def testPowerSpetraSlow(self):
xs = 0.4 # fraction of slow currents in microcircuit
xs_mode = 0.5 # fraction of slow currents from 4I to 4I
dsd = 1.0
circ = circuit.Circuit('microcircuit', analysis_type=None)
I_new = circ.I.copy()
# reduce indegrees from 4I to 4E
I_new[2][3] = np.round(circ.I[2][3] * (1.0 - 0.15))
Next_new = circ.Next.copy()
# adjust external input to 4E
Next_new[2] -= 300
Next_new[2] *= (1.0 - 0.011)
new_params = {
'de_sd': circ.de * dsd,
'di_sd': circ.di * dsd,
'I': I_new,
'Next': Next_new,
'delay_dist': 'truncated_gaussian'
}
params = circ.params
params.update(new_params)
params['xs'] = np.ones((8, 8)) * xs
params['xs_ext'] = np.ones(8) * xs
params['xs'][3][3] = xs_mode
circ = circuit.Circuit('microcircuit', params, fmax=500.0)
freqs, power = circ.create_power_spectra()
power_test = h5.load_h5(self.test_data, 'power_slow')
assert (np.allclose(power_test, power, rtol=self.rtol, atol=self.atol))
def inverse_qft(n, prepared=True, init_state=[]):
qft_circuit = circuit.Circuit(n, prepared=prepared, init_state=init_state)
# Reflect the qubits (the QFT swaps their order by default)
qft_circuit.append_layer(swap_all(n))
if n == 1:
qft_circuit.append_layer("H")
return qft_circuit
for i in range(n):
i = n - i - 1
# Layers with the rotations
for j in range(1, n - i):
qft_circuit.append_layer(
gates.CU(j + i, i, gates.R((-1) * 2 * gates.PI / (2**(j + 1))),
n))
# Layer with the H gate
current_layer = []
for j in range(i):
current_layer.append("I")
current_layer.append("H")
for j in range(n - i - 1):
current_layer.append("I")
qft_circuit.append_layer(*current_layer)
return qft_circuit
def pair_double_sel(ps, pm, pg, eta, a0, a1, theta):
"""
Create a entangled pair using the double selection
distillation protocol. The EPL protocol is used in the intermediate
entangled pairs used.
Parameters
----------
ps : single qubit gate error rate.
pm : measurement error rate.
pg : two qubit gate error rate.
eta : detection efficiency.
a0 : extra environmental error when electron spin is being operated.
a1 : default environmental error.
theta : determines how the states are initialized when generating remote
entanglement.
"""
cb = circuit_block.Blocks(ps, pm, pg, eta, a0, a1, theta)
epl = pair_EPL(ps, pm, pg, eta, a0, a1, theta)
double_sel = circuit.Circuit(a0=a0, a1=a1,
circuit_block=cb.double_selection_ops,
targets=[0, 1], ancillas1=[2, 3],
ancillas2=[4, 5], sigma="Z")
double_sel.add_circuit(circuit_block=cb.swap_pair,
pair=[0, 1])
double_sel.add_circuit(circuit_block=epl.append_circuit)
double_sel.add_circuit(circuit_block=epl.append_circuit)
double_sel.add_circuit(circuit_block=cb.start_epl)
return double_sel
def borneC(pi, matrice, v=False):
s = 0
c = circuit.Circuit(pi, matrice)
tc = c.resolve()
if len(pi) == len(matrice[0]):
#cas particulier ou pi est un ordonancement final
return tc
#amelioration tC prime
c = circuit.Circuit2M(pi, matrice)
tB = c.resolve()
if v:
print('matrice : ', matrice)
machineA = matrice[0]
machineB = matrice[1]
machineC = matrice[2]
tA = 0
miniB = float("inf")
miniAB = float("inf")
for i in range(len(machineC)):
if i not in pi:
s += machineC[i]
miniB = min(miniB, machineB[i])
miniAB = min(miniAB, machineA[i] + machineB[i])
else:
tA += machineA[i]
s += max(tc, tB + miniB, tA + miniAB)
return s
def generate_rep_bare_meas_ion(n_data,
stabilizer_list,
n_rounds,
even_spaced=True):
'''
'''
n_stab = len(stabilizer_list) / 2
rep_meas_circ = cir.Circuit()
circ_I = generate_wait_circ(n_data)
circ_I = cir.Encoded_Gate('WAIT', [circ_I]).circuit_wrap()
rep_meas_circ.join_circuit(circ_I, False)
for i in range(n_rounds):
gate_nameX = 'SX_%i' % i
stab_circX = generate_bare_meas_ion(n_data, stabilizer_list[:n_stab])
stab_circX = cir.Encoded_Gate(gate_nameX, [stab_circX]).circuit_wrap()
gate_nameZ = 'SZ_%i' % i
stab_circZ = generate_bare_meas_ion(n_data, stabilizer_list[n_stab:])
stab_circZ = cir.Encoded_Gate(gate_nameZ, [stab_circZ]).circuit_wrap()
rep_meas_circ.join_circuit(stab_circX, False)
rep_meas_circ.join_circuit(stab_circZ, False)
if even_spaced:
circ_I = generate_wait_circ(n_data)
circ_I = cir.Encoded_Gate('WAIT', [circ_I]).circuit_wrap()
rep_meas_circ.join_circuit(circ_I, False)
if not even_spaced:
circ_I = generate_wait_circ(n_data)
circ_I = cir.Encoded_Gate('WAIT', [circ_I]).circuit_wrap()
rep_meas_circ.join_circuit(circ_I, False)
return rep_meas_circ
def ghz2_double(ps, pm, pg, eta, a0, a1, theta):
"""
GHZ state of weigth 2 created using 2 Bell pairs
generated using the double selection protocol.
Parameters
----------
ps : (scalar) single qubit gate error rate.
pm : (scalar) measurement error rate.
pg : (scalar) two qubit gate error rate.
eta : (scalar) detection efficiency.
a0 : (scalar) extra environmental error when electron spin is being operated.
a1 : (scalar) default environmental error.
theta : (scalar) determines how the states are initialized when generating remote
entanglement.
"""
cb = circuit_block.Blocks(ps, pm, pg, eta, a0, a1, theta)
pair = pair_double_sel(ps, pm, pg, eta, a0, a1, theta)
ghz = circuit.Circuit(a0=a0, a1=a1,
circuit_block=cb.single_selection_ops,
targets=[0, 1], ancillas=[2, 3], sigma="Z")
ghz.add_circuit(circuit_block=cb.swap_pair,
pair=[0, 1])
ghz.add_circuit(circuit_block=pair.run_parallel)
return ghz
def main():
t1 = []
t2 = []
t3 = []
t4 = []
t5 = []
for counter in range(10):
print(counter)
c1 = circuit.Circuit.rand_circuit(sparse_tensor_gate.TensorGate,
sparse_tools.clifford_set, 8, 64)
c2 = circuit.Circuit.rand_circuit(sparse_tensor_gate.TensorGate,
sparse_tools.clifford_set, 8, 64)
c3 = circuit.Circuit(c1 + c2)
t1.append(time.time())
p1 = collapse(c1)
t2.append(time.time())
p2 = collapse(c2)
t3.append(time.time())
p3a = p1 * p2
t4.append(time.time())
p3b = collapse(c3)
t5.append(time.time())
d1 = np.mean([t2[i] - t1[i] for i in range(len(t1))])
d2 = np.mean([t3[i] - t2[i] for i in range(len(t1))])
d3 = np.mean([t4[i] - t3[i] for i in range(len(t1))])
d4 = np.mean([t5[i] - t4[i] for i in range(len(t1))])
print(f'p1: {d1}\np2: {d2}\np3: {d3}\np4: {d4}')
def __init__(self, modelType, modelParam, numInverters):
s0 = 3.0
if modelType == "lcMosfet":
model = circuit.LcMosfet
self.Vtp = modelParam[0]
self.Vtn = modelParam[1]
self.Vdd = modelParam[2]
self.Kn = modelParam[3]
self.Kp = modelParam[4]
nfet = circuit.MosfetModel('nfet', self.Vtn, self.Kn)
pfet = circuit.MosfetModel('pfet', self.Vtp, self.Kp)
elif modelType == "scMosfet":
model = circuit.ScMosfet
self.Vdd = modelParam[0]
nfet = circuit.MosfetModel('nfet')
pfet = circuit.MosfetModel('pfet')
self.numInverters = numInverters
# V = [V0, V1, V2, ... grnd, Vdd]
transistorList = []
for i in range(numInverters):
transistorList.append(
model(numInverters, i, (i + 1) % numInverters, nfet, s0))
transistorList.append(
model(numInverters + 1, i, (i + 1) % numInverters, pfet,
s0 * 2))
self.c = circuit.Circuit(transistorList)
self.bounds = []
for i in range(numInverters):
self.bounds.append([0.0, self.Vdd])
def __init__(self, modelType, modelParam, inputVoltage):
self.inputVoltage = inputVoltage
s0 = 3.0
if modelType == "lcMosfet":
model = circuit.LcMosfet
self.Vtp = modelParam[0]
self.Vtn = modelParam[1]
self.Vdd = modelParam[2]
self.Kn = modelParam[3]
self.Kp = modelParam[4]
nfet = circuit.MosfetModel('nfet', self.Vtn, self.Kn)
pfet = circuit.MosfetModel('pfet', self.Vtp, self.Kp)
elif modelType == "scMosfet":
model = circuit.ScMosfet
self.Vdd = modelParam[0]
nfet = circuit.MosfetModel('nfet')
pfet = circuit.MosfetModel('pfet')
# V = [outputVoltage, inputVoltage, grnd, Vdd]
transistorList = []
transistorList.append(model(2, 1, 0, nfet, s0))
transistorList.append(model(3, 1, 0, pfet, s0 * 2))
self.c = circuit.Circuit(transistorList)
self.bounds = []
# output voltage
self.bounds.append([0.0, self.Vdd])
def __init__(self):
self.circuit = circuit.Circuit() # the circuit which Alice will garble
# a mapping of each wire to its two labels (secret!)
self.wire_labels = dict()
# the wires which Alice will supply input to, mapped to their corresponding inputs.
# Values of this dictionary are secret!
self.input_wires = dict()
# a mapping of encrypted values to entry pairs. We cache this so that, if point-and-permute is enabled we can
# sort encrypted values according to their select bits without having to decrypt. We intentionally garble
# gates separately from permuting gates, and do it after encryption, to match the accompanying written
# tutorial.
self.encrypted_entries = dict()
# the random R value for free-XOR. Only used if free-XOR is enabled
if config.USE_FREE_XOR:
if config.USE_POINT_PERMUTE:
self.R = bitstring.Bits(bytes=os.urandom(16))
else:
# if we are using free-xor and NOT using point-and-permute, we need to to make sure R has the proper
# zero bits just like the labels, otherwise things get messed up!
self.R = bitstring.Bits(bytes=bytes(
config.CLASSIC_SECURITY_PARAMETER)) + bitstring.Bits(
bytes=os.urandom(16 -
config.CLASSIC_SECURITY_PARAMETER))
config.R = self.R
print("ALICE: Generating random R = {}".format(self.R.hex))
def testFiringRatesSlow(self):
circ = circuit.Circuit('microcircuit', analysis_type='stationary')
params = circ.params
xs_array = [0.1 * i for i in range(10)]
rates = []
for xs in xs_array:
params['xs'] = np.ones((8, 8)) * xs
params['xs_ext'] = np.ones(8) * xs
circ = circuit.Circuit('microcircuit',
params,
analysis_type='stationary',
from_file=False)
rates.append(circ.th_rates)
rates = np.transpose(np.asarray(rates))
rates_test = h5.load_h5(self.test_data, 'rates_slow')
assert (np.allclose(rates_test, rates, rtol=self.rtol, atol=self.atol))
def syndrome_meas_circuit(self):
"""Returns a circuit which measures all stabilizer generators
onto ancillae, using ``measure_gen_onto_ancilla``."""
return sum((self.measure_gen_onto_ancilla(idx_gen).relabel_qubits(
{self.nq: self.nq + idx_gen})
for idx_gen in range(len(self.group_generators))),
circuit.Circuit())
def ghz4_epl(ps, pm, pg, eta, a0, a1, theta):
"""
GHZ state of weigth 4 created using 4 Bell pairs
generated using the EPL protocol.
Parameters
----------
ps : (scalar) single qubit gate error rate.
pm : (scalar) measurement error rate.
pg : (scalar) two qubit gate error rate.
eta : (scalar) detection efficiency.
a0 : (scalar) extra environmental error when electron spin is being operated.
a1 : (scalar) default environmental error.
theta : (scalar) determines how the states are initialized when generating remote
entanglement.
"""
# Circuits are assemebled in reversed order
cb = circuit_block.Blocks(ps, pm, pg, eta, a0, a1, theta)
epl = pair_EPL(ps, pm, pg, eta, a0, a1, theta)
# Phase 3 - Create GHZ
# Perform the measurements
ghz = circuit.Circuit(a0=a0, a1=a1,
circuit_block=cb.collapse_ancillas_GHZ,
ghz_size=4,
measure_pos=[4, 5, 6, 7])
# Apply two qubit gates in the nodes
ghz.add_circuit(circuit_block=cb.two_qubit_gates, controls=[1, 3, 0, 2],
targets=[4, 5, 6, 7], sigma="X")
# Phase 2 Create last two pairs
pair = circuit.Circuit(a0=a0, a1=a1, circuit_block=cb.swap_pair,
pair=[0, 1])
pair.add_circuit(circuit_block=cb.start_epl)
wrap_EPL_parallel = circuit.Circuit(a0=a0, a1=a1,
circuit_block=pair.run_parallel)
ghz.add_circuit(circuit_block=wrap_EPL_parallel.append_circuit)
# Phase 1 Create initial two pairs
ghz.add_circuit(circuit_block=epl.run_parallel)
return ghz
def generate_wait_circ(n_data):
'''
'''
circ_I = cir.Circuit()
for i in range(n_data):
circ_I.add_gate_at([i], 'I_idle')
return circ_I
def arborescence_mix(nbTaches, matrice, b=bornes.b1, debug=False):
P1 = projet.Johnson(nbTaches, matrice)
c = circuit.Circuit(P1, matrice)
v = c.resolve()
taches = [i for i in range((int)(nbTaches))]
tree = Arbre(taches, matrice, b)
tree.borneSup = [v]
P, res = tree.resolve()
tree.accuracy(debug)
return P, res
def measure_node_to_circuit(self, measurement_node: qiskit.qasm.node.Measure)\
-> circuit.Circuit:
c = circuit.Circuit(self.num_qubits())
op_list = [rotation.PauliOperator.I] * self.num_qubits()
measure_idx: int = _SegmentedQASMParser.extract_measurement_idx(
measurement_node)
op_list[measure_idx] = _SegmentedQASMParser.measurement_operator
c.add_pauli_block(rotation.Measurement.from_list(op_list))
return c
def mixed_maker_helper(N, circuit_scale):
num_T = int(circuit_scale * (N**2) * T_percent)
num_gates = int(circuit_scale * (N**2) * (1 - T_percent))
circ = circuit.Circuit.rand_circuit(gate_constructor,
tools.clifford_set, N, num_gates)
circT = circuit.Circuit.rand_circuit(gate_constructor,
{'T': tools.universal_set["T"]},
N, num_gates)
circ2 = circ + circT
random.shuffle(circ2)
return circuit.Circuit(circ2)
def parse_new_circ_line(self, new_circ_line_list):
"""new-circ: global-id purpose is_predicted_circuit"""
assert (len(new_circ_line_list) == 4)
assert (new_circ_line_list[0] == "new-circ:")
global_id, purpose = new_circ_line_list[1], new_circ_line_list[2]
is_predicted = new_circ_line_list[3]
new_circ = circuit.Circuit(global_id, purpose, is_predicted,
self.timestamp)
self.hs_log.register_circuit(new_circ)
def GetAnalyzer(name=None, plot=False):
if name is None:
name = circuit._DEFAULT_CIRCUIT_NAME
with analyzer_instances_lock(util.READ_LOCKED):
if name in analyzer_instances:
analyzer = analyzer_instances[name]
else:
analyzer_instances_lock.promote() # Write locked.
analyzer = CircuitAnalyzer(circuit.Circuit(name), plot=plot)
analyzer_instances[name] = analyzer
return analyzer
def build_circuit(bitlength, n_parties, output_max=True):
num_inputs = [bitlength] * n_parties
c = circuit.Circuit(n_parties, num_inputs)
x0 = [0]
for i in range(1, n_parties):
x0.append(x0[i - 1] + num_inputs[i - 1])
bitmap, maximum = max_block(c, n_parties, bitlength, x0, output_max, None)
if output_max == True:
c.add_to_output_wires(maximum)
c.add_to_output_wires(bitmap)
c.wire_outputs()
return str(c)
def __init__(self, modelType, modelParam, inputVoltage):
s0 = 3.0
self.inputVoltage = inputVoltage
if modelType == "lcMosfet":
model = circuit.LcMosfet
self.Vtp = modelParam[0]
self.Vtn = modelParam[1]
self.Vdd = modelParam[2]
self.Kn = modelParam[3]
self.Kp = modelParam[4]
nfet = circuit.MosfetModel('nfet',
self.Vtn,
self.Kn,
gds="default")
pfet = circuit.MosfetModel('pfet',
self.Vtp,
self.Kp,
gds="default")
elif modelType == "scMosfet":
model = circuit.ScMosfet
self.Vdd = modelParam[0]
nfet = circuit.MosfetModel('nfet')
pfet = circuit.MosfetModel('pfet')
# with the voltage array containing [grnd, Vdd, input, X[0], X[1], X[2]]
# where X[0] is the output voltage and X[1] is the voltage at node with
# nfets and X[2] is the voltage at node with pfets
# src, gate, drain = grnd, input, X[1]
m0 = model(0, 2, 4, nfet, s0)
# src, gate, drain = X[1], input, X[0]
m1 = model(4, 2, 3, nfet, s0)
# src, gate, drain = X[1], X[0], Vdd
m2 = model(4, 3, 1, nfet, s0)
# src, gate, drain = Vdd, input, X[2]
m3 = model(1, 2, 5, pfet, s0 * 2.0)
# src, gate, drain = X[2], in, X[0]
m4 = model(5, 2, 3, pfet, s0 * 2.0)
# src, gate, drain = X[2], X[0], grnd
m5 = model(5, 3, 0, pfet, s0 * 2.0)
self.c = circuit.Circuit([m0, m1, m2, m3, m4, m5])
self.bounds = []
for i in range(3):
self.bounds.append([0.0, self.Vdd])
def testEmpiricalTransferFunction(self):
params = {}
params['tf_mode'] = 'empirical'
params['tau_impulse'] = np.array(
[8.555, 5.611, 4.167, 4.381, 4.131, 3.715, 4.538, 3.003])
params['delta_f'] = np.array([
0.0880, 0.458, 0.749, 0.884, 1.183, 1.671, 0.140, 1.710
]) / self.net.params['w']
net = circuit.Circuit('microcircuit', params)
freqs, power = net.create_power_spectra()
power_test = h5.load_h5(self.test_data, 'empirical/power')
assert (np.allclose(power_test, power, rtol=self.rtol, atol=self.atol))
def ghz3_bk_simple(ps, pm, pg, eta, a0, a1, theta):
"""
GHZ state of weigth 3 created using 2 Bell pairs
generated using the Barret-Kok protocol.
Parameters
----------
ps : (scalar) single qubit gate error rate.
pm : (scalar) measurement error rate.
pg : (scalar) two qubit gate error rate.
eta : (scalar) detection efficiency.
a0 : (scalar) extra environmental error when electron spin is being operated.
a1 : (scalar) default environmental error.
theta : (scalar) determines how the states are initialized when generating remote
entanglement.
"""
# Circuits are assemebled in reversed order
cb = circuit_block.Blocks(ps, pm, pg, eta, a0, a1, theta)
# Phase 3 - Create GHZ
# Perform the measurements
ghz = circuit.Circuit(a0=a0, a1=a1,
circuit_block=cb.collapse_ancillas_GHZ,
ghz_size=3,
measure_pos=[2])
# Apply two qubit gates in the nodes
ghz.add_circuit(circuit_block=cb.two_qubit_gates, controls=[1],
targets=[2], sigma="X")
# Phase 2 Create second pair
ghz.add_circuit(circuit_block=cb.swap_pair,
pair=[0, 1])
bk = circuit.Circuit(a0=a0, a1=a1, circuit_block=cb.start_BK)
ghz.add_circuit(circuit_block=bk.append_circuit)
# Phase 1 Create initial pair
ghz.add_circuit(circuit_block=cb.start_BK)
return ghz
def measure_gen_onto_ancilla(self, gen_idx):
"""
Produces a circuit that measures the stabilizer code generator
``self.group_generators[gen_idx]`` onto the qubit labelled by
``stab.nq`` (that is, the next qubit not in the physical register
used by the code).
:param int gen_idx: Index of a generator of the stabilizer group, as
specified by the ``group_generators`` property of this instance.
:returns qecc.Circuit: A circuit that maps a measurement of
``group_generators[gen_idx]`` onto a measurement of :math:`Z` on the
ancilla qubit alone.
"""
circ = circuit.Circuit()
for qubit_idx, operator in enumerate(
self.group_generators[gen_idx].op):
# operator = (self.group_generators[gen_idx].op)[qubit_idx]
if operator == 'I':
pass
elif operator == 'Z':
circ += circuit.Circuit(('CNOT', qubit_idx, self.nq))
elif operator == 'Y':
circ += circuit.Circuit(
circuit.Location('P', qubit_idx),
circuit.Location('Z', qubit_idx),
circuit.Location('H', qubit_idx),
circuit.Location('CNOT', qubit_idx, self.nq),
circuit.Location('H', qubit_idx),
circuit.Location('P', qubit_idx))
elif operator == 'X':
circ += circuit.Circuit(
circuit.Location('H', qubit_idx),
circuit.Location('CNOT', qubit_idx, self.nq),
circuit.Location('H', qubit_idx))
else:
raise ValueError("Pauli operator not I, X, Y, or Z")
return circ
def init_func():
house = 0
end_sw = 0
curr_player = 0
hands = init_shuffle()
p1 = player(hands[0], 0)
p2 = player(hands[1], 0)
p3 = player(hands[2], 0)
player_arr = [p1, p2, p3]
c = ct.Circuit()
return c, house, end_sw, curr_player, player_arr
def random_evaluation(pi, nbTaches, matrice, nbEvalutation=10):
taille = nbTaches - len(pi)
mini = float('inf')
for i in range(nbEvalutation):
genome = pi[::]
while len(genome) != nbTaches:
r = random.randint(0, nbTaches - 1)
if r not in genome:
genome.append(r)
#print('genome : ', genome)
c = circuit.Circuit(genome, matrice)
res = c.resolve()
mini = min(mini, res)
return mini
def ghz3_double(ps, pm, pg, eta, a0, a1, theta):
"""
GHZ state of weigth 3 created using 3 Bell pairs
generated using the EPL protocol.
Parameters
----------
ps : (scalar) single qubit gate error rate.
pm : (scalar) measurement error rate.
pg : (scalar) two qubit gate error rate.
eta : (scalar) detection efficiency.
a0 : (scalar) extra environmental error when electron spin is being operated.
a1 : (scalar) default environmental error.
theta : (scalar) determines how the states are initialized when generating remote
entanglement.
"""
# Circuits are assemebled in reversed order
cb = circuit_block.Blocks(ps, pm, pg, eta, a0, a1, theta)
pair = pair_double_sel(ps, pm, pg, eta, a0, a1, theta)
# Phase 3 - Create GHZ
# Perform the measurements
ghz = circuit.Circuit(a0=a0, a1=a1,
circuit_block=cb.collapse_ancillas_GHZ,
ghz_size=3,
measure_pos=[3, 4, 5])
# Apply two qubit gates in the nodes
ghz.add_circuit(circuit_block=cb.two_qubit_gates, controls=[1, 2, 0],
targets=[3, 4, 5], sigma="X")
# Phase 1 Create initial pair
swaped_pair = circuit.Circuit(a0=a0, a1=a1, circuit_block=cb.swap_pair,
pair=[0, 1])
swaped_pair.add_circuit(circuit_block=pair.run_as_block)
ghz.add_circuit(circuit_block=swaped_pair.run_parallel, parallel=3)
return ghz
def expend(self, debug=False):
node = self.LNode[-1]
if debug:
print('on examine le noeud ', node)
res = node.expend(debug)
if debug:
print('res : ', res)
if res == None:
del self.LNode[-1]
else:
if type(res) == bool and res == True:
#on est arriver à une feuille
v = node.borneInf
if len(node.P) < self.nbTaches:
#noeud intermedaire
del self.LNode[-1]
else:
if debug:
print('--------------------------------')
print('on examine une feuille ')
print('borne inf : {}, borne sup : {}'.format(
v, self.borneSup))
print('--------------------------------')
#pas nessessaire pour la borne1
c = circuit.Circuit(node.P, self.matrice)
trueValue = c.resolve()
if trueValue < self.borneSup[0]:
#probleme il faut prendre la vrai valeur de la solution ?
self.borneSup = [trueValue]
if debug:
print('la nouvelle borne sup est :', v)
self.bestP = node.P[::]
if debug:
print('bestP : {}'.format(self.bestP))
del self.LNode[-1]
else:
P = (node.P)[::]
P.append(res)
Lrestantes = (node.Lrestantes)[::]
Lrestantes.remove(res)
self.cpt += 1
new = Noeud(P, self.matrice, Lrestantes, self.f_borneInf,
self.borneSup)
if debug:
print("creation du noeud :", new)
self.LNode.append(new)
def local_test(filename):
with open(filename) as json_file:
json_circuits = json.load(json_file)
for json_circuit in json_circuits['circuits']:
circuit = c.Circuit(json_circuit)
# generate title
generateTitle(json_circuit['name'])
for aliceInput in circuit.perms(len(json_circuit['alice'])):
# check whether this circuit involves bob.
if circuit.bob:
# build relevant files needed to send to bob
toBob = circuit.sendToBob(aliceInput)
# iterate through bob's potential inputs.
for bobInput in circuit.perms(len(json_circuit['bob'])):
# get the reference output to verify the results.
test = circuit.compute(aliceInput + bobInput)
# do oblivious transfer on this.
bobPInput = []
for i in range(len(bobInput)):
bobValue = bobInput[i]
bobIndex = toBob['bobIndex'][i]
otB = ot.Bob(bobValue)
inp1, inp2 = circuit.setupBobOT(bobIndex)
otA = ot.Alice(inp1, inp2)
# garbled circuit stuff.
ot_c = otA.send_c()
h0 = otB.send_h0(ot_c)
c_1, E, length = otA.sendMessage(h0)
payload = otB.getMessage(c_1, E, length)
bobPInput.append(payload)
# bob evaluates the output.
output = bh.evaluate(toBob, pinputs=bobPInput)
# create checksum to determine whether
# our output is the same as the input.
check = True if test == output else False
if check:
# the computation is equivalent;
# Alice will compute the truth table.
circuit.printRow(aliceInput, bobInput)
else:
print("ERROR: The garbled circuit does not compute.")
else:
# bob is not involved in this circuit so theres no point
# computing our encrypted transfer.
circuit.printRow(aliceInput, None)
def oracle_circuit(o):
o = "{0:b}".format(o).zfill(n)
oracle_circuit = circuit.Circuit(n + 1, prepared=True)
first_third_layer = []
for i in range(n):
if o[i] == 0:
first_third_layer.append("X")
else:
first_third_layer.append("I")
first_third_layer.append("I")
oracle_circuit.append_layer(*first_third_layer)
second_layer = [CNOT_All()]
oracle_circuit.append_layer(*second_layer)
oracle_circuit.append_layer(*first_third_layer)
return oracle_circuit