def calc_B1(self):
"""Calculate the optimal B1 given right gauge-fixing on B2..N and
no gauge-fixing on B1.
We use the non-norm-preserving K's, since the norm-preservation
is not needed elsewhere. It is cleaner to subtract the relevant
norm-changing terms from the K's here than to generate all K's
with norm-preservation.
"""
B1 = sp.empty_like(self.A[1])
try:
r1_i = self.r[1].inv()
except AttributeError:
r1_i = mm.invmh(self.r[1])
try:
l0_i = self.l[0].inv()
except AttributeError:
l0_i = mm.invmh(self.l[0])
A0 = self.A[0]
A1 = self.A[1]
A2 = self.A[2]
r1 = self.r[1]
r2 = self.r[2]
l0 = self.l[0]
KLh = mm.H(self.u_gnd_l.K_left - l0 * mm.adot(self.u_gnd_l.K_left, self.r[0]))
K2 = self.K[2] - r1 * mm.adot(self.l[1], self.K[2])
C1 = self.C[1] - self.h_expect[1] * self.AA1
C0 = self.C[0] - self.h_expect[0] * self.AA0
for s in xrange(self.q[1]):
try:
B1[s] = A1[s].dot(r1_i.dot_left(K2))
except AttributeError:
B1[s] = A1[s].dot(K2.dot(r1_i))
for t in xrange(self.q[2]):
try:
B1[s] += C1[s, t].dot(r2.dot(r1_i.dot_left(mm.H(A2[t]))))
except AttributeError:
B1[s] += C1[s, t].dot(r2.dot(mm.H(A2[t]).dot(r1_i)))
B1sbit = KLh.dot(A1[s])
for t in xrange(self.q[0]):
B1sbit += mm.H(A0[t]).dot(l0.dot(C0[t,s]))
B1[s] += l0_i.dot(B1sbit)
rb = sp.zeros_like(self.r[0])
for s in xrange(self.q[1]):
rb += B1[s].dot(r1.dot(mm.H(B1[s])))
eta = sp.sqrt(mm.adot(l0, rb))
return B1, eta
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 not longer Hermitian (enough).
"""
l_sqrt, evd = m.sqrtmh(self.l[n - 1], ret_evd=True)
l_sqrt_inv = m.invmh(l_sqrt, evd=evd)
r_sqrt, evd = m.sqrtmh(self.r[n], ret_evd=True)
r_sqrt_inv = m.invmh(r_sqrt, evd=evd)
return l_sqrt, r_sqrt, l_sqrt_inv, r_sqrt_inv
def __init__(self, tdvp, n, KLnm1, tau=1, sanity_checks=False):
"""
"""
self.D = tdvp.D
self.q = tdvp.q
self.tdvp = tdvp
self.n = n
self.KLnm1 = KLnm1
self.sanity_checks = sanity_checks
self.sanity_tol = 1E-12
d = self.D[n - 1] * self.D[n] * self.q[n]
self.shape = (d, d)
self.dtype = sp.dtype(tdvp.typ)
self.calls = 0
self.tau = tau
if n > 1:
try:
self.lm1_i = tdvp.l[n - 1].inv()
except AttributeError:
self.lm1_i = m.invmh(tdvp.l[n - 1])
else:
self.lm1_i = tdvp.l[n - 1]
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_l_r_roots(self):
try:
self.l_sqrt = self.l.sqrt()
self.l_sqrt_i = self.l_sqrt.inv()
except AttributeError:
self.l_sqrt, evd = m.sqrtmh(self.l, ret_evd=True)
self.l_sqrt_i = m.invmh(self.l_sqrt, evd=evd)
try:
self.r_sqrt = self.r.sqrt()
self.r_sqrt_i = self.r_sqrt.inv()
except AttributeError:
self.r_sqrt, evd = m.sqrtmh(self.r, ret_evd=True)
self.r_sqrt_i = m.invmh(self.r_sqrt, evd=evd)
if self.sanity_checks:
if not np.allclose(self.l_sqrt.dot(self.l_sqrt), self.l):
print "Sanity check failed: l_sqrt is bad!"
if not np.allclose(self.l_sqrt.dot(self.l_sqrt_i), np.eye(self.D)):
print "Sanity check failed: l_sqrt_i is bad!"
if not np.allclose(self.r_sqrt.dot(self.r_sqrt), self.r):
print "Sanity check failed: r_sqrt is bad!"
if (not np.allclose(self.r_sqrt.dot(self.r_sqrt_i), np.eye(self.D))):
print "Sanity check failed: r_sqrt_i is bad!"
def gauge_align(self, other, tol=1E-12):
"""Gauge-align the state with another.
Given two states that differ only by a gauge-transformation
and a phase, this makes equalizes the parameter tensors by performing
the required transformation.
Parameters
----------
other : EvoMPS_MPS_Uniform
MPS with which to calculate the per-site fidelity.
tol : float
Tolerance for detecting per-site fidelity != 1.
Returns
-------
Nothing if the per-site fidelity is 1. Otherwise:
g : ndarray
The gauge-transformation matrix used.
g_i : ndarray
The inverse of g.
phi : complex
The phase factor.
"""
d, phi, gR = self.fidelity_per_site(other, full_output=True)
if abs(d - 1) > tol:
return
gR = gR.reshape(self.D, self.D)
try:
g = other.r.inv().dotleft(gR)
except:
g = gR.dot(m.invmh(other.r))
gi = la.inv(g)
for s in xrange(self.q):
self.A[s] = phi.conj() * gi.dot(self.A[s]).dot(g)
self.l = m.H(g).dot(self.l.dot(g))
self.r = gi.dot(self.r.dot(m.H(gi)))
return g, gi, phi
def calc_B_centre(self):
"""Calculate the optimal B_centre given right gauge-fixing on n_centre+1..N and
left gauge-fixing on 1..n_centre-1.
We use the non-norm-preserving K's, since the norm-preservation
is not needed elsewhere. It is cleaner to subtract the relevant
norm-changing terms from the K's here than to generate all K's
with norm-preservation.
"""
Bc = sp.empty_like(self.A[self.N_centre])
Nc = self.N_centre
try:
rc_i = self.r[Nc].inv()
except AttributeError:
rc_i = mm.invmh(self.r[Nc])
try:
lcm1_i = self.l[Nc - 1].inv()
except AttributeError:
lcm1_i = mm.invmh(self.l[Nc - 1])
Acm1 = self.A[Nc - 1]
Ac = self.A[Nc]
Acp1 = self.A[Nc + 1]
rc = self.r[Nc]
rcp1 = self.r[Nc + 1]
lcm1 = self.l[Nc - 1]
lcm2 = self.l[Nc - 2]
#Note: this is a 'bra-vector'
K_l_cm1 = self.K_l[Nc - 1] - lcm1 * mm.adot_noconj(
self.K_l[Nc - 1], self.r[Nc - 1])
Kcp1 = self.K[Nc + 1] - rc * mm.adot(self.l[Nc], self.K[Nc + 1])
Cc = self.C[Nc] - self.h_expect[Nc] * self.AAc
Ccm1 = self.C[Nc - 1] - self.h_expect[Nc - 1] * self.AAcm1
for s in xrange(self.q[1]):
try: #3
Bc[s] = Ac[s].dot(rc_i.dot_left(Kcp1))
except AttributeError:
Bc[s] = Ac[s].dot(Kcp1.dot(rc_i))
for t in xrange(self.q[2]): #1
try:
Bc[s] += Cc[s,
t].dot(rcp1.dot(rc_i.dot_left(mm.H(Acp1[t]))))
except AttributeError:
Bc[s] += Cc[s, t].dot(rcp1.dot(mm.H(Acp1[t]).dot(rc_i)))
Bcsbit = K_l_cm1.dot(Ac[s]) #4
for t in xrange(self.q[0]): #2
Bcsbit += mm.H(Acm1[t]).dot(lcm2.dot(Ccm1[t, s]))
Bc[s] += lcm1_i.dot(Bcsbit)
rb = tm.eps_r_noop(rc, Bc, Bc)
eta = sp.sqrt(mm.adot(lcm1, rb))
return Bc, eta
def calc_B_centre(self):
"""Calculate the optimal B_centre given right gauge-fixing on n_centre+1..N and
left gauge-fixing on 1..n_centre-1.
We use the non-norm-preserving K's, since the norm-preservation
is not needed elsewhere. It is cleaner to subtract the relevant
norm-changing terms from the K's here than to generate all K's
with norm-preservation.
"""
Bc = sp.empty_like(self.A[self.N_centre])
Nc = self.N_centre
try:
rc_i = self.r[Nc].inv()
except AttributeError:
rc_i = mm.invmh(self.r[Nc])
try:
lcm1_i = self.l[Nc - 1].inv()
except AttributeError:
lcm1_i = mm.invmh(self.l[Nc - 1])
Acm1 = self.A[Nc - 1]
Ac = self.A[Nc]
Acp1 = self.A[Nc + 1]
rc = self.r[Nc]
rcp1 = self.r[Nc + 1]
lcm1 = self.l[Nc - 1]
lcm2 = self.l[Nc - 2]
#Note: this is a 'bra-vector'
K_l_cm1 = self.K_l[Nc - 1] - lcm1 * mm.adot_noconj(self.K_l[Nc - 1], self.r[Nc - 1])
Kcp1 = self.K[Nc + 1] - rc * mm.adot(self.l[Nc], self.K[Nc + 1])
Cc = self.C[Nc] - self.h_expect[Nc] * self.AAc
Ccm1 = self.C[Nc - 1] - self.h_expect[Nc - 1] * self.AAcm1
for s in xrange(self.q[1]):
try: #3
Bc[s] = Ac[s].dot(rc_i.dot_left(Kcp1))
except AttributeError:
Bc[s] = Ac[s].dot(Kcp1.dot(rc_i))
for t in xrange(self.q[2]): #1
try:
Bc[s] += Cc[s, t].dot(rcp1.dot(rc_i.dot_left(mm.H(Acp1[t]))))
except AttributeError:
Bc[s] += Cc[s, t].dot(rcp1.dot(mm.H(Acp1[t]).dot(rc_i)))
Bcsbit = K_l_cm1.dot(Ac[s]) #4
for t in xrange(self.q[0]): #2
Bcsbit += mm.H(Acm1[t]).dot(lcm2.dot(Ccm1[t,s]))
Bc[s] += lcm1_i.dot(Bcsbit)
rb = tm.eps_r_noop(rc, Bc, Bc)
eta = sp.sqrt(mm.adot(lcm1, rb))
return Bc, eta
