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Project: Testing 18.04
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Kernel: Python 3 (Ubuntu Linux)

QuisKit Terra in Python 3 (Ubuntu Linux)

import sys sys.version
from qiskit import QuantumRegister, ClassicalRegister from qiskit import QuantumCircuit, BasicAer, execute q = QuantumRegister(2) c = ClassicalRegister(2) qc = QuantumCircuit(q, c) qc.h(q[0])[0], q[1]) qc.measure(q, c) backend = BasicAer.get_backend('qasm_simulator') job_sim = execute(qc, backend) sim_result = job_sim.result() print(sim_result.get_counts(qc))
res = sim_result.get_counts(qc) assert res['11'] >= 100 and res['00'] >= 400
import numpy as np from import plot_bloch_vector from qiskit import BasicAer from qiskit_aqua.algorithms import AmplitudeEstimation from import QFactory from qiskit_aqua.components.uncertainty_problems import UncertaintyProblem from qiskit_aqua.utils.circuit_utils import cry
# the probability to be recovered probability = 0.3 theta_p = 2*np.arcsin(np.sqrt(probability)) # the resulting quantum state after A is applied plot_bloch_vector([np.sin(theta_p), 0.0, np.cos(theta_p)])
class BernoulliAFactory(UncertaintyProblem): """ Circuit Factory representing the operator A. A is used to initialize the state as well as to construct Q. """ def __init__(self, probability=0.5, i_state=None): super().__init__(1) self._probability = probability self._theta_p = 2 * np.arcsin(np.sqrt(probability)) if i_state is None: i_state = 0 self._params = {'i_state': i_state} def build(self, qc, q, q_ancillas=None, params=None): if params is None: params = self._params # A is a rotation of angle theta_p around the Y-axis qc.ry(self._theta_p, q[params['i_state']]) class BernoulliQFactory(QFactory): """ Circuit Factory representing the operator Q. This implementation exploits the fact that powers of Q can be implemented efficiently by just multiplying the angle. (amplitude estimation only requires controlled powers of Q, thus, only this method is overridden.) """ def __init__(self, bernoulli_expected_value): super().__init__(bernoulli_expected_value) def build(self, qc, q, q_ancillas=None, params=None): i_state = self.a_factory._params['i_state'] theta_p = self.a_factory._theta_p # Q is a rotation of angle 2*theta_p around the Y-axis qc.ry(q[i_state], 2 * theta_p) def build_controlled_power(self, qc, q, q_control, power, q_ancillas=None, params=None): i_state = self.a_factory._params['i_state'] theta_p = self.a_factory._theta_p cry(2 * power * theta_p, q_control, q[i_state], qc)
# construct factories for A and Q bernoulli_a_factory = BernoulliAFactory(probability) bernoulli_q_factory = BernoulliQFactory(bernoulli_a_factory)
# set number of evaluation qubits m = 3 # construct amplitude estimation # here, we override the standard construction of Q since we know a more efficient way # (exploiting the fact that A and Q are just Y-rotations) ae = AmplitudeEstimation(m, bernoulli_a_factory, q_factory=bernoulli_q_factory)
# result ='qasm_simulator')) result ='statevector_simulator'))
all(_ >= 0 for _ in result['values'])
# plot estimated values import matplotlib.pyplot as plt['values'], result['probabilities'], width=0.5/len(result['probabilities'])) plt.plot([probability, probability], [0,1], 'r--', linewidth=2) plt.xticks(size=15) plt.yticks([0, 0.25, 0.5, 0.75, 1], size=15) plt.title('Estimated Values', size=15) plt.ylabel('Probability', size=15) plt.ylim((0,1)) plt.grid()