def swap(N=None, control=None, target=None, mask=None):
"""Quantum object representing the SWAP gate.
Returns
-------
swap_gate : qobj
Quantum object representation of SWAP gate
Examples
--------
>>> swap()
Quantum object: dims = [[2, 2], [2, 2]], \
shape = [4, 4], type = oper, isHerm = True
Qobj data =
[[ 1.+0.j 0.+0.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 1.+0.j 0.+0.j]
[ 0.+0.j 1.+0.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 0.+0.j 1.+0.j]]
"""
if mask:
warnings.warn("The mask argument to iswap is deprecated. " +
"Use the N, control and target arguments instead.")
if sum(mask) != 2:
raise ValueError("mask must only have two ones, rest zeros")
dims = [2] * len(mask)
idx, = where(mask)
N = prod(dims)
data = sp.lil_matrix((N, N))
for s1 in state_number_enumerate(dims):
i1 = state_number_index(dims, s1)
if s1[idx[0]] == s1[idx[1]]:
i2 = i1
else:
s2 = array(s1).copy()
s2[idx[0]], s2[idx[1]] = s2[idx[1]], s2[idx[0]]
i2 = state_number_index(dims, s2)
data[i1, i2] = 1
return Qobj(data, dims=[dims, dims], shape=[N, N])
elif not N is None and not control is None and not target is None:
return gate_expand_2toN(swap(), N, control, target)
else:
uu = qstate('uu')
ud = qstate('ud')
du = qstate('du')
dd = qstate('dd')
Q = uu * uu.dag() + ud * du.dag() + du * ud.dag() + dd * dd.dag()
return Q
def swap(N=None, control=None, target=None, mask=None):
"""Quantum object representing the SWAP gate.
Returns
-------
swap_gate : qobj
Quantum object representation of SWAP gate
Examples
--------
>>> swap()
Quantum object: dims = [[2, 2], [2, 2]], \
shape = [4, 4], type = oper, isHerm = True
Qobj data =
[[ 1.+0.j 0.+0.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 1.+0.j 0.+0.j]
[ 0.+0.j 1.+0.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 0.+0.j 1.+0.j]]
"""
if mask:
warnings.warn("The mask argument to iswap is deprecated. " +
"Use the N, control and target arguments instead.")
if sum(mask) != 2:
raise ValueError("mask must only have two ones, rest zeros")
dims = [2] * len(mask)
idx, = where(mask)
N = prod(dims)
data = sp.lil_matrix((N, N))
for s1 in state_number_enumerate(dims):
i1 = state_number_index(dims, s1)
if s1[idx[0]] == s1[idx[1]]:
i2 = i1
else:
s2 = array(s1).copy()
s2[idx[0]], s2[idx[1]] = s2[idx[1]], s2[idx[0]]
i2 = state_number_index(dims, s2)
data[i1, i2] = 1
return Qobj(data, dims=[dims, dims], shape=[N, N])
elif not N is None and not control is None and not target is None:
return gate_expand_2toN(swap(), N, control, target)
else:
uu = qstate('uu')
ud = qstate('ud')
du = qstate('du')
dd = qstate('dd')
Q = uu * uu.dag() + ud * du.dag() + du * ud.dag() + dd * dd.dag()
return Q
def iswap(N=None, control=None, target=None, mask=None):
"""Quantum object representing the iSWAP gate.
Returns
-------
iswap_gate : qobj
Quantum object representation of iSWAP gate
Examples
--------
>>> iswap()
Quantum object: dims = [[2, 2], [2, 2]], \
shape = [4, 4], type = oper, isHerm = False
Qobj data =
[[ 1.+0.j 0.+0.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 0.+1.j 0.+0.j]
[ 0.+0.j 0.+1.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 0.+0.j 1.+0.j]]
"""
if mask:
warnings.warn("The mask argument to iswap is deprecated. " +
"Use the N, control and target arguments instead.")
if sum(mask) != 2:
raise ValueError("mask must only have two ones, rest zeros")
dims = [2] * len(mask)
idx, = where(mask)
N = prod(dims)
data = sp.lil_matrix((N, N), dtype=complex)
for s1 in state_number_enumerate(dims):
i1 = state_number_index(dims, s1)
if s1[idx[0]] == s1[idx[1]]:
i2 = i1
val = 1.0
else:
s2 = s1.copy()
s2[idx[0]], s2[idx[1]] = s2[idx[1]], s2[idx[0]]
i2 = state_number_index(dims, s2)
val = 1.0j
data[i1, i2] = val
return Qobj(data, dims=[dims, dims], shape=[N, N])
elif not N is None and not control is None and not target is None:
return gate_expand_2toN(iswap(), N, control, target)
else:
return Qobj(array([[1, 0, 0, 0], [0, 0, 1j, 0], [0, 1j, 0, 0],
[0, 0, 0, 1]]),
dims=[[2, 2], [2, 2]])
def iswap(N=None, control=None, target=None, mask=None):
"""Quantum object representing the iSWAP gate.
Returns
-------
iswap_gate : qobj
Quantum object representation of iSWAP gate
Examples
--------
>>> iswap()
Quantum object: dims = [[2, 2], [2, 2]], \
shape = [4, 4], type = oper, isHerm = False
Qobj data =
[[ 1.+0.j 0.+0.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 0.+1.j 0.+0.j]
[ 0.+0.j 0.+1.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 0.+0.j 1.+0.j]]
"""
if mask:
warnings.warn("The mask argument to iswap is deprecated. " +
"Use the N, control and target arguments instead.")
if sum(mask) != 2:
raise ValueError("mask must only have two ones, rest zeros")
dims = [2] * len(mask)
idx, = where(mask)
N = prod(dims)
data = sp.lil_matrix((N, N), dtype=complex)
for s1 in state_number_enumerate(dims):
i1 = state_number_index(dims, s1)
if s1[idx[0]] == s1[idx[1]]:
i2 = i1
val = 1.0
else:
s2 = s1.copy()
s2[idx[0]], s2[idx[1]] = s2[idx[1]], s2[idx[0]]
i2 = state_number_index(dims, s2)
val = 1.0j
data[i1, i2] = val
return Qobj(data, dims=[dims, dims], shape=[N, N])
elif not N is None and not control is None and not target is None:
return gate_expand_2toN(iswap(), N, control, target)
else:
return Qobj(array([[1, 0, 0, 0], [0, 0, 1j, 0], [0, 1j, 0, 0],
[0, 0, 0, 1]]),
dims=[[2, 2], [2, 2]])
def _partial_transpose_reference(rho, mask):
"""
This is a reference implementation that explicitly loops over
all states and performs the transpose. It's slow but easy to
understand and useful for testing.
"""
A_pt = np.zeros(rho.shape, dtype=complex)
for psi_A in state_number_enumerate(rho.dims[0]):
m = state_number_index(rho.dims[0], psi_A)
for psi_B in state_number_enumerate(rho.dims[1]):
n = state_number_index(rho.dims[1], psi_B)
m_pt = state_number_index(
rho.dims[1], np.choose(mask, [psi_A, psi_B]))
n_pt = state_number_index(
rho.dims[0], np.choose(mask, [psi_B, psi_A]))
A_pt[m_pt, n_pt] = rho.data[m, n]
return Qobj(A_pt, dims=rho.dims)
def _partial_transpose_reference(rho, mask):
"""
This is a reference implementation that explicitly loops over
all states and performs the transpose. It's slow but easy to
understand and useful for testing.
"""
A_pt = np.zeros(rho.shape, dtype=complex)
for psi_A in state_number_enumerate(rho.dims[0]):
m = state_number_index(rho.dims[0], psi_A)
for psi_B in state_number_enumerate(rho.dims[1]):
n = state_number_index(rho.dims[1], psi_B)
m_pt = state_number_index(
rho.dims[1], np.choose(mask, [psi_A, psi_B]))
n_pt = state_number_index(
rho.dims[0], np.choose(mask, [psi_B, psi_A]))
A_pt[m_pt, n_pt] = rho.data[m, n]
return Qobj(A_pt, dims=rho.dims)
def swap(mask=None):
"""Quantum object representing the SWAP gate.
Returns
-------
swap_gate : qobj
Quantum object representation of SWAP gate
Examples
--------
>>> swap()
Quantum object: dims = [[2, 2], [2, 2]], \
shape = [4, 4], type = oper, isHerm = True
Qobj data =
[[ 1.+0.j 0.+0.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 1.+0.j 0.+0.j]
[ 0.+0.j 1.+0.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 0.+0.j 1.+0.j]]
"""
if mask is None:
uu = qstate('uu')
ud = qstate('ud')
du = qstate('du')
dd = qstate('dd')
Q = uu * uu.dag() + ud * du.dag() + du * ud.dag() + dd * dd.dag()
return Qobj(Q)
else:
if sum(mask) != 2:
raise ValueError("mask must only have two ones, rest zeros")
dims = [2] * len(mask)
idx, = where(mask)
N = prod(dims)
data = sp.lil_matrix((N, N))
for s1 in state_number_enumerate(dims):
i1 = state_number_index(dims, s1)
if s1[idx[0]] == s1[idx[1]]:
i2 = i1
else:
s2 = array(s1).copy()
s2[idx[0]], s2[idx[1]] = s2[idx[1]], s2[idx[0]]
i2 = state_number_index(dims, s2)
data[i1, i2] = 1
return Qobj(data, dims=[dims, dims], shape=[N, N])
def swap(mask=None):
"""Quantum object representing the SWAP gate.
Returns
-------
swap_gate : qobj
Quantum object representation of SWAP gate
Examples
--------
>>> swap()
Quantum object: dims = [[2, 2], [2, 2]], \
shape = [4, 4], type = oper, isHerm = True
Qobj data =
[[ 1.+0.j 0.+0.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 1.+0.j 0.+0.j]
[ 0.+0.j 1.+0.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 0.+0.j 1.+0.j]]
"""
if mask is None:
uu = qstate('uu')
ud = qstate('ud')
du = qstate('du')
dd = qstate('dd')
Q = uu * uu.dag() + ud * du.dag() + du * ud.dag() + dd * dd.dag()
return Qobj(Q)
else:
if sum(mask) != 2:
raise ValueError("mask must only have two ones, rest zeros")
dims = [2] * len(mask)
idx, = where(mask)
N = prod(dims)
data = sp.lil_matrix((N, N))
for s1 in state_number_enumerate(dims):
i1 = state_number_index(dims, s1)
if s1[idx[0]] == s1[idx[1]]:
i2 = i1
else:
s2 = array(s1).copy()
s2[idx[0]], s2[idx[1]] = s2[idx[1]], s2[idx[0]]
i2 = state_number_index(dims, s2)
data[i1, i2] = 1
return Qobj(data, dims=[dims, dims], shape=[N, N])
def iswap(mask=None):
"""Quantum object representing the iSWAP gate.
Returns
-------
iswap_gate : qobj
Quantum object representation of iSWAP gate
Examples
--------
>>> iswap()
Quantum object: dims = [[2, 2], [2, 2]], \
shape = [4, 4], type = oper, isHerm = False
Qobj data =
[[ 1.+0.j 0.+0.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 0.+1.j 0.+0.j]
[ 0.+0.j 0.+1.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 0.+0.j 1.+0.j]]
"""
if mask is None:
return Qobj(array([[1, 0, 0, 0], [0, 0, 1j, 0], [0, 1j, 0, 0],
[0, 0, 0, 1]]),
dims=[[2, 2], [2, 2]])
else:
if sum(mask) != 2:
raise ValueError("mask must only have two ones, rest zeros")
dims = [2] * len(mask)
idx, = where(mask)
N = prod(dims)
data = sp.lil_matrix((N, N), dtype=complex)
for s1 in state_number_enumerate(dims):
i1 = state_number_index(dims, s1)
if s1[idx[0]] == s1[idx[1]]:
i2 = i1
val = 1.0
else:
s2 = s1.copy()
s2[idx[0]], s2[idx[1]] = s2[idx[1]], s2[idx[0]]
i2 = state_number_index(dims, s2)
val = 1.0j
data[i1, i2] = val
return Qobj(data, dims=[dims, dims], shape=[N, N])
def iswap(mask=None):
"""Quantum object representing the iSWAP gate.
Returns
-------
iswap_gate : qobj
Quantum object representation of iSWAP gate
Examples
--------
>>> iswap()
Quantum object: dims = [[2, 2], [2, 2]], \
shape = [4, 4], type = oper, isHerm = False
Qobj data =
[[ 1.+0.j 0.+0.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 0.+1.j 0.+0.j]
[ 0.+0.j 0.+1.j 0.+0.j 0.+0.j]
[ 0.+0.j 0.+0.j 0.+0.j 1.+0.j]]
"""
if mask is None:
return Qobj(array([[1, 0, 0, 0], [0, 0, 1j, 0], [0, 1j, 0, 0],
[0, 0, 0, 1]]),
dims=[[2, 2], [2, 2]])
else:
if sum(mask) != 2:
raise ValueError("mask must only have two ones, rest zeros")
dims = [2] * len(mask)
idx, = where(mask)
N = prod(dims)
data = sp.lil_matrix((N, N), dtype=complex)
for s1 in state_number_enumerate(dims):
i1 = state_number_index(dims, s1)
if s1[idx[0]] == s1[idx[1]]:
i2 = i1
val = 1.0
else:
s2 = s1.copy()
s2[idx[0]], s2[idx[1]] = s2[idx[1]], s2[idx[0]]
i2 = state_number_index(dims, s2)
val = 1.0j
data[i1, i2] = val
return Qobj(data, dims=[dims, dims], shape=[N, N])
