# Make qubits 3 to 5 in a HW=1 state
qudit_start = 3
qudit_end = 5
HW = 1
init_prog = Dicke_state_local(init_prog, qudit_start, qudit_end, HW)
# Make qubits 6 to 11 in a HW=3 state
qudit_start = 6
qudit_end = 11
HW = 3
init_prog = Dicke_state_local(init_prog, qudit_start, qudit_end, HW)
wfn = WavefunctionSimulator().wavefunction(init_prog)
# print states with non-zero probability
for state, prob in wfn.get_outcome_probs().items():
if (prob > 0):
print(state, '%3.2f' % prob)
#print(wfn.get_outcome_probs() )
print(init_prog)
#Test senarios
# test Dicke_state_local(prog, 0, 2, HW) output is the same as Dicke_state(3, HW)
if 0:
qudit_start = 0
qudit_end = 2
HW = 1
init_prog = Dicke_state(3, HW)
wfn = WavefunctionSimulator().wavefunction(init_prog)
print(wfn.get_outcome_probs())
import numpy as np
from grove.pyqaoa.maxcut_qaoa import maxcut_qaoa
from pyquil import get_qc
from pyquil.api import WavefunctionSimulator
from hidden_prints import hidden_prints
from .rigetti_result_analysis import convert_result
graph = [(0, 1), (0, 2), (0, 3)]
qvm = get_qc('4q-qvm')
with hidden_prints():
maxcut_solver = maxcut_qaoa(graph=graph, steps=4) # qaoa steps
betas, gammas = maxcut_solver.get_angles()
angles = np.hstack((betas, gammas))
param_prog = maxcut_solver.get_parameterized_program()
prog = param_prog(angles) # gives the sequence of qubits and gates
measurements = qvm.run_and_measure(prog,
trials=1000) # simulate the program runs
counts = convert_result(measurements)
# plot_state_histogram(counts)
wavefunction = WavefunctionSimulator().wavefunction(
prog) # use wavefunction class to get theoretical measures
prob_dict = wavefunction.get_outcome_probs(
) # extracts the probabilities of outcomes as a dict
print(prob_dict)
prob_dict.keys() # these store the bitstring outcomes