This notebook demonstrates how to use the Qiskit Aqua library to invoke the EOH algorithm and process the result.
Further information may be found for the algorithms in the online Aqua documentation.
For this particular demonstration, we illustrate the EOH algorithm. First, two Operator instances we created are randomly generated Hamiltonians.
In [1]:
import numpy as np
from qiskit_aqua.operator import Operator
num_qubits = 2
temp = np.random.random((2 ** num_qubits, 2 ** num_qubits))
qubitOp = Operator(matrix=temp + temp.T)
temp = np.random.random((2 ** num_qubits, 2 ** num_qubits))
evoOp = Operator(matrix=temp + temp.T)
For EOH, we would like to evolve some initial state (e.g. the uniform superposition state) with evoOp and do a measurement using qubitOp. Below, we illustrate how such an example dynamics process can be easily prepared.
In [2]:
from qiskit_aqua.input import get_input_instance
params = {
'problem': {
'name': 'eoh'
},
'algorithm': {
'name': 'EOH',
'num_time_slices': 1
},
'initial_state': {
'name': 'CUSTOM',
'state': 'uniform'
},
'backend': {
'name': 'statevector_simulator'
}
}
algo_input = get_input_instance('EnergyInput')
algo_input.qubit_op = qubitOp
algo_input.add_aux_op(evoOp)
With all the necessary pieces prepared, we can then proceed to run the algorithm and examine the result.
In [3]:
from qiskit_aqua import run_algorithm
ret = run_algorithm(params, algo_input)
print('The result is\n{}'.format(ret))
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