def qft():
print('start')
state = qcgpu.State(24)
num_qubits = state.num_qubits
for j in range(num_qubits):
for k in range(j):
state.cu1(j, k, math.pi / float(2**(j - k)))
state.h(j)
def bench_qcgpu(num_qubits):
start = time.time()
state = qcgpu.State(num_qubits)
for j in range(num_qubits):
for k in range(j):
state.cu1(j, k, math.pi/float(2**(j-k)))
state.h(j)
state.backend.queue.finish()
return time.time() - start
def bell_state():
import qcgpu
print("Creating Bell State")
state = qcgpu.State(2)
state.h(0)
state.cx(0, 1)
print("Measurement Results:")
print(state.measure(samples=1000))
def bench_qcgpu(n, depth):
h = qcgpu.gate.h()
x = qcgpu.gate.x()
t = qcgpu.gate.t()
state = qcgpu.State(n)
start = time.time()
for level in range(depth):
for q in range(n):
state.apply_gate(h, q)
state.apply_gate(t, q)
if q != 0:
state.apply_controlled_gate(x, q, 0)
return time.time() - start
def bernstein_vazirani():
import qcgpu
num_qubits = 7 # The number of qubits to use
a = 101 # The hidden integer, bitstring is 1100101
register = qcgpu.State(num_qubits) # Create a new quantum register
register.apply_all(qcgpu.gate.h()) # Apply a hadamard gate to each qubit
# Apply the inner products oracle
for i in range(num_qubits):
if a & (1 << i) != 0:
register.z(i)
register.apply_all(qcgpu.gate.h()) # Apply a hadamard gate to each qubit
results = register.measure(
samples=1000) # Measure the register (sample 1000 times)
print(results)
def bench_qcgpu(n, depth):
state = qcgpu.State(n)
h = qcgpu.gate.h()
x = qcgpu.gate.x()
sqrt_x = qcgpu.gate.sqrt_x()
start = time.time()
for level in range(depth):
for q in range(n):
state.apply_gate(h, q)
state.apply_gate(sqrt_x, q)
if q != 0:
state.apply_controlled_gate(x, q, 0)
runtime = time.time() - start
return {'name': 'qcgpu', 'num_qubits': n, 'depth': depth, 'time': runtime}
def bench(num_qubits, depth):
start = time.time()
state = qcgpu.State(num_qubits)
rand_circuit(num_qubits, depth, state)
state.backend.queue.finish()
return time.time() - start
import qcgpu
# 3 qubits, f(x) = x_0 NOT x_1 x_2
# Balanced
balanced_state = qcgpu.State(3)
balanced_state.apply_all(qcgpu.gate.h())
# Oracle U_f
balanced_state.h(2)
balanced_state.z(0)
balanced_state.cx(1, 2)
balanced_state.h(2)
balanced_state.apply_all(qcgpu.gate.h())
outcomes = balanced_state.measure(samples=1000)
if int(max(outcomes, key=outcomes.get)) == 0:
print('constant')
else:
print('balanced')
# 3 qubits, f(x) = 0
# Constant
constant_state = qcgpu.State(3)
constant_state.apply_all(qcgpu.gate.h())
# Oracle is equivalent to the identity gate,
# thus has no effect on the state
import qcgpu import time s = qcgpu.State(28) h = qcgpu.gate.h() s.apply_all(h) print(s.measure(1000)) # print(s.backend.measure(samples=10000))
def run_experiment(self, experiment):
"""Run an experiment (circuit) and return a single experiment result.
Args:
experiment (QobjExperiment): experiment from qobj experiments list
Returns:
dict: A dictionary of results.
dict: A result dictionary
Raises:
QCGPUSimulatorError: If the number of qubits is too large, or another
error occurs during execution.
"""
self._number_of_qubits = experiment.header.n_qubits
self._statevector = 0
start = time.time()
try:
sim = qcgpu.State(self._number_of_qubits)
except OverflowError:
raise QCGPUSimulatorError('too many qubits')
for operation in experiment.instructions:
name = operation.name
qubits = operation.qubits
params = [
float(param) for param in getattr(operation, 'params', [])
]
if name == 'id':
logger.info('Identity gates are ignored.')
elif name == 'barrier':
logger.info('Barrier gates are ignored.')
elif name == 'u3':
sim.u(qubits[0], *params)
elif name == 'u2':
sim.u2(qubits[0], *params)
elif name == 'u1':
sim.u1(qubits[0], *params)
elif name == 'cx':
sim.cx(*qubits)
elif name == 'h':
sim.h(qubits[0])
elif name == 'x':
sim.x(qubits[0])
elif name == 'y':
sim.y(qubits[0])
elif name == 'z':
sim.z(qubits[0])
elif name == 's':
sim.s(qubits[0])
elif name == 't':
sim.t(qubits[0])
amps = [
complex(z) for z in sim.amplitudes().round(self._chop_threshold)
]
end = time.time()
# amps = np.stack((amps.real, amps.imag), axis=-1)
return ExperimentResult(shots=1,
success=True,
data=ExperimentResultData(statevector=amps),
time_taken=(end - start))
def run_experiment(self, experiment):
"""Run an experiment (circuit) and return a single experiment result.
Args:
experiment (QobjExperiment): experiment from qobj experiments list
Returns:
dict: A dictionary of results.
dict: A result dictionary
Raises:
QCGPUSimulatorError: If the number of qubits is too large, or another
error occurs during execution.
"""
self._number_of_qubits = experiment.header.n_qubits
self._number_of_cbits = experiment.header.memory_slots
self._classical_state = 0
self._statevector = 0
if hasattr(experiment.config, 'seed'):
seed = experiment.config.seed
elif hasattr(self._qobj_config, 'seed'):
seed = self._qobj_config.seed
else:
# For compatibility on Windows force dyte to be int32
# and set the maximum value to be (2 ** 31) - 1
seed = np.random.randint(2147483647, dtype='int32')
self._local_random.seed(seed)
self._can_sample(experiment)
if not self._sample_measure:
raise QCGPUSimulatorError(
'Measurements are only supported at the end')
experiment = experiment.to_dict()
# qcgpu.backend.create_context()
samples = []
start = time.time()
try:
sim = qcgpu.State(self._number_of_qubits)
except OverflowError:
raise QCGPUSimulatorError('too many qubits')
for operation in experiment['instructions']:
params = operation.get('params', [])
name = operation['name']
if name == 'id':
logger.info('Identity gates are ignored.')
elif name == 'barrier':
logger.info('Barrier gates are ignored.')
elif name == 'u3':
sim.u(operation['qubits'][0], *params)
elif name == 'u2':
sim.u2(operation['qubits'][0], *params)
elif name == 'u1':
sim.u1(operation['qubits'][0], *params)
elif name == 'cx':
sim.cx(*operation['qubits'])
elif name == 'h':
sim.h(operation['qubits'][0])
elif name == 'x':
sim.x(operation['qubits'][0])
elif name == 'y':
sim.y(operation['qubits'][0])
elif name == 'z':
sim.z(operation['qubits'][0])
elif name == 's':
sim.s(operation['qubits'][0])
elif name == 't':
sim.t(operation['qubits'][0])
elif name == 'measure':
samples.append(
(operation['qubits'][0], operation['memory'][0]))
if self._number_of_cbits > 0:
memory = self._add_sample_measure(samples, sim, self._shots)
else:
memory = []
end = time.time()
# amps = sim.amplitudes().round(self._chop_threshold)
# amps = np.stack((amps.real, amps.imag), axis=-1)
data = {'counts': dict(Counter(memory))}
if self._memory:
data['memory'] = memory
return {
'name': experiment['header']['name'],
'shots': self._shots,
'data': data,
'seed': seed,
'status': 'DONE',
'success': True,
'time_taken': (end - start),
'header': experiment['header']
}
def run_experiment(self, experiment):
"""Run an experiment (circuit) and return a single experiment result.
Args:
experiment (QobjExperiment): experiment from qobj experiments list
Returns:
dict: A dictionary of results.
dict: A result dictionary
Raises:
QCGPUSimulatorError: If the number of qubits is too large, or another
error occurs during execution.
"""
self._number_of_qubits = experiment.header.n_qubits
self._statevector = 0
experiment = experiment.as_dict()
start = time.time()
try:
sim = qcgpu.State(self._number_of_qubits)
except OverflowError:
raise QCGPUSimulatorError('too many qubits')
for operation in experiment['instructions']:
params = operation['params']
name = operation['name']
if name == 'id':
logger.info('Identity gates are ignored.')
elif name == 'barrier':
logger.info('Barrier gates are ignored.')
elif name == 'u3':
sim.u(operation['qubits'][0], *params)
elif name == 'u2':
sim.u2(operation['qubits'][0], *params)
elif name == 'u1':
sim.u1(operation['qubits'][0], *params)
elif name == 'cx':
sim.cx(*operation['qubits'])
elif name == 'h':
sim.h(operation['qubits'][0])
elif name == 'x':
sim.x(operation['qubits'][0])
elif name == 'y':
sim.y(operation['qubits'][0])
elif name == 'z':
sim.z(operation['qubits'][0])
elif name == 's':
sim.s(operation['qubits'][0])
elif name == 't':
sim.t(operation['qubits'][0])
end = time.time()
amps = sim.amplitudes().round(self._chop_threshold)
amps = np.stack((amps.real, amps.imag), axis=-1)
return {
'name': experiment['header']['name'],
'shots': 1,
'data': {
'statevector': amps
},
'status': 'DONE',
'success': True,
'time_taken': (end - start),
'header': experiment['header']
}