def sublattice_state(v1,v2,system):
state_bits = np.zeros(system.N)
for n in range(0,np.size(v1)):
state_bits[2*v1[n]] = 1
for n in range(0,np.size(v2)):
state_bits[2*v2[n]+1] = 1
return bin_state(state_bits,system)
Hp[0].model_coef = np.array([1])
Hp[1] = Hamiltonian(pxp, pxp_syms)
Hp[1].site_ops[1] = np.array([[0, 0], [1, 0]])
Hp[1].site_ops[2] = np.array([[0, 1], [0, 0]])
Hp[1].model = np.array([[0, 1, 2, 0, 0], [0, 0, 1, 2, 0]])
Hp[1].model_coef = np.array([1, 1])
Hp[2] = Hamiltonian(pxp, pxp_syms)
Hp[2].site_ops[1] = np.array([[0, 0], [1, 0]])
Hp[2].site_ops[2] = np.array([[0, 1], [0, 0]])
Hp[2].site_ops[4] = np.array([[0, 0], [0, 1]])
Hp[2].model = np.array([[0, 4, 0, 1, 2, 0], [0, 1, 2, 0, 4, 0]])
Hp[2].model_coef = np.array([1, 1])
psi = bin_state(np.array([0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0]), pxp)
k = pxp_syms.find_k_ref(psi.ref)
for n in range(0, len(Hp)):
for m in range(0, np.size(k, axis=0)):
Hp[n].gen(k[m])
coef = np.load("./data/xy,pert_coef,10.npy")
Hp_total = deepcopy(Hp[0])
for n in range(1, len(Hp)):
Hp_total = H_operations.add(Hp_total, Hp[n], np.array([1, coef[n - 1]]))
Hp = Hp_total
Hm = Hp_total.herm_conj()
H = H_operations.add(Hp, Hm, np.array([1, 1]))
for n in range(0, np.size(root_sectors, axis=0)):
print("\n")
for m in range(0, np.size(root_refs[perm_key(root_sectors[n])], axis=0)):
ref = root_refs[perm_key(root_sectors[n])][m]
print(pxp.basis[pxp.keys[ref]], basis_perm_labels[pxp.keys[ref]])
# smaller hypercubes + hamming, for subcube identification
sub_cube_systems = dict()
sub_cube_hamming = dict()
for n in range(int(pxp.N / 2), 1, -1):
sub_cube_systems[n] = unlocking_System([0, 1], "open", 2, n)
sub_cube_systems[n].gen_basis()
z_temp = np.ones(sub_cube_systems[n].N)
z_temp[np.size(z_temp, axis=0) - 1] = 0
z_temp = bin_state(z_temp, sub_cube_systems[n])
sub_cube_hamming[n] = find_hamming_sectors(z_temp.bits,
sub_cube_systems[n])
#find pos of zeros + ones for subcubes
#loc of poss 1 that is zero
for n in range(0, np.size(root_sectors, axis=0)):
for m in range(0, np.size(root_refs[perm_key(root_sectors[n])], axis=0)):
ref = root_refs[perm_key(root_sectors[n])][m]
state = pxp.basis[pxp.keys[ref]]
poss_zero_loc = []
for i in range(0, np.size(state, axis=0)):
if i % 2 != 0:
if state[i] == 0:
if i == 0:
im1 = pxp.N - 1
H.model_coef = np.array([V,1,1])
# H.model = np.array([[0,1]])
# H.model_coef = np.array([1])
k=[0]
H.gen(k)
H.sector.find_eig(k)
block_refs = pxp_syms.find_block_refs(k)
block_keys = dict()
for n in range(0,np.size(block_refs,axis=0)):
block_keys[block_refs[n]] = n
neel=zm_state(2,1,pxp,1)
pol = ref_state(0,pxp)
all_ones = bin_state(np.append([0],np.ones(pxp.N-1)),pxp)
neel_trans = np.zeros(np.size(block_refs))
pol_trans = np.zeros(np.size(block_refs))
all_ones_trans = np.zeros(np.size(block_refs))
neel_trans[block_keys[neel.ref]] = 1
pol_trans[block_keys[pol.ref]] = 1
all_ones_trans[block_keys[all_ones.ref]] = 1
neel_trans_energy = np.dot(np.conj(np.transpose(H.sector.eigvectors(k))),neel_trans)
pol_trans_energy = np.dot(np.conj(np.transpose(H.sector.eigvectors(k))),pol_trans)
all_ones_trans_energy = np.dot(np.conj(np.transpose(H.sector.eigvectors(k))),all_ones_trans)
t=np.arange(0,80,0.01)
f_neel = np.zeros(np.size(t))