

# set the strike price (should be within the low and the high value of the uncertainty)
strike_price = 1.896

# set the approximation scaling for the payoff function
c_approx = 0.25

# setup piecewise linear objective fcuntion
breakpoints = [low, strike_price]
slopes = [0, 1]
offsets = [0, 0]
f_min = 0
f_max = high - strike_price
european_call_objective = LinearAmplitudeFunction(
    num_uncertainty_qubits,
    slopes,
    offsets,
    domain=(low, high),
    image=(f_min, f_max),
    breakpoints=breakpoints,
    rescaling_factor=c_approx,
)

# construct A operator for QAE for the payoff function by
# composing the uncertainty model and the objective
num_qubits = european_call_objective.num_qubits
european_call = QuantumCircuit(num_qubits)
european_call.append(uncertainty_model, range(num_uncertainty_qubits))
european_call.append(european_call_objective, range(num_qubits))

# evaluate exact expected value (normalized to the [0, 1] interval)
x = uncertainty_model.values
y = np.maximum(0, x - strike_price)
exact_value = np.dot(uncertainty_model.probabilities, y)
exact_delta = sum(uncertainty_model.probabilities[x >= strike_price])
print("exact expected value:\t%.4f" % exact_value)
print("exact delta value:   \t%.4f" % exact_delta)

# set target precision and confidence level
epsilon = 0.01
alpha = 0.05

problem = EstimationProblem(
    state_preparation=european_call,
    objective_qubits=[3],
    post_processing=european_call_objective.post_processing,
)
# construct amplitude estimation
sampler=Sampler(run_options={"shots": 100, "seed": 75})
#sampler=RefSampler(options={"shots": 100, "seed": 75})  # This works with qiskit sampler
ae = IterativeAmplitudeEstimation(
    epsilon_target=epsilon, alpha=alpha, sampler=sampler
)

print("Computing result...")
result = ae.estimate(problem)

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

Above you will see two lines

sampler=Sampler(run_options={"shots": 100, "seed": 75})
#sampler=RefSampler(options={"shots": 100, "seed": 75})  # This works with qiskit sampler

If you uncomment the 2nd it will instead use the reference Sampler from Qiskit and when using that things work correctly. With the Aer sampler I get this

> python fin_nb_3_fail.py (above code is what is in this file for me)
exact expected value:   0.1623
exact delta value:      0.8098
Computing result...
Segmentation fault (core dumped)

What is the expected behavior?

It should work without segmentation failure as it did in the past

Suggested solutions

[JiayuLiu666]

I have a same problem.