def estimate_gradient(f_h, precision, n_measurements=50, cxn=False):
""" Estimate the gradient from point of evaluation
to point of perturbation, h
:param float f_h: Oracle output at perturbation h.
:param int precision: Bit precision of gradient.
:param Connection cxn: connection to the QPU or QVM
:param int n_measurements: Number of times to measure system.
"""
# enumerate input and ancilla qubits
input_qubits = list(range(precision))
ancilla_qubit = precision
# generate gradient program
perturbation_sign = np.sign(f_h)
p_gradient = gradient_estimator(abs(f_h), ancilla_qubit)
# run gradient program
if not cxn:
from pyquil.api import SyncConnection
cxn = SyncConnection()
measurements = cxn.run(p_gradient, input_qubits, n_measurements)
# summarize measurements
bf_estimate = perturbation_sign * measurements_to_bf(measurements)
bf_explicit = '{0:.16f}'.format(bf_estimate)
deci_estimate = binary_to_real(bf_explicit)
return deci_estimate
from pyquil.api import SyncConnection
from pyquil.api import QVMConnection
from pyquil.gates import *
p = Program()
p.inst(H(0))
p.inst(CNOT(0, 1))
qvm = SyncConnection()
result = qvm.wavefunction(p)
print('result', result)
qvm = QVMConnection()
p = Program()
p.inst(H(0), CNOT(0, 1), MEASURE(0, 0), MEASURE(1, 1))
print(p)
x = qvm.run(p, [0, 1], 10)
print(x)
import numpy as np
from pyquil.quil import Program
from pyquil.api import QVMConnection
quantum_simulator = QVMConnection()
# pyQuil is based around operations (or gates) so we will start with the most
# basic one: the identity operation, called I. I takes one argument, the index
# of the qubit that it should be applied to.
from pyquil.gates import I
p = Program(I(0))
# Quantum states are called wavefunctions for historical reasons.
# We can run this basic program on our connection to the simulator.
# This call will return the state of our qubits after we run program p.