def makeTab(chi, D):
bdi = uni10.Bond(uni10.BD_IN, chi)
bdi1 = uni10.Bond(uni10.BD_IN, D)
bdo = uni10.Bond(uni10.BD_OUT, chi)
Tem0 = uni10.UniTensor([bdi, bdi1, bdo])
Tem0.randomize()
Tem1 = uni10.UniTensor([bdi, bdi1, bdo])
Tem1.randomize()
return Tem0, Tem1
def __init__(self, physical=2, Dimension=2, Number=2, rand_fuc='rand'):
self.N = Number
self.D = Dimension
self.d = physical
self.tensor = [None] * Number
bdi = uni10.Bond(uni10.BD_IN, self.D)
bdo = uni10.Bond(uni10.BD_OUT, self.D)
bdi1 = uni10.Bond(uni10.BD_IN, 1)
bdo1 = uni10.Bond(uni10.BD_OUT, 1)
bdi_pys = uni10.Bond(uni10.BD_IN, self.d)
#print "Hi", bdi_pys
A_fixed = uni10.UniTensor([bdi, bdi_pys, bdo], "A_middle")
A_fixed.randomize()
for i in xrange(self.N):
if i == 0:
A = uni10.UniTensor([bdi1, bdi_pys, bdo], "A_0")
if rand_fuc is 'rand':
A.randomize()
self.tensor[i] = A
#print "Hi0A"
elif rand_fuc is 'ortho':
A.orthoRand()
#print "Hi10"
self.tensor[i] = A
elif rand_fuc is 'iden':
A.identity()
self.tensor[i] = A
#print "Hi20"
elif i == ((self.N) - 1):
A = uni10.UniTensor([bdi, bdi_pys, bdo1], "A_N")
if rand_fuc is 'rand':
A.randomize()
self.tensor[i] = A
elif rand_fuc is 'ortho':
A.orthoRand()
self.tensor[i] = A
elif rand_fuc is 'iden':
#print "HIIIIIIIIIIIIIII"
A.identity()
self.tensor[i] = A
else:
A = uni10.UniTensor([bdi, bdi_pys, bdo], "A_middle")
if rand_fuc is 'rand':
A.randomize()
self.tensor[i] = A
elif rand_fuc is 'ortho':
A.orthoRand()
self.tensor[i] = A
elif rand_fuc is 'randuni':
self.tensor[i] = copy.copy(A_fixed)
elif rand_fuc is 'iden':
A.identity()
self.tensor[i] = A
def makec1(chi, D):
bdi = uni10.Bond(uni10.BD_IN, chi)
bdo = uni10.Bond(uni10.BD_OUT, chi)
Tem0 = uni10.UniTensor([bdi, bdo])
Tem0.randomize()
Tem1 = uni10.UniTensor([bdi, bdo])
Tem1.randomize()
Tem2 = uni10.UniTensor([bdi, bdo])
Tem2.randomize()
Tem3 = uni10.UniTensor([bdi, bdo])
Tem3.randomize()
return Tem0, Tem1, Tem2, Tem3
def inv(svd): bdi=uni10.Bond(uni10.BD_IN, svd.col()) bdo=uni10.Bond(uni10.BD_OUT, svd.row()) Landa=uni10.UniTensor([bdi,bdo]) Landa.putBlock(svd) Landa=basic.inverse(Landa) return Landa
def test_energy_lr(N_uni, l, l_d, r, r_d, q,qq,U,E1, E2, E3, E4, E5,E6,a_u,b_u): U.setLabel([-20,-40,20,40]) Iden=uni10.UniTensor(U.bond()) Bond_val=U.bond()[0].dim()*U.bond()[1].dim() matrix_Iden=uni10.Matrix(Bond_val, Bond_val) matrix_Iden.identity() Iden.putBlock(matrix_Iden) Iden.setLabel([-20,-40,20,40]) a_u.setLabel([20,13,1,2,3]) a_d=copy.copy(a_u) a_d.setLabel([-20,-13,-1,-2,-3]) b_u.setLabel([40,2,4,5,6]) b_d=copy.copy(b_u) b_d.setLabel([-40,-2,-4,-5,-6]) B=(((r*r_d)*U)*(l*l_d))*(N_uni) A=(((r*r_d)*Iden)*(l*l_d))*(N_uni) print 'E=', B[0]/A[0] a_ut=q*r a_ut.permute([20,4,5,3,2],3) print a_ut.similar(a_u),distance_two(a_u.getBlock(),a_ut.getBlock()) b_ut=qq*l b_ut.permute([40,3,4,5,6],3) print b_ut.similar(b_u), distance_two(b_u.getBlock(),b_ut.getBlock())
def pauli4body():
'''
Tensors for 4-body Pauli strings.
'''
bond_dim = 2
iden = matIden()
sx = matSx()
sy = matSy()
sz = matSz()
bdi = uni10.Bond(uni10.BD_IN, bond_dim)
bdo = uni10.Bond(uni10.BD_OUT, bond_dim)
pauli1b = [iden, sx, sy, sz]
pauli4b = []
pauli2b = []
l2b = 16
for i in xrange(4):
for j in xrange(4):
mat = uni10.otimes(pauli1b[i], pauli1b[j])
pauli2b.append(mat)
for i in xrange(l2b):
for j in xrange(l2b):
mat = uni10.otimes(pauli2b[i], pauli2b[j])
P = uni10.UniTensor([bdi, bdi, bdi, bdi, bdo, bdo, bdo, bdo])
P.putBlock(mat)
pauli4b.append(P)
return pauli4b
def pauli6body():
'''
Tensors for 6-body Pauli strings.
'''
bond_dim = 2
iden = matIden()
sx = matSx()
sy = matSy()
sz = matSz()
bdi = uni10.Bond(uni10.BD_IN, bond_dim)
bdo = uni10.Bond(uni10.BD_OUT, bond_dim)
pauli1b = [iden, sx, sy, sz]
pauli6b = []
bonds = [bdi] * 6 + [bdo] * 6
pauli3b = []
for i in xrange(4):
for j in xrange(4):
for k in xrange(4):
mat = uni10.otimes(uni10.otimes(pauli1b[i], pauli1b[j]),
pauli1b[k])
pauli3b.append(mat)
l3b = 64
for i in xrange(l3b):
for j in xrange(l3b):
mat = uni10.otimes(pauli3b[i], pauli3b[j])
P = uni10.UniTensor(bonds)
P.putBlock(mat)
pauli6b.append(P)
return pauli6b
def intialize_unitary_list(L, L_lay, d, delta):
U_list = []
bdi_spin = uni10.Bond(uni10.BD_IN, d)
bdo_spin = uni10.Bond(uni10.BD_OUT, d)
Svd = []
matrix_random = uni10.Matrix(d * d, d * d)
for i in xrange(d * d):
for j in xrange(d * d):
if (i == j):
matrix_random[i * d * d + j] = 1.0
else:
matrix_random[i * d * d + j] = 0.0
#matrix_random.setIdentity()
for i in xrange(L / 2):
U_list.append([])
for i in xrange(L / 2):
for j in xrange(len(L_lay)):
U_uni10 = uni10.UniTensor([bdi_spin, bdi_spin, bdo_spin, bdo_spin],
"Unitary_uni10")
matrix_random1 = copy.copy(matrix_random)
matrix_random1.randomize()
matrix_random1 = matrix_random + (delta) * matrix_random1
Svd = matrix_random1.svd()
U_uni10.putBlock(Svd[0] * Svd[2])
U_list[i].append(U_uni10)
return U_list
def setTruncation(theta, chi):
#print theta
LA = uni10.UniTensor(theta.bond())
GA = uni10.UniTensor(theta.bond())
GB = uni10.UniTensor(theta.bond())
bond_In_list, bond_OUT_list = bond_list_dist(theta)
#print bond_In_list,bond_OUT_list
svds = {}
blk_qnums = theta.blockQnum()
dim_svd = []
for qnum in blk_qnums:
M_tem = theta.getBlock(qnum)
svds[qnum] = M_tem.svd()
#print qnum, svds[qnum][1]
dim_svd.append(int(svds[qnum][1].col()))
svs = []
bidxs = []
for bidx in xrange(len(blk_qnums)):
svs, bidxs = sv_merge(svs, bidxs, bidx, svds[blk_qnums[bidx]][1], chi,
len(blk_qnums))
#print "lol", svs, bidxs
dims = [0] * len(blk_qnums)
for bidx in bidxs:
dims[bidx] += 1
qnums = []
for bidx in xrange(len(blk_qnums)):
qnums += [blk_qnums[bidx]] * dims[bidx]
bdi_mid = uni10.Bond(uni10.BD_IN, qnums)
#print "Hi", chi, svs, qnums, bdi_mid
#print bdi_mid
bdo_mid = uni10.Bond(uni10.BD_OUT, qnums)
bond_In_list.append(bdo_mid)
GA.assign(bond_In_list)
bond_OUT_list.insert(0, bdi_mid)
GB.assign(bond_OUT_list)
LA.assign([bdi_mid, bdo_mid])
degs = bdi_mid.degeneracy()
for qnum, dim in degs.iteritems():
if qnum not in svds:
raise Exception("In setTruncaton(): Fatal error.")
svd = svds[qnum]
GA.putBlock(qnum, svd[0].resize(svd[0].row(), dim))
GB.putBlock(qnum, svd[2].resize(dim, svd[2].col()))
LA.putBlock(qnum, svd[1].resize(dim, dim))
#print LA
return GA, GB, LA
def Reload_Fullp():
ap_u = uni10.UniTensor("Store/ap_u")
bp_u = uni10.UniTensor("Store/bp_u")
cp_u = uni10.UniTensor("Store/cp_u")
dp_u = uni10.UniTensor("Store/dp_u")
ap = uni10.UniTensor("Store/ap")
bp = uni10.UniTensor("Store/bp")
cp = uni10.UniTensor("Store/cp")
dp = uni10.UniTensor("Store/dp")
return ap_u, bp_u, cp_u, dp_u, ap, bp, cp, dp
def Reload_Full():
a_u = uni10.UniTensor("Store/a_u")
b_u = uni10.UniTensor("Store/b_u")
c_u = uni10.UniTensor("Store/c_u")
d_u = uni10.UniTensor("Store/d_u")
a = uni10.UniTensor("Store/a")
b = uni10.UniTensor("Store/b")
c = uni10.UniTensor("Store/c")
d = uni10.UniTensor("Store/d")
return a_u, b_u, c_u, d_u, a, b, c, d
def final_test_distance(ap_u, bp_u, a_u, b_u,E1, E2, E3, E4, E5,E6,U,N_uni): U.setLabel([-20,-40,20,40]) H1=copy.copy(U) H1.transpose() H1.setLabel([-20,-40,30,50]) U.setLabel([30,50,20,40]) H=U*H1 H.permute([-20,-40,20,40],2) #H.setLabel([-20,-40,20,40]) H1.setLabel([-20,-40,20,40]) U.setLabel([-20,-40,20,40]) Iden=uni10.UniTensor(U.bond()) matrix_Iden=uni10.Matrix(U.bond()[0].dim()*U.bond()[1].dim(), U.bond()[2].dim()*U.bond()[3].dim()) matrix_Iden.identity() Iden.putBlock(matrix_Iden) Iden.setLabel([-20,-40,20,40]) a_u.setLabel([20,13,1,2,3]) a_d=copy.copy(a_u) a_d.setLabel([-20,-13,-1,-2,-3]) b_u.setLabel([40,2,4,5,6]) b_d=copy.copy(b_u) b_d.setLabel([-40,-2,-4,-5,-6]) ap_u.setLabel([20,13,1,2,3]) ap_d=copy.copy(ap_u) ap_d.setLabel([-20,-13,-1,-2,-3]) bp_u.setLabel([40,2,4,5,6]) bp_d=copy.copy(bp_u) bp_d.setLabel([-40,-2,-4,-5,-6]) #N=(((((E2*)*E1)*E3)*Iden)*((((E5)*E6)*E4))) # a_u=q*r # a_u.permute([20,4,5,3,2],3) # a_u.setLabel([0,1,2,3,4]) # #print a_ut.similar(a_u)#,a_ut.printDiagram() # # b_u=qq*l # b_u.permute([40,3,4,5,6],3) # b_u.setLabel([0,1,2,3,4]) # #print b_ut.similar(b_u) A1=(((((E2*(a_u*a_d))*E1)*E3)*H)*(((((b_u*b_d)*E5)*E6)*E4))) A2=((((E2*(ap_u*ap_d))*E1)*E3)*Iden)*(((((bp_u*bp_d)*E5)*E6)*E4)) A3=((((E2*(a_u*ap_d))*E1)*E3)*U)*(((((b_u*bp_d)*E5)*E6)*E4)) A4=((((E2*(ap_u*a_d))*E1)*E3)*H1)*(((((bp_u*b_d)*E5)*E6)*E4)) A=A1+A2+(-1.00)*A3+(-1.00)*A4 return A
def trgSVD(wch, T, chi):
if wch % 2 == 0:
T.permute([-4, -3, -1, -2], 2)
else:
T.permute([-1, -4, -2, -3], 2)
svd = T.getBlock().svd()
chi = chi if chi < svd[1].row() else svd[1].row()
bdi_chi = uni10.Bond(uni10.BD_IN, chi)
bdo_chi = uni10.Bond(uni10.BD_OUT, chi)
S0 = uni10.UniTensor([T.bond(0), T.bond(1), bdo_chi])
S1 = uni10.UniTensor([bdi_chi, T.bond(2), T.bond(3)])
svd[1].resize(chi, chi)
for i in xrange(chi):
svd[1][i] = np.sqrt(svd[1][i])
S0.putBlock(svd[0].resize(svd[0].row(), chi) * svd[1])
S1.putBlock(svd[1] * svd[2].resize(chi, svd[2].col()))
return S0, S1
def sqt(Landa2):
invLanda2 = uni10.UniTensor(Landa2.bond())
invL2 = uni10.Matrix(Landa2.bond()[0].dim(), Landa2.bond()[1].dim())
D = Landa2.bond()[0].dim()
for i in xrange(Landa2.bond()[0].dim()):
for j in xrange(Landa2.bond()[0].dim()):
invL2[i * D + j] = ((Landa2[i * D + j])**(1.00 / 2.00))
invLanda2.putBlock(invL2)
return invLanda2
def load_ulist(L, lla, outdir):
Ulist = []
for l in xrange(L):
Ulist.append([])
for i in xrange(lla):
u = uni10.UniTensor(outdir + '/U%d%d_L%dLU%d' % (l, i, L, lla))
Ulist[l].append(u)
return Ulist
def vec2uni(v, L):
bond_dim = 2
vl = list(v)
vl10 = uni10.Matrix(2**L, 1, vl)
bdi = uni10.Bond(uni10.BD_IN, bond_dim)
bdo = uni10.Bond(uni10.BD_OUT, bond_dim)
vtensor = uni10.UniTensor([bdi] * L, 'GS')
vtensor.putBlock(vl10)
return vtensor
def inverse(Landa2):
invLanda2 = uni10.UniTensor(Landa2.bond())
invL2 = uni10.Matrix(Landa2.bond()[0].dim(), Landa2.bond()[1].dim())
D = Landa2.bond()[0].dim()
for i in xrange(Landa2.bond()[0].dim()):
for j in xrange(Landa2.bond()[0].dim()):
invL2[i * D + j] = 0 if ((Landa2[i * D + j].real) < 1.0e-12) else (
1.00 / (Landa2[i * D + j].real))
invLanda2.putBlock(invL2)
return invLanda2
def qr_parity(theta):
#bd1=copy.copy(theta.bond(3))
#bd1.change(uni10.BD_IN)
bd1 = uni10.Bond(uni10.BD_IN, theta.bond(1).Qlist())
GA = uni10.UniTensor(uni10.CTYPE, [theta.bond(0), theta.bond(1)])
LA = uni10.UniTensor(uni10.CTYPE, [bd1, theta.bond(1)])
svds = {}
blk_qnums = theta.blockQnum()
dim_svd = []
for qnum in blk_qnums:
svds[qnum] = theta.getBlock(qnum).qr()
GA.putBlock(qnum, svds[qnum][0])
LA.putBlock(qnum, svds[qnum][1])
# print LA
return GA, LA
def Reduction_lastbond(U_up_1, n, Letter):
Mat=U_up_1.getBlock()
dim0=U_up_1.bond(0).dim()
dim1=U_up_1.bond(1).dim()
dim2=U_up_1.bond(2).dim()
dim3=U_up_1.bond(3).dim()
if Letter is 'up':
Mat1=uni10.Matrix(dim0*dim1, dim2)
Mat1.set_zero()
for i in xrange(dim0):
for j in xrange(dim1):
for m in xrange(dim2):
Mat1[i*dim1*dim2+j*dim2+m]=U_up_1[i*dim1*dim2*dim3+j*dim2*dim3+m*dim3+n]
bond_list=list(U_up_1.bond())
#print list(bond_list)
bond_list=list(bond_list)
bond_list.pop()
#print bond_list
Uni_tensor=uni10.UniTensor(bond_list,U_up_1.getName())
Uni_tensor.putBlock(Mat1)
#print Uni_tensor.printDiagram(), n
return Uni_tensor
elif Letter is 'down':
Mat1=uni10.Matrix(dim0, dim2*dim3)
Mat1.set_zero()
for i in xrange(dim0):
for j in xrange(dim3):
for m in xrange(dim2):
Mat1[i*dim3*dim2+m*dim3+j]=U_up_1[i*dim1*dim2*dim3+n*dim2*dim3+m*dim3+j]
bond_list=list(U_up_1.bond())
#print list(bond_list)
bond_list=list(bond_list)
A=bond_list.pop()
B=bond_list.pop()
C=bond_list.pop()
bond_list.append(B)
bond_list.append(A)
#print bond_list
Uni_tensor=uni10.UniTensor(bond_list,U_up_1.getName())
Uni_tensor.putBlock(Mat1)
#print Uni_tensor.printDiagram(), n
return Uni_tensor
def get_umpo_up_1layer(Ulist,L,bond_idx,bond_dim=2):
# Will not be called
if (bond_idx%2 == 0):
U = Ulist[bond_idx/2]
else:
bdi = uni10.Bond(uni10.BD_IN, bond_dim)
bdo = uni10.Bond(uni10.BD_OUT, bond_dim)
U = uni10.UniTensor([bdi,bdi,bdo,bdo],'U0')
I = matIden()
U.putBlock(I)
return U
def l_optimum_full(l, r, l_d, r_d, lp, rp, lp_d, rp_d ,N_uni,U): U.setLabel([-20,-40,20,40]) H1=copy.copy(U) H1.transpose() H1.setLabel([-20,-40,20,40]) Iden=uni10.UniTensor(U.bond()) Bond_val=U.bond()[0].dim()*U.bond()[1].dim() matrix_Iden=uni10.Matrix(Bond_val, Bond_val) matrix_Iden.identity() Iden.putBlock(matrix_Iden) Iden.setLabel([-20,-40,20,40]) A2=(((rp*rp_d)*N_uni)*Iden) A2.permute([2,40,3,-2,-40,-3],3) A2_trans=copy.copy(A2) A2_trans.transpose() A2=A2+A2_trans A3=((l)*U*(r*rp_d))*N_uni A3.permute([-2,-40,-3],0) A3p=((l_d)*U*(rp*r_d))*N_uni A3p.permute([2,40,3],0) A3=A3+A3p svd=A2.getBlock().svd() Landa=inv(svd[1]) #print Landa.getBlock()*svd[1] v=copy.copy(svd[2]) v.transpose() u=svd[0] u.transpose() s=Landa.getBlock() A2_inv=v*s*u A2_mat=A2.getBlock() #distance_iden_val=distance_iden(A2_mat,A2_inv) #print 'distance=', distance_iden_val A=A3.getBlock() A=A*A2_inv A3.putBlock(A) A3.setLabel([2,40,3]) A3.permute([2,40,3],2) lf=copy.copy(A3) lf_d=copy.copy(lf) lf_d.setLabel([-2,-40,-3]) return lf, lf_d
def test_u2p():
umat = uni10.Matrix(4,4,[0.,0.,0.9,0.,0.,0.,0.,0.1,0.9,0.,0.,0.,0.,0.1,0.,0.])
umat = uni10.Matrix(4,4,[-1., -0., -0., -1.,-0., -0., -1., -0., -0., -1., -0., -0.,-1.,-0.,-0.,1.])
U = uni10.UniTensor([bdi,bdi,bdo,bdo],'U')
U.putBlock(umat)
pstr, _ = pauli2body()
coef = []
for i in range(len(pstr)):
c = unitary2pauli(U,pstr[i],2)
coef.append(c)
print coef
def r_optimum_full(l, r, l_d, r_d, lp, rp, lp_d, rp_d ,N_uni,U): U.setLabel([-20,-40,20,40]) H1=copy.copy(U) H1.transpose() H1.setLabel([-20,-40,30,50]) #print H1, U Iden=uni10.UniTensor(U.bond()) d=U.bond()[0].dim()*U.bond()[1].dim() matrix_Iden=uni10.Matrix(d, d) matrix_Iden.identity() Iden.putBlock(matrix_Iden) Iden.setLabel([-20,-40,20,40]) A2=(((lp*lp_d)*N_uni)*Iden) A2.permute([3,20,1,-3,-20,-1],3) A2_trans=copy.copy(A2) A2_trans.transpose() A2=A2+A2_trans A3=((r)*U*(l*lp_d))*N_uni A3.permute([-3,-20,-1],0) A3p=((r_d)*U*(l_d*lp))*N_uni A3p.permute([3,20,1],0) A3=A3+A3p svd=A2.getBlock().svd() Landa=inv(svd[1]) #print Landa.getBlock()*svd[1] v=copy.copy(svd[2]) v.transpose() u=svd[0] u.transpose() s=Landa.getBlock() A2_inv=v*s*u A2_mat=A2.getBlock() #distance_iden_val=distance_iden(A2_mat,A2_inv) #print 'distance1=', distance_iden_val #print A2.getBlock()*A2_inv A=A3.getBlock() A=A*A2_inv A3.putBlock(A) A3.setLabel([3,20,1]) A3.permute([3,20,1],2) rf=copy.copy(A3) rf_d=copy.copy(rf) rf_d.setLabel([-3,-20,-1]) return rf, rf_d
def Ham(h):
spin = 0.5
sx = (matSp() + matSm())
sz = matSz()
iden = uni10.Matrix(2, 2, [1, 0, 0, 1])
ham = uni10.otimes(sz, sz) * (-2.00) + (-0.500) * float(h) * (
uni10.otimes(iden, sx) + uni10.otimes(sx, iden))
dim = int(spin * 2 + 1)
bdi = uni10.Bond(uni10.BD_IN, dim)
bdo = uni10.Bond(uni10.BD_OUT, dim)
H = uni10.UniTensor([bdi, bdi, bdo, bdo], "TFIM")
H.putBlock(ham)
return H
def transverseIsing(h):
spin = 0.5
sx = 0.5 * (matSp() + matSm())
sz = matSz()
iden = uni10.Matrix(2, 2, [1, 0, 0, 1])
ham =uni10.otimes(2*sz,2*sz)*(-1) \
+0.25*float(h)*(uni10.otimes(iden,2*sx)+uni10.otimes(2*sx,iden))
dim = int(spin * 2 + 1)
bdi = uni10.Bond(uni10.BD_IN, dim)
bdo = uni10.Bond(uni10.BD_OUT, dim)
H = uni10.UniTensor([bdi, bdi, bdo, bdo], "TFIM")
H.putBlock(ham)
return H
def svd_parity(theta):
#print theta,theta.getBlock().svd()
bdo=uni10.Bond(uni10.BD_OUT,theta.bond(1).Qlist())
bdo1=uni10.Bond(uni10.BD_OUT,theta.bond(2).Qlist())
#A_{m<n}=U_{mm}S_{mm}V_{mn}
LA=uni10.UniTensor([theta.bond(1), bdo])
GA=uni10.UniTensor([theta.bond(0), theta.bond(1),bdo])
GB=uni10.UniTensor([theta.bond(1), bdo1])
svds = {}
blk_qnums = theta.blockQnum()
dim_svd=[]
for qnum in blk_qnums:
svds[qnum] = theta.getBlock(qnum).svd()
for qnum in blk_qnums:
svd = svds[qnum]
GA.putBlock(qnum, svd[0])
GB.putBlock(qnum, svd[2])
LA.putBlock(qnum, svd[1])
# print LA
return GA, LA,GB
def Iden_uni10(d):
bdi=uni10.Bond(uni10.BD_IN, d)
bdo=uni10.Bond(uni10.BD_OUT, d)
Iden_Matrix=uni10.Matrix(d*d,d)
for i in xrange(d):
for j in xrange(d):
for m in xrange(d):
if i is j and j is m:
Iden_Matrix[i*d+j,m]=1
else:
Iden_Matrix[i*d+j,m]=0
Iden_uni10=uni10.UniTensor([bdi,bdi,bdo], 'Iden_uni10')
Iden_uni10.putBlock(Iden_Matrix)
return Iden_uni10
def transverseIsing(h):
spin = 0.5
sx = matSx()
sy = matSy()
sz = matSz()
iden = uni10.Matrix(2, 2, [1, 0, 0, 1])
ham = uni10.otimes(sz, sz) * (-1) + (-0.2500) * float(h) * (
uni10.otimes(iden, sx) + uni10.otimes(sx, iden))
dim = int(spin * 2 + 1)
bdi = uni10.Bond(uni10.BD_IN, dim)
bdo = uni10.Bond(uni10.BD_OUT, dim)
H = uni10.UniTensor([bdi, bdi, bdo, bdo], "TFIM")
H.putBlock(ham)
return H
def initialize_SVD_lrprime(l, r, l_d, r_d, N_uni,U,D,d_phys): bdiB=uni10.Bond(uni10.BD_IN, d_phys*D) bdoB=uni10.Bond(uni10.BD_OUT, d_phys*D) bdi_pys=uni10.Bond(uni10.BD_IN, d_phys) bdi_pyso=uni10.Bond(uni10.BD_OUT, d_phys) bdi=uni10.Bond(uni10.BD_IN, D) bdo=uni10.Bond(uni10.BD_OUT, D) U.setLabel([-20,-40,20,40]) lp=copy.copy(l) rp=copy.copy(r) #lp.setLabel([1,40,-3]) Teta=U*lp*rp Teta.permute([1,-20,2,-40],2) svd=Teta.getBlock().svd() s=Sqrt(svd[1]) U=svd[0]*s V=s*svd[2] U.resize(U.row(),rp.bond()[0].dim() ) V.resize(rp.bond()[0].dim(), V.col()) rp=uni10.UniTensor([bdiB, bdi_pys, bdo ]) rp.putBlock(U) rp.setLabel([1,20,3]) rp.permute([3,20,1],2) lp=uni10.UniTensor([ bdi, bdoB, bdi_pyso]) lp.putBlock(V) lp.setLabel([3,2,40]) lp.permute([2,40,3],2) lp_d=copy.copy(lp) rp_d=copy.copy(rp) rp_d.setLabel([-3,-20,-1]) lp_d.setLabel([-2,-40,-3]) return lp, rp, lp_d, rp_d
def Heisenberg(h):
spin = 0.5
sx = matSx()
sy = matSy()
sz = matSz()
iden = uni10.Matrix(2, 2, [1, 0, 0, 1])
ham = (float(h) * uni10.otimes(sz, sz) * (1.00 / 4.00) +
(1.00 / 4.00) * uni10.otimes(sx, sx) +
(-1.00 / 4.00) * uni10.otimes(sy, sy))
dim = int(spin * 2 + 1)
bdi = uni10.Bond(uni10.BD_IN, dim)
bdo = uni10.Bond(uni10.BD_OUT, dim)
H = uni10.UniTensor([bdi, bdi, bdo, bdo], "Heisenberg")
H.putBlock(ham)
return H
