def _define_fixed_operators(self):
N1 = self.N1
N2 = self.N2
self.I = tensor([ops.identity(N1), ops.identity(N2)])
self.a1 = tensor([ops.destroy(N1), ops.identity(N2)])
self.a1_dag = tensor([ops.create(N1), ops.identity(N2)])
self.a2 = tensor([ops.identity(N1), ops.destroy(N2)])
self.a2_dag = tensor([ops.identity(N1), ops.create(N2)])
self.q1 = tensor([ops.position(N1), ops.identity(N2)])
self.p1 = tensor([ops.momentum(N1), ops.identity(N2)])
self.n1 = tensor([ops.num(N1), ops.identity(N2)])
self.q2 = tensor([ops.identity(N1), ops.position(N2)])
self.p2 = tensor([ops.identity(N1), ops.momentum(N2)])
self.n2 = tensor([ops.identity(N1), ops.num(N2)])
self.parity1 = tensor([ops.parity(N1), ops.identity(N2)])
self.parity2 = tensor([ops.identity(N1), ops.parity(N2)])
tensor_with = [None, ops.identity(N2)]
self.translate1 = ops.TranslationOperator(N1, tensor_with=tensor_with)
self.displace1 = lambda a: self.translate1(sqrt(2) * a)
self.rotate1 = ops.RotationOperator(N1, tensor_with=tensor_with)
tensor_with = [ops.identity(N1), None]
self.translate2 = ops.TranslationOperator(N2, tensor_with=tensor_with)
self.displace2 = lambda a: self.translate2(sqrt(2) * a)
self.rotate2 = ops.RotationOperator(N2, tensor_with=tensor_with)
def _define_fixed_operators(self):
N = self.N
self.I = ops.identity(N)
self.a = ops.destroy(N)
self.a_dag = ops.create(N)
self.q = ops.position(N)
self.p = ops.momentum(N)
self.n = ops.num(N)
self.parity = ops.parity(N)
self.phase = ops.Phase()
self.rotate = ops.RotationOperator(N)
self.translate = ops.TranslationOperator(N)
self.displace = lambda a: self.translate(sqrt(2) * a)
self.SNAP = ops.SNAP(N)
def Hamiltonian(Ej, Ec, freq_a, V):
"""
In the final Hamiltonian the levels should be interpreted (qubit, cavity):
eigvals[0] : (0,0)
eigvals[1] : (0,1)
eigvals[2] : (1,0)
eigvals[4] : (1,1)
eigvals[5] : (2,0)
"""
plot_charge_matrix = False
plot_transmon_levels = False
# diagonalize transmon in charge basis
n = tf.cast(diag(tf.range(-N_qb / 2, N_qb / 2)), c64)
cos_phi = tf.cast(
(diag(tf.ones(N_qb - 1), k=1) + diag(tf.ones(N_qb - 1), k=-1)) / 2,
c64)
H = -Ej * cos_phi + 4 * Ec * n**2
eigvals, U = tf.linalg.eigh(H) # H = U @ diag(eigvals) @ U_dag
# project onto subspace of L transmon levels
H_L = tf.linalg.diag(eigvals[:L])
n_L = (tf.linalg.adjoint(U) @ n @ U)[:L, :L]
# operators in joint transmon-cavity Hilbert space
H_cav = freq_a * tensor([ops.identity(L), ops.num(N_cav)])
H_qb = tensor([H_L, ops.identity(N_cav)])
H_coupling = V * tensor([n_L, ops.position(N_cav)])
if plot_transmon_levels:
fig, ax = plt.subplots(1, 1)
ax.set_ylabel('Transmon energy level (Hz)')
ax.plot(range(25), eigvals[:25], marker='.', linestyle='none')
if plot_charge_matrix:
fig, ax = plt.subplots(1, 1)
ax.set_title('Charge matrix in transmon basis')
ax.pcolormesh(range(L), range(L), np.transpose(np.abs(n_L)))
H0 = H_cav + H_qb
H = H0 + H_coupling
# free up that sweet memory
del (n, cos_phi, H_L, n_L, H_cav, H_qb, H_coupling)
return H0, H
def _define_fixed_operators(self):
N = self.N
N_large = self._N_large
self.I = tensor([ops.identity(2), ops.identity(N)])
self.a = tensor([ops.identity(2), ops.destroy(N)])
self.a_dag = tensor([ops.identity(2), ops.create(N)])
self.q = tensor([ops.identity(2), ops.position(N)])
self.p = tensor([ops.identity(2), ops.momentum(N)])
self.n = tensor([ops.identity(2), ops.num(N)])
self.parity = tensor([ops.identity(2), ops.parity(N)])
self.sx = tensor([ops.sigma_x(), ops.identity(N)])
self.sy = tensor([ops.sigma_y(), ops.identity(N)])
self.sz = tensor([ops.sigma_z(), ops.identity(N)])
self.sm = tensor([ops.sigma_m(), ops.identity(N)])
tensor_with = [ops.identity(2), None]
self.phase = ops.Phase()
self.translate = ops.TranslationOperator(N, tensor_with=tensor_with)
self.displace = lambda a: self.translate(sqrt(2) * a)
self.rotate = ops.RotationOperator(N, tensor_with=tensor_with)
# displacement operators with larger intermediate hilbert space used for tomography
self.translate_large = lambda a: tensor(
[ops.identity(2), ops.TranslationOperator(N_large)(a)[:, :N, :N]]
)
self.displace_large = lambda a: self.translate_large(sqrt(2) * a)
self.displaced_parity_large = lambda a: tf.linalg.matmul(
tf.linalg.matmul(self.displace_large(a), self.parity),
self.displace_large(-a),
)
tensor_with = [None, ops.identity(N)]
self.rotate_qb_xy = ops.QubitRotationXY(tensor_with=tensor_with)
self.rotate_qb_z = ops.QubitRotationZ(tensor_with=tensor_with)
self.rxp = self.rotate_qb_xy(tf.constant(pi / 2), tf.constant(0))
self.rxm = self.rotate_qb_xy(tf.constant(-pi / 2), tf.constant(0))
# qubit sigma_z measurement projector
self.P = {i: tensor([ops.projector(i, 2), ops.identity(N)]) for i in [0, 1]}
self.sx_selective = tensor([ops.sigma_x(), ops.projector(0, N)]) + tensor(
[ops.identity(2), ops.identity(N) - ops.projector(0, N)]
)
def _define_fixed_operators(self):
N = self.N
self.I = tensor([ops.identity(2), ops.identity(N)])
self.a = tensor([ops.identity(2), ops.destroy(N)])
self.a_dag = tensor([ops.identity(2), ops.create(N)])
self.q = tensor([ops.identity(2), ops.position(N)])
self.p = tensor([ops.identity(2), ops.momentum(N)])
self.n = tensor([ops.identity(2), ops.num(N)])
self.parity = tensor([ops.identity(2), ops.parity(N)])
self.sx = tensor([ops.sigma_x(), ops.identity(N)])
self.sy = tensor([ops.sigma_y(), ops.identity(N)])
self.sz = tensor([ops.sigma_z(), ops.identity(N)])
self.sm = tensor([ops.sigma_m(), ops.identity(N)])
self.hadamard = tensor([ops.hadamard(), ops.identity(N)])
tensor_with = [ops.identity(2), None]
self.phase = ops.Phase()
self.translate = ops.TranslationOperator(N, tensor_with=tensor_with)
self.displace = lambda a: self.translate(sqrt(2) * a)
self.rotate = ops.RotationOperator(N, tensor_with=tensor_with)
self.SNAP = ops.SNAP(N, tensor_with=tensor_with)
self.SNAP_miscalibrated = ops.SNAPv3(N, chi=1e6, pulse_len=3.4e-6)
tensor_with = [None, ops.identity(N)]
self.rotate_qb_xy = ops.QubitRotationXY(tensor_with=tensor_with)
self.rotate_qb_z = ops.QubitRotationZ(tensor_with=tensor_with)
self.rxp = self.rotate_qb_xy(tf.constant(pi / 2), tf.constant(0))
self.rxm = self.rotate_qb_xy(tf.constant(-pi / 2), tf.constant(0))
# qubit sigma_z measurement projector
self.P = {
i: tensor([ops.projector(i, 2),
ops.identity(N)])
for i in [0, 1]
}
self.sx_selective = tensor([ops.sigma_x(), ops.projector(0, N)]) + \
tensor([ops.identity(2), ops.identity(N)-ops.projector(0, N)])
def test_num(self):
npt.assert_array_equal(num(self.N), qt.num(self.N))