def controlled(U, ctrl=(1,), dim=None):
r"""Controlled gate.
Returns the (t+1)-qudit controlled-U gate, where t == length(ctrl).
U has to be a square matrix.
ctrl is an integer vector defining the control nodes. It has one entry k per
control qudit, denoting the required computational basis state :math:`|k\rangle`
for that particular qudit. Value k == -1 denotes no control.
dim is the dimensions vector for the control qudits. If not given, all controls
are assumed to be qubits.
Examples:
controlled(NOT, [1]) gives the standard CNOT gate.
controlled(NOT, [1, 1]) gives the Toffoli gate.
"""
# Ville Bergholm 2009-2011
# TODO generalization, uniformly controlled gates?
if isscalar(dim): dim = (dim,) # scalar into a tuple
t = len(ctrl)
if dim == None:
dim = qubits(t) # qubits by default
if t != len(dim):
raise ValueError('ctrl and dim vectors have unequal lengths.')
if any(array(ctrl) >= array(dim)):
raise ValueError('Control on non-existant state.')
yes = 1 # just the diagonal
for k in range(t):
if ctrl[k] >= 0:
temp = zeros(dim[k])
temp[ctrl[k]] = 1 # control on k
yes = kron(yes, temp)
else:
yes = kron(yes, ones(dim[k])) # no control on this qudit
no = 1 - yes
T = prod(dim)
dim = list(dim)
if isinstance(U, lmap):
d1 = dim + list(U.dim[0])
d2 = dim + list(U.dim[1])
U = U.data
else:
d1 = dim + [U.shape[0]]
d2 = dim + [U.shape[1]]
# controlled gates only make sense for square matrices U (we need an identity transformation for the 'no' cases!)
U_dim = U.shape[0]
S = U_dim * T
out = sparse.spdiags(kron(no, ones(U_dim)), 0, S, S) + sparse.kron(sparse.spdiags(yes, 0, T, T), U)
return lmap(out, (d1, d2))
def walsh(n):
"""Walsh-Hadamard gate.
Returns the Walsh-Hadamard gate for n qubits.
The returned lmap is dense.
"""
# Ville Bergholm 2009-2010
from base import H
U = 1
for k in range(n):
U = kron(U, H)
dim = qubits(n)
return lmap(U, (dim, dim))
