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For the number of iteration, you can do

for i,j in enumerate(result_delta._estimate_intervals):
    print(i,np.mean(j))

For the circuit result you can try to do result.circuit_results, it might return a Statevector or counts dictionary.
and algorithm.construct_circuit(problem) to check the problem circuit.

#recommend way to view the problem gate
from qiskit import transpile
transpile(ae_delta.construct_circuit(problem),basis_gates = ['ry', 'cx','ccx','x']).draw()

For any more related coding questions, you can just do something like dir(result) and see its attribute, then check sources code or docs about it for explanation.

hope it is something you are looking for

For the number of iterations, you can do

for i,j in enumerate(result_delta._estimate_intervals):
    print(i,np.mean(j))

For the circuit result, you can try to do result.circuit_results, it might return a Statevector or counts dictionary.
and algorithm.construct_circuit(problem) to check the problem circuit.

#recommend way to view the problem gate
from qiskit import transpile
transpile(ae_delta.construct_circuit(problem),basis_gates = ['ry', 'cx','ccx','x']).draw()

For any more related coding questions, you can just do something like dir(result) to see its attribute, then check sources code or docs about these attributes for the explanation.

hope it is something you are looking for

For the number of iteration, you can do len(result._estimate_intervals), _estimate_intervals is just "Set the confidence intervals for the estimate in each iteration."
For the circuit result you can try to do result.circuit_results, it might return a Statevector or counts dictionary.
and algorithm.construct_circuit(problem) to check the problem circuit.

#recommend way to view the problem gate
from qiskit import transpile
transpile(ae_delta.construct_circuit(problem),basis_gates = ['ry', 'cx','ccx','x']).draw()

For any more related coding questions, you can just do something like dir(result) and see its attribute, then check sources code or docs about it for explanation.

hope it is something you are looking for

For the number of iteration, you can do

for i,j in enumerate(result_delta._estimate_intervals):
    print(i,np.mean(j))

For the circuit result you can try to do result.circuit_results, it might return a Statevector or counts dictionary.
and algorithm.construct_circuit(problem) to check the problem circuit.

#recommend way to view the problem gate
from qiskit import transpile
transpile(ae_delta.construct_circuit(problem),basis_gates = ['ry', 'cx','ccx','x']).draw()

For any more related coding questions, you can just do something like dir(result) and see its attribute, then check sources code or docs about it for explanation.

hope it is something you are looking for

For the number of iteration, you can do len(result._estimate_intervals), _estimate_intervals is just "Set the confidence intervals for the estimate in each iteration."
For the circuit result you can try to do result.circuit_results, it might return a Statevector or counts dictionary.
and algorithm.construct_circuit(problem) to check the circuit.

For any more related coding questions, you can just do something like dir(result) and see its attribute, then check sources code or docs about it for explanation.

hope it is something you are looking for

For the number of iteration, you can do len(result._estimate_intervals), _estimate_intervals is just "Set the confidence intervals for the estimate in each iteration."
For the circuit result you can try to do result.circuit_results, it might return a Statevector or counts dictionary.
and algorithm.construct_circuit(problem) to check the problem circuit.

#recommend way to view the problem gate
from qiskit import transpile
transpile(ae_delta.construct_circuit(problem),basis_gates = ['ry', 'cx','ccx','x']).draw()

For any more related coding questions, you can just do something like dir(result) and see its attribute, then check sources code or docs about it for explanation.

hope it is something you are looking for