# Setting up the backend
print("(Aer Backends)")
for backend in BasicAer.backends():
print(backend.status())
qasm_simulator = BasicAer.get_backend('qasm_simulator')
# Compile and run the circuit on a real device backend
# See a list of available remote backends
print("\n(IBMQ Backends)")
for backend in provider.backends():
print(backend.status())
try:
# select least busy available device and execute.
least_busy_device = least_busy(provider.backends(simulator=False))
except:
print("All devices are currently unavailable.")
print("Running on current least busy device: ", least_busy_device)
# Transpile the circuits to make them compatible with the experimental backend
[qc1_new, qc2_new] = transpile(circuits=[qc1, qc2], backend=least_busy_device)
print("Bell circuit before transpile:")
print(qc1)
print("Bell circuit after transpile:")
print(qc1_new)
print("Superposition circuit before transpile:")
print(qc2)
print("Superposition circuit after transpile:")
def test_retrieve_job_error(self, provider):
"""Test retrieving an invalid job."""
backends = provider.backends(simulator=False)
backend = least_busy(backends)
self.assertRaises(IBMQBackendError, backend.retrieve_job, 'BAD_JOB_ID')
# running the job
job_sim = execute([qc1, qc2], BasicAer.get_backend('qasm_simulator'))
sim_result = job_sim.result()
# Show the results
print(sim_result.get_counts(qc1))
print(sim_result.get_counts(qc2))
# see a list of available remote backends
print("\n(IBMQ Backends)")
print(IBMQ.backends())
# Compile and run on a real device backend
try:
# select least busy available device and execute.
least_busy_device = least_busy(IBMQ.backends(simulator=False))
print("Running on current least busy device: ", least_busy_device)
# running the job
job_exp = execute([qc1, qc2],
backend=least_busy_device,
shots=1024,
max_credits=10)
job_monitor(job_exp)
exp_result = job_exp.result()
# Show the results
print(exp_result.get_counts(qc1))
print(exp_result.get_counts(qc2))
except:
def test_filter_least_busy(self, qe_token, qe_url):
"""Test filtering by least busy function"""
IBMQ.enable_account(qe_token, qe_url)
backends = IBMQ.backends()
filtered_backends = least_busy(backends)
self.assertTrue(filtered_backends)
simulator = Aer.get_backend('qasm_simulator')
# Execute and get counts
result = execute(circ, simulator,
shots=simulator.configuration().max_shots).result()
counts = result.get_counts(circ)
display(
plot_histogram(counts,
title='Simulated counts for ' + str(n_gates) +
' SWAP gates.'))
print("Simulated SWAP counts:", counts)
input("Press enter to run on an IBM Q backend...\n")
# Import the least busy backend
from qiskit.providers.ibmq import least_busy
backend = least_busy(provider.backends(operational=True, simulator=False))
print("Least busy backend:", backend)
# Execute and get counts
job = execute(circ, backend, shots=backend.configuration().max_shots)
job_monitor(job)
nisq_result = job.result()
nisq_counts = nisq_result.get_counts(circ)
print("NISQ SWAP counts:", nisq_counts)
display(
plot_histogram(nisq_counts,
title='Counts for ' + str(n_gates) + ' SWAP gates on ' +
str(backend)))
input("Press enter to transpile the circuit...\n")
# Comparing the circuit with the transpiled circuit
circuit.h(1)
circuit.cx(1,2)
circuit.barrier()
circuit.cx(0,1)
circuit.h(0)
circuit.barrier()
circuit.cx(1, 2)
circuit.cz(0, 2)
circuit.barrier()
circuit.measure([0, 1, 2], [0, 1, 2])
print(circuit)
backend1 = Aer.get_backend('qasm_simulator')
job1 = execute(circuit, backend=backend1, shots=1000)
result1 = job1.result()
measurement1 = result1.get_counts(circuit)
plot_histogram(measurement1)
IBMQ.save_account('bc0221c2c435408a718744a0e3388325cd4241375e15beefb8909a759a28201db7c6a425e8d07e09e54105e373aceccb175fa8eb46700b4db88ac50dbdc6fa84')
provider = IBMQ.load_account()
backend2 = least_busy(provider.backends(filters=lambda b: b.configuration().n_qubits >= 3 and not b.configuration().simulator and b.status().operational==True))
job2 = execute(circuit, backend=backend2, shots=1000)
result2 = job2.result()
measurement2 = result2.get_counts(circuit)
plot_histogram(measurement2)
plt.show()
counts = result_sim.get_counts(qc)
print(counts)
#Run on IBMQ
from qiskit import IBMQ
IBMQ.load_accounts()
print("Available backends:")
IBMQ.backends()
from qiskit.providers.ibmq import least_busy
large_enough_devices = IBMQ.backends(filters=lambda x: x.configuration(
).n_qubits > 4 and not x.configuration().simulator)
backend = least_busy(large_enough_devices)
print("The best backend is " + backend.name())
#To run the circuit on the backend
from qiskit.tools.monitor import job_monitor
shots = 1024 # Number of shots to run the program (experiment); maximum is 8192 shots.
max_credits = 3 # Maximum number of credits to spend on executions.
job_exp = execute(qc, backend=backend, shots=shots, max_credits=max_credits)
job_monitor(job_exp)
result_exp = job_exp.result()
counts_exp = result_exp.get_counts(qc)
def main(run_mode):
# graph of city coordinates
cities = np.array([[0, 0], [0, 1]]) # coordinates of the cities
num_cities = len(cities)
num_qubits = num_cities ** 2
# algorithm properties
p = 2 # number of time steps
beta = np.random.uniform(0, np.pi * 2, p)
gamma = np.random.uniform(0, np.pi * 2, p)
# create matrix of distances between cities
distance_mat = tsp.calc_distance(cities).w # note that this method does integer distances
# create mixing Hamiltonian. A city may or may not be visited in a timestep
mixing_hamiltonian = reduce(lambda x, y: x + y,
[pauli_x(i, 1, num_qubits) for i in range(num_qubits)])
# penalty_operators = create_weights_cost_operators(num_cities=num_cities, num_qubits=num_qubits,
# dist_mat=distance_mat)
penalty_operators = create_penalty_operators_for_bilocation(num_qubits=num_qubits, num_cities=num_cities,
distance_mat=distance_mat)
penalty_operators += create_penalty_operators_for_repetition(num_qubits=num_qubits, num_cities=num_cities,
distance_mat=distance_mat)
print(penalty_operators)
cost_hamiltonian = penalty_operators
# circuit initial state vector. All states in equal superposition
init_state_vect = [1 for i in range(2 ** num_qubits)]
init_state = Custom(num_qubits, state_vector=init_state_vect)
# initialize quantum circuit
qr = QuantumRegister(num_qubits, name='q')
init_circ = init_state.construct_circuit('circuit', qr)
# find optimal beta and gamma
evaluate = partial(neg_evaluate_circuit, qr=qr, p=p, m_H=mixing_hamiltonian, c_H=cost_hamiltonian,
init_circ=init_circ)
print("Looking for optimal beta and gamma")
# TODO: maybe we should use a different or faster method of finding the min? Super long even with two cities
result = minimize(evaluate, np.concatenate([gamma, beta]), method='L-BFGS-B')
# result = minimize(evaluate, np.concatenate([gamma, beta]))
print(result)
# now use the result of the gathered angles to find the answer
circuit = create_circuit(qr, result['x'][:p], result['x'][p:], p, m_H=mixing_hamiltonian, c_H=cost_hamiltonian,
init_circ=init_circ)
if run_mode == "IBM quantum":
import secrets
from qiskit import IBMQ
from qiskit.providers.ibmq import least_busy
provider = IBMQ.enable_account(secrets.IBM_TOKEN)
large_enough_devices = provider.backends(filters=lambda x: x.configuration().n_qubits > 4 and
not x.configuration().simulator)
backend = least_busy(large_enough_devices)
print("This will be running on the IBM device " + backend.name())
else:
print("Preparing to run on local simulator")
backend = BasicAer.get_backend('statevector_simulator')
job = execute(circuit, backend)
state = np.asarray(job.result().get_statevector(circuit))
print(list_to_easier_vis(np.absolute(state)))
qc.barrier()
# Measure all of the qubits in the standard basis
for i in range(num_qubits):
qc.measure(q[i], c[i])
###############################################################
# Set up the API and execute the program.
###############################################################
try:
IBMQ.load_accounts()
except:
print("""WARNING: There's no connection with the API for remote backends.
Have you initialized a file with your personal token?
For now, there's only access to local simulator backends...""")
# First version: simulator
sim_backend = BasicAer.get_backend('qasm_simulator')
job = execute(qc, sim_backend, shots=1024)
result = job.result()
print('Qasm simulator : ')
print(result.get_counts(qc))
# Second version: real device
least_busy_device = least_busy(IBMQ.backends(simulator=False,
filters=lambda x: x.configuration().n_qubits > 4))
print("Running on current least busy device: ", least_busy_device)
job = execute(qc, least_busy_device, shots=1024)
result = job.result()
print('Physical device (%s) : ' % least_busy_device)
print(result.get_counts(qc))
measure_Z.cx(qr[1], qr[3])
# We measure the circuit
measure_Z.measure(qr, cr)
# We add the two circuits to obtain a test result on how many possible
# combinations we have and the probabilities of each combination to be
# exist. These results are mearly theorethical and in a real quantum
# computer, the results may vary.
test_Z = qc + measure_Z
# We run the circuit in the local simulator. For more information on
# the simulators, please visit the Github or read the documentation.
# For more, visit https://qiskit.com
qk.IBMQ.load_accounts(hub=None)
backend = least_busy(
qk.IBMQ.backends(filters=lambda x: not x.configuration().simulator))
qjob_2 = qk.compile(test_Z, backend, shots=1024)
print(qjob_2)
job_2 = backend.run(qjob_2)
print(job_2.status())
result_1 = job_2.result()
print(result_1.get_counts(test_Z))
plot_histogram(result_1.get_counts(test_Z)).savefig("test_Z.png")
# =====================================================================
# Another example where we use the same starting quantum circuit qc
import qiskit
from qiskit import Aer, QuantumRegister, ClassicalRegister, QuantumCircuit, IBMQ
from qiskit.providers import JobStatus
from qiskit.providers.ibmq import least_busy
from qiskit.exceptions import QiskitError
from qiskit.compiler import transpile, assemble
from Scripts.ibmq_account import account_key
IBMQ.enable_account(account_key)
q_device = IBMQ.backends(
filters=lambda x: x.configuration().n_qubits == 5 and x.configuration(
).memory and not x.configuration().simulator)
real_backend = least_busy(q_device)
real_name = real_backend.name()
sim_backend = Aer.get_backend('qasm_simulator')
class CircuitBuilder():
def __init__(self, register_size):
self.size = register_size
self.qr = QuantumRegister(register_size)
self.cr = ClassicalRegister(register_size)
self.circuit = QuantumCircuit(self.qr, self.cr)
def add_operation(self, op, backend):
op_name = op[0]
if op_name == 'h':
self.circuit.h(self.qr[op[1]])
elif op_name == 'x':
self.circuit.x(self.qr[op[1]])
# Apply the quantum gates
circuit.h(q[0])
circuit.cx(q[0], q[1])
# Finish off with the measurements
circuit.measure(q, c)
# Draw the circuit
circuit.draw(output="mpl")
# First, simulate the circuit
simulator = Aer.get_backend('qasm_simulator')
job = execute(circuit, backend=simulator, shots=1024)
result = job.result()
# Then, plot a histogram of the results
counts = result.get_counts(circuit)
plot_histogram(counts)
# Next, find the least-busy IBM device
lb_device = least_busy(IBMQ.backends())
# And run the circuit on that device
job = execute(circuit, backend=lb_device, shots=1024)
job_monitor(job)
result = job.result()
# Finally, plot a histogram of the results
counts = result.get_counts(circuit)
plot_histogram(counts)
# Authenticate for access to remote backends
try:
provider = IBMQ.load_account()
except:
print("""WARNING: No valid IBMQ credentials found on disk.
You must store your credentials using IBMQ.save_account(token, url).
For now, there's only access to local simulator backends...""")
exit(0)
# see a list of available remote backends
ibmq_backends = provider.backends()
print("Remote backends: ", ibmq_backends)
# Compile and run the Quantum Program on a real device backend
# select those with at least 2 qubits
try:
least_busy_device = least_busy(
provider.backends(filters=lambda x: x.configuration().n_qubits >= 2,
simulator=False))
except:
print("All devices are currently unavailable.")
print("Running on current least busy device: ", least_busy_device)
#running the job
job_exp = execute(qc, least_busy_device, shots=1024, max_credits=10)
result_exp = job_exp.result()
# Show the results
print('Counts: ', result_exp.get_counts(qc))
def leastBusy(minQubits, provider):
large_enough_devices = provider.backends(filters=lambda x: x.configuration(
).n_qubits > minQubits and not x.configuration().simulator)
leastBusybackend = least_busy(large_enough_devices)
return leastBusybackend
state = key
print("The Quantum 8-ball says:")
if state == '000':
print('It is certain.')
elif state == '001':
print('Without a doubt.')
elif state == '010':
print('Yes - deinitely.')
elif state == '011':
print('Most likely.')
elif state == '100':
print("Don't count on it.")
elif state == '101':
print('My reply is no.')
elif state == '110':
print('Very doubtful.')
else:
print('Concentrate and ask again.')
# Execute the job on the simulator.
job = execute(qc, backend=Aer.get_backend('qasm_simulator'), shots=1)
result = job.result().get_counts(qc)
answer(result)
# Execute the program on a real quantum computer.
backend = least_busy(IBMQ.backends(simulator=False))
print("Running on", backend.name())
job = execute(qc, backend, shots=1)
result = job.result().get_counts(qc)
answer(result)
for i in range(n):
prog.measure(input_qubit[i], classical[i])
return prog
if __name__ == '__main__':
a = "111"
b = "0"
f = lambda rep: bitwise_xor(bitwise_dot(a, rep), b)
prog = make_circuit(4,f)
IBMQ.load_account()
provider = IBMQ.get_provider(hub='ibm-q')
provider.backends()
backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))
sample_shot =8000
info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()
backend = FakeVigo()
circuit1 = transpile(prog,backend,optimization_level=2)
writefile = open("../data/startQiskit_QC2686.csv","w")
print(info,file=writefile)
print("results end", file=writefile)
print(circuit1.__len__(),file=writefile)
print(circuit1,file=writefile)
writefile.close()
# Loading your IBM Q account(s)
provider = IBMQ.load_account()
from math import pi,sqrt
import numpy as np
from matplotlib import cm
import matplotlib.pyplot as plt
import qiskit
qiskit.__qiskit_version__
# use simulator to learn more about entangled quantum states where possible
sim_backend = BasicAer.get_backend('qasm_simulator')
sim_shots = 8192
# use device to test entanglement
device_shots = 1024
device_backend = least_busy(IBMQ.get_provider().backends(operational=True,simulator=False))
print(the backend is " + device_backend.name())
# Creating registers
q = QuantumRegister(2)
c = ClassicalRegister(2)
# quantum circuit to make an entangled bell state
bell = QuantumCircuit(q, c)
bell.h(q[0])
bell.cx(q[0], q[1])
# quantum circuit to measure q in the Z basis
measureZZ = QuantumCircuit(q, c)
measureZZ.measure(q[0], c[0])
measureZZ.measure(q[1], c[1])
bellZZ = bell+measureZZ