def calc_Vsh(A, r_s, sanity_checks=False):
D = A.shape[2]
Dm1 = A.shape[1]
q = A.shape[0]
if q * D - Dm1 <= 0:
return None
R = sp.zeros((D, q, Dm1), dtype=A.dtype, order='C')
for s in xrange(q):
R[:,s,:] = r_s.dot(A[s].conj().T)
R = R.reshape((q * D, Dm1))
Vconj = ns.nullspace_qr(R.conj().T).T
if sanity_checks:
if not sp.allclose(mm.mmul(Vconj.conj(), R), 0):
log.warning("Sanity Fail in calc_Vsh!: VR != 0")
if not sp.allclose(mm.mmul(Vconj, Vconj.conj().T), sp.eye(Vconj.shape[0])):
log.warning("Sanity Fail in calc_Vsh!: V H(V) != eye")
Vconj = Vconj.reshape((q * D - Dm1, D, q))
Vsh = Vconj.T
Vsh = sp.asarray(Vsh, order='C')
if sanity_checks:
Vs = sp.transpose(Vsh, axes=(0, 2, 1)).conj()
M = eps_r_noop(r_s, Vs, A)
if not sp.allclose(M, 0):
log.warning("Sanity Fail in calc_Vsh!: Bad Vsh")
return Vsh
def density_2s(self, n1, n2):
"""Returns a reduced density matrix for a pair of sites.
Currently only supports sites in the nonuniform window.
Parameters
----------
n1 : int
The site number of the first site.
n2 : int
The site number of the second site (must be > n1).
"""
rho = sp.empty((self.q[n1] * self.q[n2], self.q[n1] * self.q[n2]), dtype=sp.complex128)
r_n2 = sp.empty_like(self.r[n2 - 1])
r_n1 = sp.empty_like(self.r[n1 - 1])
ln1m1 = self.get_l(n1 - 1)
for s2 in xrange(self.q[n2]):
for t2 in xrange(self.q[n2]):
r_n2 = mm.mmul(self.A[n2][t2], self.r[n2], mm.H(self.A[n2][s2]))
r_n = r_n2
for n in reversed(xrange(n1 + 1, n2)):
r_n = tm.eps_r_noop(r_n, self.A[n], self.A[n])
for s1 in xrange(self.q[n1]):
for t1 in xrange(self.q[n1]):
r_n1 = mm.mmul(self.A[n1][t1], r_n, mm.H(self.A[n1][s1]))
tmp = mm.adot(ln1m1, r_n1)
rho[s1 * self.q[n1] + s2, t1 * self.q[n1] + t2] = tmp
return rho
def density_2s(self, d):
"""Returns a reduced density matrix for a pair of (seperated) sites.
The site number basis is used: rho[s * q + u, t * q + v]
with 0 <= s, t < q and 0 <= u, v < q.
The state must be up-to-date -- see self.update()!
Parameters
----------
d : int
The distance between the first and the second sites considered (d = n2 - n1).
Returns
-------
rho : ndarray
Reduced density matrix in the number basis.
"""
rho = sp.empty((self.q * self.q, self.q * self.q), dtype=sp.complex128)
for s2 in xrange(self.q):
for t2 in xrange(self.q):
r_n2 = m.mmul(self.A[t2], self.r, m.H(self.A[s2]))
r_n = r_n2
for n in xrange(d - 1):
r_n = tm.eps_r_noop(r_n, self.A, self.A)
for s1 in xrange(self.q):
for t1 in xrange(self.q):
r_n1 = m.mmul(self.A[t1], r_n, m.H(self.A[s1]))
tmp = m.adot(self.l, r_n1)
rho[s1 * self.q + s2, t1 * self.q + t2] = tmp
return rho
def density_2s(self, n1, n2):
"""Returns a reduced density matrix for a pair of sites.
Currently only supports sites in the nonuniform window.
Parameters
----------
n1 : int
The site number of the first site.
n2 : int
The site number of the second site (must be > n1).
"""
rho = sp.empty((self.q[n1] * self.q[n2], self.q[n1] * self.q[n2]),
dtype=sp.complex128)
r_n2 = sp.empty_like(self.r[n2 - 1])
r_n1 = sp.empty_like(self.r[n1 - 1])
ln1m1 = self.get_l(n1 - 1)
for s2 in xrange(self.q[n2]):
for t2 in xrange(self.q[n2]):
r_n2 = mm.mmul(self.A[n2][t2], self.r[n2],
mm.H(self.A[n2][s2]))
r_n = r_n2
for n in reversed(xrange(n1 + 1, n2)):
r_n = tm.eps_r_noop(r_n, self.A[n], self.A[n])
for s1 in xrange(self.q[n1]):
for t1 in xrange(self.q[n1]):
r_n1 = mm.mmul(self.A[n1][t1], r_n,
mm.H(self.A[n1][s1]))
tmp = mm.adot(ln1m1, r_n1)
rho[s1 * self.q[n1] + s2, t1 * self.q[n1] + t2] = tmp
return rho
def density_2s(self, n1, n2):
"""Returns a reduced density matrix for a pair of sites.
Parameters
----------
n1 : int
The site number of the first site.
n2 : int
The site number of the second site (must be > n1).
"""
rho = sp.empty((self.q[n1] * self.q[n2], self.q[n1] * self.q[n2]), dtype=sp.complex128)
r_n2 = sp.empty_like(self.r[n2 - 1])
r_n1 = sp.empty_like(self.r[n1 - 1])
for s2 in xrange(self.q[n2]):
for t2 in xrange(self.q[n2]):
r_n2 = m.mmul(self.A[n2][t2], self.r[n2], m.H(self.A[n2][s2]))
r_n = r_n2
for n in reversed(xrange(n1 + 1, n2)):
r_n = self.eps_r(n, r_n)
for s1 in xrange(self.q[n1]):
for t1 in xrange(self.q[n1]):
r_n1 = m.mmul(self.A[n1][t1], r_n, m.H(self.A[n1][s1]))
tmp = m.mmul(self.l[n1 - 1], r_n1)
rho[s1 * self.q[n1] + s2, t1 * self.q[n1] + t2] = tmp.trace()
return rho
def calc_Vsh(A, r_s, sanity_checks=False):
D = A.shape[2]
Dm1 = A.shape[1]
q = A.shape[0]
if q * D - Dm1 <= 0:
return None
R = sp.zeros((D, q, Dm1), dtype=A.dtype, order='C')
for s in xrange(q):
R[:, s, :] = r_s.dot(A[s].conj().T)
R = R.reshape((q * D, Dm1))
Vconj = ns.nullspace_qr(R.conj().T).T
if sanity_checks:
if not sp.allclose(mm.mmul(Vconj.conj(), R), 0):
log.warning("Sanity Fail in calc_Vsh!: VR != 0")
if not sp.allclose(mm.mmul(Vconj,
Vconj.conj().T), sp.eye(Vconj.shape[0])):
log.warning("Sanity Fail in calc_Vsh!: V H(V) != eye")
Vconj = Vconj.reshape((q * D - Dm1, D, q))
Vsh = Vconj.T
Vsh = sp.asarray(Vsh, order='C')
if sanity_checks:
Vs = sp.transpose(Vsh, axes=(0, 2, 1)).conj()
M = eps_r_noop(r_s, Vs, A)
if not sp.allclose(M, 0):
log.warning("Sanity Fail in calc_Vsh!: Bad Vsh")
return Vsh
def restore_ONR_n(self, n, G_n_i):
"""Transforms a single A[n] to obtain right orthonormalization.
Implements the condition for right-orthonormalization from sub-section
3.1, theorem 1 of arXiv:quant-ph/0608197v2.
This function must be called for each n in turn, starting at N + 1,
passing the gauge transformation matrix from the previous step
as an argument.
Finds a G[n-1] such that ON_R is fulfilled for n.
Eigenvalues = 0 are a problem here... IOW rank-deficient matrices.
Apparently, they can turn up during a run, but if they do we're screwed.
The fact that M should be positive definite is used to optimize this.
Parameters
----------
n : int
The site number.
G_n_i : ndarray
The inverse gauge transform matrix for site n obtained in the previous step (for n + 1).
Returns
-------
G_n_m1_i : ndarray
The inverse gauge transformation matrix for the site n - 1.
"""
if G_n_i is None:
GGh_n_i = self.r[n]
else:
GGh_n_i = mm.mmul(G_n_i, self.r[n], mm.H(G_n_i))
M = self.eps_r(n, GGh_n_i)
try:
tu = la.cholesky(M) #Assumes M is pos. def.. It should raise LinAlgError if not.
G_nm1 = mm.H(mm.invtr(tu)) #G is now lower-triangular
G_nm1_i = mm.H(tu)
except sp.linalg.LinAlgError:
print "Restore_ON_R_%u: Falling back to eigh()!" % n
e,Gh = la.eigh(M)
G_nm1 = mm.H(mm.mmul(Gh, sp.diag(1/sp.sqrt(e) + 0.j)))
G_nm1_i = la.inv(G_nm1)
if G_n_i is None:
G_n_i = G_nm1_i
if self.sanity_checks:
if not sp.allclose(sp.dot(G_nm1, G_nm1_i), sp.eye(G_nm1.shape[0]), atol=1E-13, rtol=1E-13):
print "Sanity Fail in restore_ONR_%u!: Bad GT at n=%u" % (n, n)
for s in xrange(self.q[n]):
self.A[n][s] = mm.mmul(G_nm1, self.A[n][s], G_n_i)
return G_nm1_i, G_nm1
def restore_ONR_n(self, n, G_n_i):
"""Transforms a single A[n] to obtain right orthonormalization.
Implements the condition for right-orthonormalization from sub-section
3.1, theorem 1 of arXiv:quant-ph/0608197v2.
This function must be called for each n in turn, starting at N + 1,
passing the gauge transformation matrix from the previous step
as an argument.
Finds a G[n-1] such that ON_R is fulfilled for n.
Eigenvalues = 0 are a problem here... IOW rank-deficient matrices.
Apparently, they can turn up during a run, but if they do we're screwed.
The fact that M should be positive definite is used to optimize this.
Parameters
----------
n : int
The site number.
G_n_i : ndarray
The inverse gauge transform matrix for site n obtained in the previous step (for n + 1).
Returns
-------
G_n_m1_i : ndarray
The inverse gauge transformation matrix for the site n - 1.
"""
GGh_n_i = m.mmul(
G_n_i, m.H(G_n_i)
) #r[n] does not belong here. The condition is for sum(AA). r[n] = 1 is a consequence.
M = self.eps_r(n, GGh_n_i)
#The following should be more efficient than eigh():
try:
tu = la.cholesky(
M
) #Assumes M is pos. def.. It should raise LinAlgError if not.
G_nm1 = m.H(m.invtr(tu)) #G is now lower-triangular
G_nm1_i = m.H(tu)
except sp.linalg.LinAlgError:
print "restore_ONR_n: Falling back to eigh()!"
e, Gh = la.eigh(M)
G_nm1 = m.H(m.mmul(Gh, sp.diag(1 / sp.sqrt(e) + 0.j)))
G_nm1_i = la.inv(G_nm1)
for s in xrange(self.q[n]):
self.A[n][s] = m.mmul(G_nm1, self.A[n][s], G_n_i)
#It's ok to use the same matrix as out and as an operand here
#since there are > 2 matrices in the chain and it is not the last argument.
return G_nm1_i
def calc_x(self, n, Vsh, sqrt_l, sqrt_r, sqrt_l_inv, sqrt_r_inv):
"""Calculate the parameter matrix x* giving the desired B.
This is equivalent to eqn. (49) of arXiv:1103.0936v2 [cond-mat.str-el] except
that, here, norm-preservation is not enforced, such that the optimal
parameter matrices x*_n (for the parametrization of B) are given by the
derivative w.r.t. x_n of <Phi[B, A]|Ĥ|Psi[A]>, rather than
<Phi[B, A]|Ĥ - H|Psi[A]> (with H = <Psi|Ĥ|Psi>).
An additional sum was added for the single-site hamiltonian.
Some multiplications have been pulled outside of the sums for efficiency.
Direct dependencies:
- A[n - 1], A[n], A[n + 1]
- r[n], r[n + 1], l[n - 2], l[n - 1]
- C[n], C[n - 1]
- K[n + 1]
- V[n]
"""
x = sp.zeros((self.D[n - 1], self.q[n] * self.D[n] - self.D[n - 1]), dtype=self.typ, order=self.odr)
x_part = sp.empty_like(x)
x_subpart = sp.empty_like(self.A[n][0])
x_subsubpart = sp.empty_like(self.A[n][0])
x_part.fill(0)
for s in xrange(self.q[n]):
x_subpart.fill(0)
if n < self.N:
x_subsubpart.fill(0)
for t in xrange(self.q[n + 1]):
x_subsubpart += m.mmul(self.C[n][s,t], self.r[n + 1], m.H(self.A[n + 1][t])) #~1st line
x_subsubpart += m.mmul(self.A[n][s], self.K[n + 1]) #~3rd line
x_subpart += m.mmul(x_subsubpart, sqrt_r_inv)
if not self.h_ext is None:
x_subsubpart.fill(0)
for t in xrange(self.q[n]): #Extra term to take care of h_ext..
x_subsubpart += self.h_ext(n, s, t) * self.A[n][t] #it may be more effecient to squeeze this into the nn term...
x_subpart += m.mmul(x_subsubpart, sqrt_r)
x_part += m.mmul(x_subpart, Vsh[s])
x += m.mmul(sqrt_l, x_part)
if n > 1:
x_part.fill(0)
for s in xrange(self.q[n]): #~2nd line
x_subsubpart.fill(0)
for t in xrange(self.q[n + 1]):
x_subsubpart += m.mmul(m.H(self.A[n - 1][t]), self.l[n - 2], self.C[n - 1][t, s])
x_part += m.mmul(x_subsubpart, sqrt_r, Vsh[s])
x += m.mmul(sqrt_l_inv, x_part)
return x
def restore_RCF_l(self):
G_nm1 = None
l_nm1 = self.l[0]
for n in xrange(self.N + 1):
if n == 0:
x = l_nm1
else:
x = mm.mmul(mm.H(G_nm1), l_nm1, G_nm1)
M = self.eps_l(n, x)
ev, EV = la.eigh(M)
self.l[n] = mm.simple_diag_matrix(ev, dtype=self.typ)
G_n_i = EV
if n == 0:
G_nm1 = mm.H(EV) #for left uniform case
l_nm1 = self.l[n] #for sanity check
self.u_gnd_l.r = mm.mmul(G_nm1, self.u_gnd_l.r, G_n_i) #since r is not eye
for s in xrange(self.q[n]):
self.A[n][s] = mm.mmul(G_nm1, self.A[n][s], G_n_i)
if self.sanity_checks:
l = self.eps_l(n, l_nm1)
if not sp.allclose(l, self.l[n], atol=1E-12, rtol=1E-12):
print "Sanity Fail in restore_RCF_l!: l_%u is bad" % n
print la.norm(l - self.l[n])
G_nm1 = mm.H(EV)
l_nm1 = self.l[n]
if self.sanity_checks:
if not sp.allclose(sp.dot(G_nm1, G_n_i), sp.eye(G_n_i.shape[0]),
atol=1E-12, rtol=1E-12):
print "Sanity Fail in restore_RCF_l!: Bad GT for l_%u" % n
#Now G_nm1 = G_N
G_nm1_i = mm.H(G_nm1)
for s in xrange(self.q[self.N + 1]):
self.A[self.N + 1][s] = mm.mmul(G_nm1, self.A[self.N + 1][s], G_nm1_i)
##This should not be necessary if G_N is really unitary
#self.r[self.N] = mm.mmul(G_nm1, self.r[self.N], mm.H(G_nm1))
#self.r[self.N + 1] = self.r[self.N]
self.u_gnd_r.l[:] = mm.mmul(mm.H(G_nm1_i), self.u_gnd_r.l, G_nm1_i)
self.S_hc = sp.zeros((self.N), dtype=sp.complex128)
for n in xrange(1, self.N + 1):
self.S_hc[n-1] = -sp.sum(self.l[n].diag * sp.log2(self.l[n].diag))
def restore_SCF(self):
X = la.cholesky(self.r, lower=True)
Y = la.cholesky(self.l, lower=False)
U, sv, Vh = la.svd(Y.dot(X))
#s contains the Schmidt coefficients,
lam = sv**2
self.S_hc = - np.sum(lam * sp.log2(lam))
S = m.simple_diag_matrix(sv, dtype=self.typ)
Srt = S.sqrt()
g = m.mmul(Srt, Vh, m.invtr(X, lower=True))
g_i = m.mmul(m.invtr(Y, lower=False), U, Srt)
for s in xrange(self.q):
self.A[s] = m.mmul(g, self.A[s], g_i)
if self.sanity_checks:
Sfull = np.asarray(S)
if not np.allclose(g.dot(g_i), np.eye(self.D)):
print "Sanity check failed! Restore_SCF, bad GT!"
l = m.mmul(m.H(g_i), self.l, g_i)
r = m.mmul(g, self.r, m.H(g))
if not np.allclose(Sfull, l):
print "Sanity check failed: Restorce_SCF, left failed!"
if not np.allclose(Sfull, r):
print "Sanity check failed: Restorce_SCF, right failed!"
l = self.eps_l(Sfull)
r = self.eps_r(Sfull)
if not np.allclose(Sfull, l, rtol=self.itr_rtol*self.check_fac,
atol=self.itr_atol*self.check_fac):
print "Sanity check failed: Restorce_SCF, left bad!"
if not np.allclose(Sfull, r, rtol=self.itr_rtol*self.check_fac,
atol=self.itr_atol*self.check_fac):
print "Sanity check failed: Restorce_SCF, right bad!"
self.l = S
self.r = S
def density_1s(self, n):
"""Returns a reduced density matrix for a single site.
The site number basis is used: rho[s, t]
with 0 <= s, t < q[n].
The state must be up-to-date -- see self.update()!
Parameters
----------
n1 : int
The site number.
Returns
-------
rho : ndarray
Reduced density matrix in the number basis.
"""
rho = sp.empty((self.q[n], self.q[n]), dtype=sp.complex128)
r_n = self.r[n]
r_nm1 = sp.empty_like(self.r[n - 1])
for s in xrange(self.q[n]):
for t in xrange(self.q[n]):
r_nm1 = m.mmul(self.A[n][t], r_n, m.H(self.A[n][s]))
rho[s, t] = m.adot(self.l[n - 1], r_nm1)
return rho
def eps_l(self, n, x):
"""Implements the left epsilon map
Parameters
----------
res : ndarray
A matrix to hold the result (with the same dimensions as l[n]). May be None.
n : int
The site number.
x : ndarray
The argument matrix. For example, using l[n - 1] gives a result l[n]
Returns
-------
res : ndarray
The resulting matrix.
"""
if n > self.N + 1:
n = self.N + 1
elif n < 0:
n = 0
res = sp.zeros_like(self.l[n])
for s in xrange(self.q[n]):
res += mm.mmul(mm.H(self.A[n][s]), x, self.A[n][s])
return res
def density_1s(self, n):
"""Returns a reduced density matrix for a single site.
The site number basis is used: rho[s, t]
with 0 <= s, t < q[n].
The state must be up-to-date -- see self.update()!
Parameters
----------
n1 : int
The site number.
Returns
-------
rho : ndarray
Reduced density matrix in the number basis.
"""
rho = sp.empty((self.q[n], self.q[n]), dtype=sp.complex128)
r_n = self.r[n]
r_nm1 = sp.empty_like(self.r[n - 1])
for s in xrange(self.q[n]):
for t in xrange(self.q[n]):
r_nm1 = m.mmul(self.A[n][t], r_n, m.H(self.A[n][s]))
rho[s, t] = m.adot(self.l[n - 1], r_nm1)
return rho
def calc_C(self, n_low=-1, n_high=-1):
"""Generates the C matrices used to calculate the K's and ultimately the B's
These are to be used on one side of the super-operator when applying the
nearest-neighbour Hamiltonian, similarly to C in eqn. (44) of
arXiv:1103.0936v2 [cond-mat.str-el], except being for the non-norm-preserving case.
Makes use only of the nearest-neighbour hamiltonian, and of the A's.
C[n] depends on A[n] and A[n + 1].
"""
if self.h_nn is None:
return 0
if n_low < 1:
n_low = 1
if n_high < 1:
n_high = self.N
for n in xrange(n_low, n_high):
self.C[n].fill(0)
for u in xrange(self.q[n]):
for v in xrange(self.q[n + 1]):
AA = m.mmul(self.A[n][u], self.A[n + 1][v]) #only do this once for each
for s in xrange(self.q[n]):
for t in xrange(self.q[n + 1]):
h_nn_stuv = self.h_nn(n, s, t, u, v)
if h_nn_stuv != 0:
self.C[n][s, t] += h_nn_stuv * AA
def calc_Vsh(self, n, sqrt_r):
"""Generates m.H(V[n][s]) for a given n, used for generating B[n][s]
This is described on p. 14 of arXiv:1103.0936v2 [cond-mat.str-el] for left
gauge fixing. Here, we are using right gauge fixing.
Array slicing and reshaping is used to manipulate the indices as necessary.
Each V[n] directly depends only on A[n] and r[n].
We return the conjugate m.H(V) because we use it in more places than V.
"""
R = sp.zeros((self.D[n], self.q[n], self.D[n-1]), dtype=self.typ, order='C')
for s in xrange(self.q[n]):
R[:,s,:] = m.mmul(sqrt_r, m.H(self.A[n][s]))
R = R.reshape((self.q[n] * self.D[n], self.D[n-1]))
V = m.H(ns.nullspace_qr(m.H(R)))
#print (q[n]*D[n] - D[n-1], q[n]*D[n])
#print V.shape
#print sp.allclose(mat(V) * mat(V).H, sp.eye(q[n]*D[n] - D[n-1]))
#print sp.allclose(mat(V) * mat(Rh).H, 0)
V = V.reshape((self.q[n] * self.D[n] - self.D[n - 1], self.D[n], self.q[n])) #this works with the above form for R
#prepare for using V[s] and already take the adjoint, since we use it more often
Vsh = sp.empty((self.q[n], self.D[n], self.q[n] * self.D[n] - self.D[n - 1]), dtype=self.typ, order=self.odr)
for s in xrange(self.q[n]):
Vsh[s] = m.H(V[:,:,s])
return Vsh
def eps_l(self, n, x, out=None):
"""Implements the left epsilon map
FIXME: Ref.
Parameters
----------
n : int
The site number.
x : ndarray
The argument matrix. For example, using l[n - 1] gives a result l[n]
out : ndarray
A matrix to hold the result (with the same dimensions as l[n]). May be None.
Returns
-------
res : ndarray
The resulting matrix.
"""
if out is None:
out = sp.zeros_like(self.l[n])
else:
out.fill(0.)
for s in xrange(self.q[n]):
out += m.mmul(m.H(self.A[n][s]), x, self.A[n][s])
return out
def expect_1s_cor(self, o1, o2, n1, n2):
"""Computes the correlation of two single site operators acting on two different sites.
See expect_1s().
n1 must be smaller than n2.
Assumes that the state is normalized.
Parameters
----------
o1 : function
The first operator, acting on the first site.
o2 : function
The second operator, acting on the second site.
n1 : int
The site number of the first site.
n2 : int
The site number of the second site (must be > n1).
"""
r_n = self.eps_r(n2, self.r[n2], o2)
for n in reversed(xrange(n1 + 1, n2)):
r_n = self.eps_r(n, r_n)
r_n = self.eps_r(n1, r_n, o1)
res = m.mmul(self.l[n1 - 1], r_n)
return res.trace()
def calc_B(self, n, set_eta=True):
"""Generates the B[n] tangent vector corresponding to physical evolution of the state.
In other words, this returns B[n][x*] (equiv. eqn. (47) of
arXiv:1103.0936v2 [cond-mat.str-el])
with x* the parameter matrices satisfying the Euler-Lagrange equations
as closely as possible.
In the case of Bc, use the general Bc generated in calc_B_centre().
"""
if n == self.N_centre:
B, eta_c = self.calc_B_centre()
if set_eta:
self.eta[self.N_centre] = eta_c
else:
l_sqrt, r_sqrt, l_sqrt_inv, r_sqrt_inv = self.calc_l_r_roots(n)
if n > self.N_centre:
Vsh = tm.calc_Vsh(self.A[n], r_sqrt, sanity_checks=self.sanity_checks)
x = self.calc_x(n, Vsh, l_sqrt, r_sqrt, l_sqrt_inv, r_sqrt_inv, right=True)
B = sp.empty_like(self.A[n])
for s in xrange(self.q[n]):
B[s] = mm.mmul(l_sqrt_inv, x, mm.H(Vsh[s]), r_sqrt_inv)
if self.sanity_checks:
M = tm.eps_r_noop(self.r[n], B, self.A[n])
if not sp.allclose(M, 0):
print "Sanity Fail in calc_B!: B_%u does not satisfy GFC!" % n
else:
Vsh = tm.calc_Vsh_l(self.A[n], l_sqrt, sanity_checks=self.sanity_checks)
x = self.calc_x(n, Vsh, l_sqrt, r_sqrt, l_sqrt_inv, r_sqrt_inv, right=False)
B = sp.empty_like(self.A[n])
for s in xrange(self.q[n]):
B[s] = mm.mmul(l_sqrt_inv, mm.H(Vsh[s]), x, r_sqrt_inv)
if self.sanity_checks:
M = tm.eps_l_noop(self.l[n - 1], B, self.A[n])
if not sp.allclose(M, 0):
print "Sanity Fail in calc_B!: B_%u does not satisfy GFC!" % n
if set_eta:
self.eta[n] = sp.sqrt(mm.adot(x, x))
return B
def density_1s(self, n):
"""Returns a reduced density matrix for a single site.
Parameters
----------
n1 : int
The site number.
"""
rho = sp.empty((self.q[n], self.q[n]), dtype=sp.complex128)
r_n = self.r[n]
r_nm1 = sp.empty_like(self.r[n - 1])
for s in xrange(self.q[n]):
for t in xrange(self.q[n]):
r_nm1 = m.mmul(self.A[n][t], r_n, m.H(self.A[n][s]))
rho[s, t] = m.mmul(self.l[n - 1], r_nm1).trace()
return rho
def get_B_from_x(self, x, Vsh, l_sqrt_i, r_sqrt_i, out=None):
if out is None:
out = np.zeros_like(self.A)
for s in xrange(self.q):
out[s] = m.mmul(l_sqrt_i, x, m.H(Vsh[s]), r_sqrt_i)
return out
def calc_BHB_prereq(self, tdvp, tdvp2):
"""Calculates prerequisites for the application of the effective Hamiltonian in terms of tangent vectors.
This is called (indirectly) by the self.excite.. functions.
Parameters
----------
tdvp2: EvoMPS_TDVP_Uniform
Second state (may be the same, or another ground state).
Returns
-------
A lot of stuff.
"""
l = tdvp.l[0]
r_ = tdvp2.r[0]
r__sqrt = tdvp2.r_sqrt[0]
r__sqrt_i = tdvp2.r_sqrt_i[0]
A = tdvp.A[0]
A_ = tdvp2.A[0]
#Note: V has ~ D**2 * q**2 elements. We avoid making any copies of it except this one.
# This one is only needed because low-level routines force V_[s] to be contiguous.
# TODO: Store V instead of Vsh in tdvp_uniform too...
V_ = sp.transpose(tdvp2.Vsh[0], axes=(0, 2, 1)).conj().copy(order='C')
if self.ham_sites == 2:
#eyeham = m.eyemat(self.q, dtype=sp.complex128)
eyeham = sp.eye(self.q, dtype=sp.complex128)
#diham = m.simple_diag_matrix(sp.repeat([-tdvp.h_expect.real], self.q))
diham = -tdvp.h_expect.real * sp.eye(self.q, dtype=sp.complex128)
_ham_tp = self.ham_tp + [[diham, eyeham]] #subtract norm dof
Ao1 = get_Aop(A, _ham_tp, 2, conj=False)
AhlAo1 = [tm.eps_l_op_1s(l, A, A, o1.conj().T) for o1, o2 in _ham_tp]
A_o2c = get_Aop(A_, _ham_tp, 1, conj=True)
Ao1c = get_Aop(A, _ham_tp, 0, conj=True)
A_Vr_ho2 = [tm.eps_r_op_1s(r__sqrt, A_, V_, o2) for o1, o2 in _ham_tp]
A_A_o12c = get_A_ops(A_, A_, _ham_tp, conj=True)
A_o1 = get_Aop(A_, _ham_tp, 2, conj=False)
tmp = sp.empty((A_.shape[1], V_.shape[1]), dtype=A.dtype, order='C')
tmp2 = sp.empty((A_.shape[1], A_o2c[0].shape[1]), dtype=A.dtype, order='C')
rhs10 = 0
for al in xrange(len(A_o1)):
tmp2 = tm.eps_r_noop_inplace(r_, A_, A_o2c[al], tmp2)
tmp3 = m.mmul(tmp2, r__sqrt_i)
rhs10 += tm.eps_r_noop_inplace(tmp3, A_o1[al], V_, tmp)
return V_, AhlAo1, A_o2c, Ao1, Ao1c, A_Vr_ho2, A_A_o12c, rhs10, _ham_tp
elif self.ham_sites == 3:
return
def eps_l(self, x, out=None):
if out is None:
out = np.zeros_like(self.A[0])
else:
out.fill(0.)
for s in xrange(self.q):
out += m.mmul(m.H(self.A[s]), x, self.A[s])
return out
def calc_C(self, n_low=-1, n_high=-1):
"""Generates the C matrices used to calculate the K's and ultimately the B's
These are to be used on one side of the super-operator when applying the
nearest-neighbour Hamiltonian, similarly to C in eqn. (44) of
arXiv:1103.0936v2 [cond-mat.str-el], except being for the non-norm-preserving case.
Makes use only of the nearest-neighbour hamiltonian, and of the A's.
C[n] depends on A[n] and A[n + 1].
This calculation can be significantly faster if a matrix form for h_nn
is available. See gen_h_matrix().
"""
if self.h_nn is None:
return 0
if n_low < 1:
n_low = 0
if n_high < 1:
n_high = self.N + 1
if self.h_nn_mat is None:
for n in xrange(n_low, n_high):
self.C[n].fill(0)
for u in xrange(self.q[n]):
for v in xrange(self.q[n + 1]):
AA = mm.mmul(self.A[n][u], self.A[n + 1][v]) #only do this once for each
for s in xrange(self.q[n]):
for t in xrange(self.q[n + 1]):
h_nn_stuv = self.h_nn(n, s, t, u, v)
if h_nn_stuv != 0:
self.C[n][s, t] += h_nn_stuv * AA
else:
dot = sp.dot
for n in xrange(n_low, n_high):
An = self.A[n]
Anp1 = self.A[n + 1]
AA = sp.empty_like(self.C[n])
for u in xrange(self.q[n]):
for v in xrange(self.q[n + 1]):
AA[u, v] = dot(An[u], Anp1[v])
if n == 0: #FIXME: Temp. hack
self.AA0 = AA
elif n == 1:
self.AA1 = AA
res = sp.tensordot(AA, self.h_nn_mat[n], ((0, 1), (2, 3)))
res = sp.rollaxis(res, 3)
res = sp.rollaxis(res, 3)
self.C[n][:] = res
def calc_l_r_roots(self, n):
"""Returns the matrix square roots (and inverses) needed to calculate B.
Hermiticity of l[n] and r[n] is used to speed this up.
If an exception occurs here, it is probably because these matrices
are no longer Hermitian (enough).
If l[n] or r[n] are diagonal or the identity, further optimizations are
used.
"""
try:
l_sqrt = self.l[n - 1].sqrt()
except AttributeError:
l_sqrt, evd = mm.sqrtmh(self.l[n - 1], ret_evd=True)
try:
l_sqrt_inv = l_sqrt.inv()
except AttributeError:
l_sqrt_inv = mm.invmh(l_sqrt, evd=evd)
try:
r_sqrt = self.r[n].sqrt()
except AttributeError:
r_sqrt, evd = mm.sqrtmh(self.r[n], ret_evd=True)
try:
r_sqrt_inv = r_sqrt.inv()
except AttributeError:
r_sqrt_inv = mm.invmh(r_sqrt, evd=evd)
if self.sanity_checks:
if not sp.allclose(mm.mmul(l_sqrt, l_sqrt), self.l[n - 1]):
print "Sanity Fail in calc_l_r_roots: Bad l_sqrt_%u" % (n - 1)
if not sp.allclose(mm.mmul(r_sqrt, r_sqrt), self.r[n]):
print "Sanity Fail in calc_l_r_roots: Bad r_sqrt_%u" % (n)
if not sp.allclose(mm.mmul(l_sqrt, l_sqrt_inv), sp.eye(l_sqrt.shape[0])):
print "Sanity Fail in calc_l_r_roots: Bad l_sqrt_inv_%u" % (n - 1)
if not sp.allclose(mm.mmul(r_sqrt, r_sqrt_inv), sp.eye(r_sqrt.shape[0])):
print "Sanity Fail in calc_l_r_roots: Bad r_sqrt_inv_%u" % (n)
return l_sqrt, r_sqrt, l_sqrt_inv, r_sqrt_inv
def calc_K(self, n_low=-1, n_high=-1):
"""Generates the K matrices used to calculate the B's
K[n] is recursively defined. It depends on C[m] and A[m] for all m >= n.
It directly depends on A[n], A[n + 1], r[n], r[n + 1], C[n] and K[n + 1].
This is equivalent to K on p. 14 of arXiv:1103.0936v2 [cond-mat.str-el], except
that it is for the non-gauge-preserving case, and includes a single-site
Hamiltonian term.
K[1] is, assuming a normalized state, the expectation value H of Ĥ.
Instead of an explicit single-site term here, one could also include the
single-site Hamiltonian in the nearest-neighbour term, which may be more
efficient.
"""
if n_low < 1:
n_low = 1
if n_high < 1:
n_high = self.N + 1
for n in reversed(xrange(n_low, n_high)):
self.K[n].fill(0)
if n < self.N:
for s in xrange(self.q[n]):
for t in xrange(self.q[n+1]):
self.K[n] += m.mmul(self.C[n][s, t],
self.r[n + 1], m.H(self.A[n+1][t]),
m.H(self.A[n][s]))
self.K[n] += m.mmul(self.A[n][s], self.K[n + 1],
m.H(self.A[n][s]))
if not self.h_ext is None:
for s in xrange(self.q[n]):
for t in xrange(self.q[n]):
h_ext_st = self.h_ext(n, s, t)
if h_ext_st != 0:
self.K[n] += h_ext_st * m.mmul(self.A[n][t],
self.r[n], m.H(self.A[n][s]))
def eps_r(self, x, A1=None, A2=None, op=None, out=None):
"""Implements the right epsilon map
FIXME: Ref.
Parameters
----------
op : function
The single-site operator to use.
out : ndarray
A matrix to hold the result (with the same dimensions as r).
x : ndarray
The argument matrix.
Returns
-------
res : ndarray
The resulting matrix.
"""
if out is None:
out = np.zeros_like(self.A[0])
else:
out.fill(0.)
if A1 is None:
A1 = self.A
if A2 is None:
A2 = self.A
if op is None:
for s in xrange(self.q):
out += m.mmul(A1[s], x, m.H(A2[s]))
else:
for s in xrange(self.q):
for t in xrange(self.q):
o_st = op(s, t)
if o_st != 0.:
tmp = m.mmul(A1[t], x, m.H(A2[s]))
tmp *= o_st
out += tmp
return out
def calc_B(self, n, set_eta=True):
"""Generates the B[n] tangent vector corresponding to physical evolution of the state.
In other words, this returns B[n][x*] (equiv. eqn. (47) of
arXiv:1103.0936v2 [cond-mat.str-el])
with x* the parameter matrices satisfying the Euler-Lagrange equations
as closely as possible.
In the case of B1, use the general B1 generated in calc_B1().
"""
if self.q[n] * self.D[n] - self.D[n - 1] > 0:
if n == 1:
B, eta1 = self.calc_B1()
if set_eta:
self.eta[1] = eta1
else:
l_sqrt, r_sqrt, l_sqrt_inv, r_sqrt_inv = self.calc_l_r_roots(n)
Vsh = self.calc_Vsh(n, r_sqrt)
x = self.calc_opt_x(n, Vsh, l_sqrt, r_sqrt, l_sqrt_inv, r_sqrt_inv)
if set_eta:
self.eta[n] = sp.sqrt(mm.adot(x, x))
B = sp.empty_like(self.A[n])
for s in xrange(self.q[n]):
B[s] = mm.mmul(l_sqrt_inv, x, mm.H(Vsh[s]), r_sqrt_inv)
if self.sanity_checks:
M = sp.zeros_like(self.r[n - 1])
for s in xrange(self.q[n]):
M += mm.mmul(B[s], self.r[n], mm.H(self.A[n][s]))
if not sp.allclose(M, 0):
print "Sanity Fail in calc_B!: B_%u does not satisfy GFC!" % n
return B
else:
return None, 0
def calc_Vsh(self, n, sqrt_r):
"""Generates mm.H(V[n][s]) for a given n, used for generating B[n][s]
This is described on p. 14 of arXiv:1103.0936v2 [cond-mat.str-el] for left
gauge fixing. Here, we are using right gauge fixing.
Array slicing and reshaping is used to manipulate the indices as necessary.
Each V[n] directly depends only on A[n] and r[n].
We return the conjugate mm.H(V) because we use it in more places than V.
"""
R = sp.zeros((self.D[n], self.q[n], self.D[n-1]), dtype=self.typ, order='C')
for s in xrange(self.q[n]):
R[:,s,:] = mm.mmul(sqrt_r, mm.H(self.A[n][s]))
R = R.reshape((self.q[n] * self.D[n], self.D[n-1]))
Vconj = ns.nullspace_qr(mm.H(R)).T
if self.sanity_checks:
if not sp.allclose(mm.mmul(Vconj.conj(), R), 0):
print "Sanity Fail in calc_Vsh!: VR_%u != 0" % (n)
if not sp.allclose(mm.mmul(Vconj, mm.H(Vconj)), sp.eye(Vconj.shape[0])):
print "Sanity Fail in calc_Vsh!: V H(V)_%u != eye" % (n)
Vconj = Vconj.reshape((self.q[n] * self.D[n] - self.D[n - 1], self.D[n], self.q[n]))
Vsh = Vconj.T
Vsh = sp.asarray(Vsh, order='C')
if self.sanity_checks:
M = sp.zeros((self.q[n] * self.D[n] - self.D[n - 1], self.D[n]), dtype=self.typ)
for s in xrange(self.q[n]):
M += mm.mmul(mm.H(Vsh[s]), sqrt_r, mm.H(self.A[n][s]))
if not sp.allclose(M, 0):
print "Sanity Fail in calc_Vsh!: Bad Vsh_%u" % (n)
return Vsh
def calc_x(self, l_sqrt, l_sqrt_i, r_sqrt, r_sqrt_i, Vsh, out=None):
if out is None:
out = np.zeros((self.D, (self.q - 1) * self.D), dtype=self.typ,
order=self.odr)
tmp = np.zeros_like(out)
for s in xrange(self.q):
tmp2 = m.mmul(self.A[s], self.K)
for t in xrange(self.q):
tmp2 += m.mmul(self.C[s, t], self.r, m.H(self.A[t]))
tmp += m.mmul(tmp2, r_sqrt_i, Vsh[s])
out += l_sqrt.dot(tmp)
tmp.fill(0)
for s in xrange(self.q):
tmp2.fill(0)
for t in xrange(self.q):
tmp2 += m.mmul(m.H(self.A[t]), self.l, self.C[t, s])
tmp += m.mmul(tmp2, r_sqrt, Vsh[s])
out += l_sqrt_i.dot(tmp)
return out
def eps_r(self, n, x, o=None, out=None):
"""Implements the right epsilon map
FIXME: Ref.
Parameters
----------
n : int
The site number.
x : ndarray
The argument matrix. For example, using r[n] (and o=None) gives a result r[n - 1]
o : function
The single-site operator to use. May be None.
out : ndarray
A matrix to hold the result (with the same dimensions as r[n - 1]). May be None.
Returns
-------
res : ndarray
The resulting matrix.
"""
if out is None:
out = sp.zeros((self.D[n - 1], self.D[n - 1]), dtype=self.typ)
else:
out.fill(0)
if o is None:
for s in xrange(self.q[n]):
out += m.mmul(self.A[n][s], x, m.H(self.A[n][s]))
else:
for s in xrange(self.q[n]):
for t in xrange(self.q[n]):
o_st = o(n, s, t)
if o_st != 0.:
tmp = m.mmul(self.A[n][t], x, m.H(self.A[n][s]))
tmp *= o_st
out += tmp
return out
def restore_RCF_r(self):
G_n_i = None
for n in reversed(xrange(1, self.N + 2)):
G_n_i, G_n = self.restore_ONR_n(n, G_n_i)
self.r[n - 1] = mm.eyemat(self.D[n - 1], self.typ)
if self.sanity_checks:
r_n = mm.eyemat(self.D[n], self.typ)
r_nm1 = self.eps_r(n, r_n)
if not sp.allclose(r_nm1, self.r[n - 1].A, atol=1E-13, rtol=1E-13):
print "Sanity Fail in restore_RCF_r!: r_%u is bad" % (n - 1)
print la.norm(r_nm1 - self.r[n - 1])
#self.r[self.N + 1] = self.r[self.N]
#Now G_n_i contains g_0_i
for s in xrange(self.q[0]): #Note: This does not change the scale of A[0]
self.A[0][s] = mm.mmul(G_n, self.A[0][s], G_n_i)
self.u_gnd_l.r = mm.mmul(G_n, self.u_gnd_l.r, mm.H(G_n))
self.l[0] = mm.mmul(mm.H(G_n_i), self.l[0], G_n_i)
def calc_l(self, start=-1, finish=-1):
"""Updates the l matrices using the current state.
Implements step 5 of the TDVP algorithm or, equivalently, eqn. (41).
(arXiv:1103.0936v2 [cond-mat.str-el])
"""
if start < 0:
start = 1
if finish < 0:
finish = self.N
for n in xrange(start, finish + 1):
self.l[n].fill(0)
for s in xrange(self.q[n]):
self.l[n] += m.mmul(m.H(self.A[n][s]), self.l[n - 1], self.A[n][s])
def density_2s(self, n1, n2):
"""Returns a reduced density matrix for a pair of (seperated) sites.
The site number basis is used: rho[s * q[n1] + u, t * q[n1] + v]
with 0 <= s, t < q[n1] and 0 <= u, v < q[n2].
The state must be up-to-date -- see self.update()!
Parameters
----------
n1 : int
The site number of the first site.
n2 : int
The site number of the second site (must be > n1).
Returns
-------
rho : ndarray
Reduced density matrix in the number basis.
"""
rho = sp.empty((self.q[n1] * self.q[n2], self.q[n1] * self.q[n2]),
dtype=sp.complex128)
for s2 in xrange(self.q[n2]):
for t2 in xrange(self.q[n2]):
r_n2 = m.mmul(self.A[n2][t2], self.r[n2], m.H(self.A[n2][s2]))
r_n = r_n2
for n in reversed(xrange(n1 + 1, n2)):
r_n = tm.eps_r_noop(r_n, self.A[n], self.A[n])
for s1 in xrange(self.q[n1]):
for t1 in xrange(self.q[n1]):
r_n1 = m.mmul(self.A[n1][t1], r_n, m.H(self.A[n1][s1]))
tmp = m.adot(self.l[n1 - 1], r_n1)
rho[s1 * self.q[n1] + s2, t1 * self.q[n1] + t2] = tmp
return rho
def density_1s(self):
"""Returns a reduced density matrix for a single site.
The site number basis is used: rho[s, t]
with 0 <= s, t < q.
The state must be up-to-date -- see self.update()!
Returns
-------
rho : ndarray
Reduced density matrix in the number basis.
"""
rho = np.empty((self.q, self.q), dtype=self.typ)
for s in xrange(self.q):
for t in xrange(self.q):
rho[s, t] = m.adot(self.l, m.mmul(self.A[t], self.r, m.H(self.A[s])))
return rho
def _calc_B_r_diss(self, op, K, C, n, set_eta=True):
if self.q[n] * self.D[n] - self.D[n - 1] > 0:
l_sqrt, l_sqrt_inv, r_sqrt, r_sqrt_inv = tm.calc_l_r_roots(
self.l[n - 1],
self.r[n],
sanity_checks=self.sanity_checks,
sc_data=("site", n))
Vsh = tm.calc_Vsh(self.A[n],
r_sqrt,
sanity_checks=self.sanity_checks)
x = self.calc_x(n, Vsh, l_sqrt, r_sqrt, l_sqrt_inv, r_sqrt_inv)
if set_eta:
self.eta[n] = sp.sqrt(mm.adot(x, x))
B = sp.empty_like(self.A[n])
for s in xrange(self.q[n]):
B[s] = mm.mmul(l_sqrt_inv, x, mm.H(Vsh[s]), r_sqrt_inv)
return B
else:
return None
def expect_2s_diss(self, op, LC, LK, n, AA=None):
"""Applies a two-site operator to two sites and returns
the value after the change. In contrast to
mps_gen.apply_op_2s, this routine does not change the state itself.
Also, this does not perform self.update().
Parameters
----------
op : ndarray or callable
The two-site operator. See self.expect_2s().
n: int
The site to apply the operator to.
(It's also applied to n-1.)
"""
#No neighbors, no fun.
if n is 1:
return 0
if n is N:
return 0
A = self.A[n - 1]
Ap1 = self.A[n]
if AA is None:
AA = tm.calc_AA(A, Ap1)
if callable(op):
op = sp.vectorize(op, otypes=[sp.complex128])
op = sp.fromfunction(
op, (A.shape[0], Ap1.shape[0], A.shape[0], Ap1.shape[0]))
op = op.reshape(4, 4, 4, 4)
C = tm.calc_C_mat_op_AA(op, AA)
res = tm.eps_r_op_2s_C12_AA34(self.r[n + 1], LC, AA)
operand = self.l[n - 1]
operand = sp.reshape(operand, (1, 16))
operand = sp.reshape(operand, (2, 8))
return mm.mmul(operand, res)
return mm.adot(self.l[n - 1], res)
def _calc_B_l(self, n, set_eta=True):
if self.q[n] * self.D[n - 1] - self.D[n] > 0:
l_sqrt, l_sqrt_inv, r_sqrt, r_sqrt_inv = tm.calc_l_r_roots(self.l[n - 1],
self.r[n],
zero_tol=self.zero_tol,
sanity_checks=self.sanity_checks,
sc_data=('site', n))
Vsh = tm.calc_Vsh_l(self.A[n], l_sqrt, sanity_checks=self.sanity_checks)
x = self.calc_x_l(n, Vsh, l_sqrt, r_sqrt, l_sqrt_inv, r_sqrt_inv)
if set_eta:
self.eta[n] = sp.sqrt(m.adot(x, x))
B = sp.empty_like(self.A[n])
for s in xrange(self.q[n]):
B[s] = m.mmul(l_sqrt_inv, m.H(Vsh[s]), x, r_sqrt_inv)
return B
else:
return None
def calc_BHB(self,
x,
p,
tdvp,
tdvp2,
prereq,
M_prev=None,
y_pi_prev=None,
pinv_solver=None):
if pinv_solver is None:
pinv_solver = las.gmres
if self.ham_sites == 3:
return
else:
V_, AhlAo1, A_o2c, Ao1, Ao1c, A_Vr_ho2, A_A_o12c, rhs10, _ham_tp = prereq
A = tdvp.A[0]
A_ = tdvp2.A[0]
l = tdvp.l[0]
r_ = tdvp2.r[0]
l_sqrt = tdvp.l_sqrt[0]
l_sqrt_i = tdvp.l_sqrt_i[0]
r__sqrt = tdvp2.r_sqrt[0]
r__sqrt_i = tdvp2.r_sqrt_i[0]
K__r = tdvp2.K[0]
K_l = tdvp.K_left[0]
pseudo = tdvp2 is tdvp
B = tdvp2.get_B_from_x(x, tdvp2.Vsh[0], l_sqrt_i, r__sqrt_i)
#Skip zeros due to rank-deficiency
if la.norm(B) == 0:
return sp.zeros_like(x), M_prev, y_pi_prev
if self.sanity_checks:
tst = tm.eps_r_noop(r_, B, A_)
if not la.norm(tst) > self.sanity_tol:
log.warning("Sanity check failed: Gauge-fixing violation! " +
str(la.norm(tst)))
if self.sanity_checks:
B2 = np.zeros_like(B)
for s in xrange(self.q):
B2[s] = l_sqrt_i.dot(x.dot(m.mmul(V_[s], r__sqrt_i)))
if la.norm(B - B2) / la.norm(B) > self.sanity_tol:
log.warning("Sanity Fail in calc_BHB! Bad Vri!")
y = tm.eps_l_noop(l, B, A)
# if pseudo:
# y = y - m.adot(r_, y) * l #should just = y due to gauge-fixing
M = pinv_1mE(y, [A_], [A],
l,
r_,
p=-p,
left=True,
pseudo=pseudo,
out=M_prev,
tol=self.pinv_tol,
solver=pinv_solver,
use_CUDA=self.pinv_CUDA,
CUDA_use_batch=self.pinv_CUDA_batch,
sanity_checks=self.sanity_checks,
sc_data='M')
#print m.adot(r, M)
if self.sanity_checks:
y2 = M - sp.exp(+1.j * p) * tm.eps_l_noop(M, A_, A)
norm = la.norm(y.ravel())
if norm == 0:
norm = 1
tst = la.norm(y - y2) / norm
if tst > self.sanity_tol:
log.warning("Sanity Fail in calc_BHB! Bad M. Off by: %g", tst)
# if pseudo:
# M = M - l * m.adot(r_, M)
Mh = M.conj().T.copy(order='C')
res_ls = 0
res_lsi = 0
exp = sp.exp
if self.ham_sites == 3:
pass
else:
Bo1 = get_Aop(B, _ham_tp, 2, conj=False)
tmp = sp.empty((B.shape[1], V_.shape[1]), dtype=A.dtype, order='C')
tmp2 = sp.empty((A_.shape[1], A_o2c[0].shape[1]),
dtype=A.dtype,
order='C')
tmp3 = sp.empty_like(tmp2, order='C')
for al in xrange(len(Bo1)):
tmp3 = m.dot_inplace(
tm.eps_r_noop_inplace(r_, A_, A_o2c[al], tmp2), r__sqrt_i,
tmp3)
res_ls += tm.eps_r_noop_inplace(tmp3, Bo1[al], V_, tmp) #1
tmp3 = m.dot_inplace(
tm.eps_r_noop_inplace(r_, B, A_o2c[al], tmp2), r__sqrt_i,
tmp3)
tmp = tm.eps_r_noop_inplace(tmp3, Ao1[al], V_, tmp) #3
tmp *= exp(+1.j * p)
res_ls += tmp
del (tmp)
del (tmp2)
del (tmp3)
res_lsi += sp.exp(-1.j * p) * Mh.dot(rhs10) #10
if self.ham_sites == 3:
pass
else:
Bo2 = get_Aop(B, _ham_tp, 3, conj=False)
for al in xrange(len(AhlAo1)):
res_lsi += AhlAo1[al].dot(tm.eps_r_noop(r__sqrt, Bo2[al],
V_)) #2
res_lsi += exp(-1.j * p) * tm.eps_l_noop(l, Ao1c[al], B).dot(
A_Vr_ho2[al]) #4
res_lsi += exp(-2.j * p) * tm.eps_l_noop(Mh, Ao1c[al], A_).dot(
A_Vr_ho2[al]) #12
K__rri = m.mmul(K__r, r__sqrt_i)
res_ls += tm.eps_r_noop(K__rri, B, V_) #5
res_lsi += K_l.dot(tm.eps_r_noop(r__sqrt, B, V_)) #6
res_lsi += sp.exp(-1.j * p) * Mh.dot(tm.eps_r_noop(K__rri, A_, V_)) #8
y1 = sp.exp(+1.j * p) * tm.eps_r_noop(K__r, B, A_) #7
if self.ham_sites == 3:
pass
elif self.ham_sites == 2:
tmp = 0
for al in xrange(len(A_A_o12c)):
tmp += sp.exp(+1.j * p) * tm.eps_r_noop(
tm.eps_r_noop(r_, A_, A_A_o12c[al][1]), B,
A_A_o12c[al][0]) #9
tmp += sp.exp(+2.j * p) * tm.eps_r_noop(
tm.eps_r_noop(r_, B, A_A_o12c[al][1]), A,
A_A_o12c[al][0]) #11
y = y1 + tmp #7, 9, 11
del (tmp)
if pseudo:
y = y - m.adot(l, y) * r_
y_pi = pinv_1mE(y, [A], [A_],
l,
r_,
p=p,
left=False,
pseudo=pseudo,
out=y_pi_prev,
tol=self.pinv_tol,
solver=pinv_solver,
use_CUDA=self.pinv_CUDA,
CUDA_use_batch=self.pinv_CUDA_batch,
sanity_checks=self.sanity_checks,
sc_data='y_pi')
#print m.adot(l, y_pi)
if self.sanity_checks:
z = y_pi - sp.exp(+1.j * p) * tm.eps_r_noop(y_pi, A, A_)
tst = la.norm((y - z).ravel()) / la.norm(y.ravel())
if tst > self.sanity_tol:
log.warning("Sanity Fail in calc_BHB! Bad x_pi. Off by: %g",
tst)
res_ls += tm.eps_r_noop(m.mmul(y_pi, r__sqrt_i), A, V_)
res = l_sqrt.dot(res_ls)
res += l_sqrt_i.dot(res_lsi)
if self.sanity_checks:
expval = m.adot(x, res) / m.adot(x, x)
#print "expval = " + str(expval)
if expval < -self.sanity_tol:
log.warning(
"Sanity Fail in calc_BHB! H is not pos. semi-definite (%s)",
expval)
if abs(expval.imag) > self.sanity_tol:
log.warning("Sanity Fail in calc_BHB! H is not Hermitian (%s)",
expval)
return res, M, y_pi
def calc_B(self, n, set_eta=True):
"""Generates the B[n] tangent vector corresponding to physical evolution of the state.
In other words, this returns B[n][x*] (equiv. eqn. (47) of
arXiv:1103.0936v2 [cond-mat.str-el])
with x* the parameter matrices satisfying the Euler-Lagrange equations
as closely as possible.
In the case of Bc, use the general Bc generated in calc_B_centre().
"""
if n == self.N_centre:
B, eta_c = self.calc_B_centre()
if set_eta:
self.eta[self.N_centre] = eta_c
else:
l_sqrt, r_sqrt, l_sqrt_inv, r_sqrt_inv = self.calc_l_r_roots(n)
if n > self.N_centre:
Vsh = tm.calc_Vsh(self.A[n],
r_sqrt,
sanity_checks=self.sanity_checks)
x = self.calc_x(n,
Vsh,
l_sqrt,
r_sqrt,
l_sqrt_inv,
r_sqrt_inv,
right=True)
B = sp.empty_like(self.A[n])
for s in xrange(self.q[n]):
B[s] = mm.mmul(l_sqrt_inv, x, mm.H(Vsh[s]), r_sqrt_inv)
if self.sanity_checks:
M = tm.eps_r_noop(self.r[n], B, self.A[n])
if not sp.allclose(M, 0):
print "Sanity Fail in calc_B!: B_%u does not satisfy GFC!" % n
else:
Vsh = tm.calc_Vsh_l(self.A[n],
l_sqrt,
sanity_checks=self.sanity_checks)
x = self.calc_x(n,
Vsh,
l_sqrt,
r_sqrt,
l_sqrt_inv,
r_sqrt_inv,
right=False)
B = sp.empty_like(self.A[n])
for s in xrange(self.q[n]):
B[s] = mm.mmul(l_sqrt_inv, mm.H(Vsh[s]), x, r_sqrt_inv)
if self.sanity_checks:
M = tm.eps_l_noop(self.l[n - 1], B, self.A[n])
if not sp.allclose(M, 0):
print "Sanity Fail in calc_B!: B_%u does not satisfy GFC!" % n
if set_eta:
self.eta[n] = sp.sqrt(mm.adot(x, x))
return B
def restore_RCF(self, update_l=True, normalize=True, diag_l=True):
"""Use a gauge-transformation to restore right canonical form.
Implements the conditions for right canonical form from sub-section
3.1, theorem 1 of arXiv:quant-ph/0608197v2.
This performs two 'almost' gauge transformations, where the 'almost'
means we allow the norm to vary (if "normalize" = True).
The last step (A[1]) is done diffently to the others since G[0],
the gauge-transf. matrix, is just a number, which can be found more
efficiently and accurately without using matrix methods.
The last step (A[1]) is important because, if we have successfully made
r[1] = 1 in the previous steps, it fully determines the normalization
of the state via r[0] ( = l[N]).
Optionally (normalize=False), the function will not attempt to make
A[1] satisfy the orthonorm. condition, and will take G[0] = 1 = G[N],
thus performing a pure gauge-transformation, but not ensuring complete
canonical form.
It is also possible to begin the process from a site n other than N,
in case the sites > n are known to be in the desired form already.
It is also possible to skip the diagonalization of the l's, such that
only the right orthonormalization condition (r_n = eye) is met.
By default, the l's are updated even if diag_l=False.
Parameters
----------
update_l : bool
Whether to call calc_l() after completion (defaults to True)
normalize : bool
Whether to also attempt to normalize the state.
diag_l : bool
Whether to put l in diagonal form (defaults to True)
"""
start = self.N
G_n_i = sp.eye(self.D[start],
dtype=self.typ) #This is actually just the number 1
for n in xrange(start, 1, -1):
self.r[n - 1], G_n, G_n_i = tm.restore_RCF_r(
self.A[n],
self.r[n],
G_n_i,
sc_data=('site', n),
zero_tol=self.zero_tol,
sanity_checks=self.sanity_checks)
#Now do A[1]...
#Apply the remaining G[1]^-1 from the previous step.
for s in xrange(self.q[1]):
self.A[1][s] = m.mmul(self.A[1][s], G_n_i)
#Now finish off
tm.eps_r_noop_inplace(self.r[1], self.A[1], self.A[1], out=self.r[0])
if normalize:
G0 = 1. / sp.sqrt(self.r[0].squeeze().real)
self.A[1] *= G0
self.r[0][:] = 1
if self.sanity_checks:
r0 = tm.eps_r_noop(self.r[1], self.A[1], self.A[1])
if not sp.allclose(r0, 1, atol=1E-12, rtol=1E-12):
log.warning(
"Sanity Fail in restore_RCF!: r_0 is bad / norm failure"
)
if diag_l:
G_nm1 = sp.eye(self.D[0], dtype=self.typ)
for n in xrange(1, self.N):
self.l[n], G_nm1, G_nm1_i = tm.restore_RCF_l(
self.A[n], self.l[n - 1], G_nm1, self.sanity_checks)
#Apply remaining G_Nm1 to A[N]
n = self.N
for s in xrange(self.q[n]):
self.A[n][s] = m.mmul(G_nm1, self.A[n][s])
#Deal with final, scalar l[N]
tm.eps_l_noop_inplace(self.l[n - 1],
self.A[n],
self.A[n],
out=self.l[n])
if self.sanity_checks:
if not sp.allclose(
self.l[self.N].real, 1, atol=1E-12, rtol=1E-12):
log.warning(
"Sanity Fail in restore_RCF!: l_N is bad / norm failure"
)
log.warning("l_N = %s", self.l[self.N].squeeze().real)
for n in xrange(1, self.N + 1):
r_nm1 = tm.eps_r_noop(self.r[n], self.A[n], self.A[n])
#r_nm1 = tm.eps_r_noop(m.eyemat(self.D[n], self.typ), self.A[n], self.A[n])
if not sp.allclose(
r_nm1, self.r[n - 1], atol=1E-11, rtol=1E-11):
log.warning(
"Sanity Fail in restore_RCF!: r_%u is bad (off by %g)",
n, la.norm(r_nm1 - self.r[n - 1]))
elif update_l:
self.calc_l()
def restore_SCF(self, ret_g=False, zero_tol=None):
"""Restores symmetric canonical form.
In this canonical form, self.l == self.r and are diagonal matrices
with the Schmidt coefficients corresponding to the half-chain
decomposition form the diagonal entries.
Parameters
----------
ret_g : bool
Whether to return the gauge-transformation matrices used.
Returns
-------
g, g_i : ndarray
Gauge transformation matrix g and its inverse g_i.
"""
if zero_tol is None:
zero_tol = self.zero_tol
X, Xi = tm.herm_fac_with_inv(self.r, lower=True, zero_tol=zero_tol)
Y, Yi = tm.herm_fac_with_inv(self.l, lower=False, zero_tol=zero_tol)
U, sv, Vh = la.svd(Y.dot(X))
#s contains the Schmidt coefficients,
lam = sv**2
self.S_hc = - np.sum(lam * sp.log2(lam))
S = m.simple_diag_matrix(sv, dtype=self.typ)
Srt = S.sqrt()
g = m.mmul(Srt, Vh, Xi)
g_i = m.mmul(Yi, U, Srt)
for s in xrange(self.q):
self.A[s] = m.mmul(g, self.A[s], g_i)
if self.sanity_checks:
Sfull = np.asarray(S)
if not np.allclose(g.dot(g_i), np.eye(self.D)):
log.warning("Sanity check failed! Restore_SCF, bad GT!")
l = m.mmul(m.H(g_i), self.l, g_i)
r = m.mmul(g, self.r, m.H(g))
if not np.allclose(Sfull, l):
log.warning("Sanity check failed: Restorce_SCF, left failed!")
if not np.allclose(Sfull, r):
log.warning("Sanity check failed: Restorce_SCF, right failed!")
l = tm.eps_l_noop(Sfull, self.A, self.A)
r = tm.eps_r_noop(Sfull, self.A, self.A)
if not np.allclose(Sfull, l, rtol=self.itr_rtol*self.check_fac,
atol=self.itr_atol*self.check_fac):
log.warning("Sanity check failed: Restorce_SCF, left bad!")
if not np.allclose(Sfull, r, rtol=self.itr_rtol*self.check_fac,
atol=self.itr_atol*self.check_fac):
log.warning("Sanity check failed: Restorce_SCF, right bad!")
self.l = S
self.r = S
if ret_g:
return g, g_i
else:
return
def calc_BHB_prereq(self, tdvp, tdvp2):
"""Calculates prerequisites for the application of the effective Hamiltonian in terms of tangent vectors.
This is called (indirectly) by the self.excite.. functions.
Parameters
----------
tdvp2: EvoMPS_TDVP_Uniform
Second state (may be the same, or another ground state).
Returns
-------
A lot of stuff.
"""
l = tdvp.l[0]
r_ = tdvp2.r[0]
r__sqrt = tdvp2.r_sqrt[0]
r__sqrt_i = tdvp2.r_sqrt_i[0]
A = tdvp.A[0]
A_ = tdvp2.A[0]
#Note: V has ~ D**2 * q**2 elements. We avoid making any copies of it except this one.
# This one is only needed because low-level routines force V_[s] to be contiguous.
# TODO: Store V instead of Vsh in tdvp_uniform too...
V_ = sp.transpose(tdvp2.Vsh[0], axes=(0, 2, 1)).conj().copy(order='C')
if self.ham_sites == 2:
#eyeham = m.eyemat(self.q, dtype=sp.complex128)
eyeham = sp.eye(self.q, dtype=sp.complex128)
#diham = m.simple_diag_matrix(sp.repeat([-tdvp.h_expect.real], self.q))
diham = -tdvp.h_expect.real * sp.eye(self.q, dtype=sp.complex128)
_ham_tp = self.ham_tp + [[diham, eyeham]] #subtract norm dof
Ao1 = get_Aop(A, _ham_tp, 2, conj=False)
AhlAo1 = [
tm.eps_l_op_1s(l, A, A,
o1.conj().T) for o1, o2 in _ham_tp
]
A_o2c = get_Aop(A_, _ham_tp, 1, conj=True)
Ao1c = get_Aop(A, _ham_tp, 0, conj=True)
A_Vr_ho2 = [
tm.eps_r_op_1s(r__sqrt, A_, V_, o2) for o1, o2 in _ham_tp
]
A_A_o12c = get_A_ops(A_, A_, _ham_tp, conj=True)
A_o1 = get_Aop(A_, _ham_tp, 2, conj=False)
tmp = sp.empty((A_.shape[1], V_.shape[1]),
dtype=A.dtype,
order='C')
tmp2 = sp.empty((A_.shape[1], A_o2c[0].shape[1]),
dtype=A.dtype,
order='C')
rhs10 = 0
for al in xrange(len(A_o1)):
tmp2 = tm.eps_r_noop_inplace(r_, A_, A_o2c[al], tmp2)
tmp3 = m.mmul(tmp2, r__sqrt_i)
rhs10 += tm.eps_r_noop_inplace(tmp3, A_o1[al], V_, tmp)
return V_, AhlAo1, A_o2c, Ao1, Ao1c, A_Vr_ho2, A_A_o12c, rhs10, _ham_tp
elif self.ham_sites == 3:
return
def restore_RCF(self, ret_g=False, zero_tol=None):
"""Restores right canonical form.
In this form, self.r = sp.eye(self.D) and self.l is diagonal, with
the squared Schmidt coefficients corresponding to the half-chain
decomposition as eigenvalues.
Parameters
----------
ret_g : bool
Whether to return the gauge-transformation matrices used.
Returns
-------
g, g_i : ndarray
Gauge transformation matrix g and its inverse g_i.
"""
if zero_tol is None:
zero_tol = self.zero_tol
#First get G such that r = eye
G, G_i, rank = tm.herm_fac_with_inv(self.r, lower=True, zero_tol=zero_tol,
return_rank=True)
self.l = m.mmul(m.H(G), self.l, G)
#Now bring l into diagonal form, trace = 1 (guaranteed by r = eye..?)
ev, EV = la.eigh(self.l)
G = G.dot(EV)
G_i = m.H(EV).dot(G_i)
for s in xrange(self.q):
self.A[s] = m.mmul(G_i, self.A[s], G)
#ev contains the squares of the Schmidt coefficients,
self.S_hc = - np.sum(ev * sp.log2(ev))
self.l = m.simple_diag_matrix(ev, dtype=self.typ)
r_old = self.r
if rank == self.D:
self.r = m.eyemat(self.D, self.typ)
else:
self.r = sp.zeros((self.D), dtype=self.typ)
self.r[-rank:] = 1
self.r = m.simple_diag_matrix(self.r, dtype=self.typ)
if self.sanity_checks:
r_ = m.mmul(G_i, r_old, m.H(G_i))
if not np.allclose(self.r, r_,
rtol=self.itr_rtol*self.check_fac,
atol=self.itr_atol*self.check_fac):
log.warning("Sanity check failed: RestoreRCF, bad r (bad GT).")
l = tm.eps_l_noop(self.l, self.A, self.A)
r = tm.eps_r_noop(self.r, self.A, self.A)
if not np.allclose(r, self.r,
rtol=self.itr_rtol*self.check_fac,
atol=self.itr_atol*self.check_fac):
log.warning("Sanity check failed: Restore_RCF, r not eigenvector! %s", la.norm(r - self.r))
if not np.allclose(l, self.l,
rtol=self.itr_rtol*self.check_fac,
atol=self.itr_atol*self.check_fac):
log.warning("Sanity check failed: Restore_RCF, l not eigenvector! %s", la.norm(l - self.l))
if ret_g:
return G, G_i
else:
return