Python Hamiltonian.model_coef Examples

Example #1

 def xx_2site(self):
     pxp = unlocking_System(np.arange(0, self.base), "open", self.base, 2)
     pxp.gen_basis()
     H = Hamiltonian(pxp)
     H.site_ops[1] = np.array([[0, 1], [1, 0]])
     H.model = np.array([[1, 1]])
     H.model_coef = np.array([[1]])
     H.gen()
     H.sector.find_eig()
     return H

Example #2

 def pxp_3site(self, Nc):
     pxp = unlocking_System(np.arange(0, self.base), "open", self.base, 3)
     pxp.gen_basis()
     X = spin_Hamiltonian(pxp, "x").site_ops[1]
     H = Hamiltonian(pxp)
     H.site_ops[1] = X
     H.model = np.array([[0, 1, 0]])
     H.model_coef = np.array([[1]])
     H.gen()
     H.sector.find_eig()
     return H

Example #3

File: unitCell_su2Decomps.py Project: Cable273/physics_scripts

def HpFromKey(key):
    Hp = Hamiltonian(pxp)
    Hp.site_ops[1] = np.array([[0, 0], [1, 0]])
    Hp.site_ops[2] = np.array([[0, 1], [0, 0]])
    model = []
    for m in range(0, np.size(key, axis=0)):
        if key[m] == 1:
            model.append([0, 1, 0])
        else:
            model.append([0, 2, 0])
    Hp.model = model
    Hp.model_coef = np.ones(pxp.N)
    Hp.uc_size = pxp.N * np.ones(pxp.N)
    Hp.uc_pos = np.arange(0, pxp.N)
    Hp.gen()
    return Hp

Example #4

def cost(alpha):
    H=Hamiltonian(pxp_half,"x")
    H.site_ops[1] = np.array([[0,alpha[0]],[alpha[0],0]])
    H.model = np.array([[1]])
    H.model_coef=np.array((1))
    H.gen()
    H.sector.find_eig()
    z_rydberg = zm_state(2,1,pxp_half)
    f_neel_ham = fidelity(z_rydberg,H).eval(t,z_rydberg)
    # plt.plot(t,f_neel_ham)
    # plt.show()

    cut_index = 5
    for n in range(0,np.size(f_neel_ham,axis=0)):
        if f_neel_ham[n]<0.1:
            cut_index = n
            break
    max_index = np.argmax(f_neel_ham[cut_index:])
    t0_ham = t[max_index]

    print(cost,t0_ham)
    return np.abs(t0_ham-t0_floquet)

Example #5

File: pxp,z3_errors.py Project: Cable273/physics_scripts

def var(Q, psi):
    Q2 = np.dot(Q, Q)
    return exp(Q2, psi) - exp(Q, psi)**2


N = 10
pxp = unlocking_System([0], "periodic", 3, N)
pxp.gen_basis()
pxp_syms = model_sym_data(pxp, [translational_general(pxp, order=2), PT(pxp)])

#orig H
Ip = Hamiltonian(pxp, pxp_syms)
Ip.site_ops[1] = np.array([[0, 1, 0], [0, 0, 0], [0, 0, 0]])
Ip.site_ops[2] = np.array([[0, 0, 0], [1, 0, 0], [0, 0, 0]])
Ip.model = np.array([[0, 1, 0], [0, 2, 0]])
Ip.model_coef = np.array([1, -1])
Ip.uc_size = np.array([2, 2])
Ip.uc_pos = np.array([1, 0])

Im = Hamiltonian(pxp, pxp_syms)
Im.site_ops[1] = np.array([[0, 1, 0], [0, 0, 0], [0, 0, 0]])
Im.site_ops[2] = np.array([[0, 0, 0], [1, 0, 0], [0, 0, 0]])
Im.model = np.array([[0, 2, 0], [0, 1, 0]])
Im.model_coef = np.array([1, -1])
Im.uc_size = np.array([2, 2])
Im.uc_pos = np.array([1, 0])

Kp = Hamiltonian(pxp, pxp_syms)
Kp.site_ops[1] = np.array([[0, 0, 1], [0, 0, 0], [0, 0, 0]])
Kp.site_ops[2] = np.array([[0, 0, 0], [0, 0, 0], [1, 0, 0]])
Kp.model = np.array([[0, 1, 0], [0, 2, 0]])

Example #6

File: perturbed_fsa.py Project: Cable273/physics_scripts

## for Palatino and other serif fonts use:
#rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
# matplotlib.rcParams['figure.dpi'] = 400

N = 16
pxp = unlocking_System([0], "periodic", 2, N)
pxp.gen_basis()
pxp_syms = model_sym_data(pxp, [translational(pxp), parity(pxp)])

Hp = dict()
Hp[0] = Hamiltonian(pxp)
Hp[0].site_ops[1] = np.array([[0, 0], [1, 0]])
Hp[0].site_ops[2] = np.array([[0, 1], [0, 0]])
Hp[0].model = np.array([[0, 1, 0], [0, 2, 0]])
Hp[0].model_coef = np.array([1, 1])
Hp[0].uc_size = np.array([2, 2])
Hp[0].uc_pos = np.array([1, 0])

#1st order pert
Hp[1] = Hamiltonian(pxp)
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, 0, 1, 0], [0, 1, 0, 0], [0, 0, 2, 0], [0, 2, 0,
                                                                   0]])
Hp[1].model_coef = np.array([1, 1, 1, 1])
Hp[1].uc_size = np.array([2, 2, 2, 2])
Hp[1].uc_pos = np.array([0, 1, 1, 0])

#2nd order perts
Hp[2] = Hamiltonian(pxp)

Example #7

File: xy_pert_dynamcs.py Project: Cable273/physics_scripts

def var(Q, psi):
    Q2 = np.dot(Q, Q)
    return exp(Q2, psi) - exp(Q, psi)**2


N = 14
pxp = unlocking_System([0, 1], "periodic", 2, N)
pxp.gen_basis()
pxp_syms = model_sym_data(pxp, [translational(pxp)])

Hp = dict()
Hp[0] = Hamiltonian(pxp, pxp_syms)
Hp[0].site_ops[1] = np.array([[0, 0], [1, 0]])
Hp[0].site_ops[2] = np.array([[0, 1], [0, 0]])
Hp[0].model = np.array([[0, 1, 2, 0]])
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)

Example #8

x = 1 / 2 * (Jp + Jm)
y = 1 / 2j * (Jp - Jm)
z = 1 / 2 * com(Jp, Jm)
z2 = np.dot(z, z)

#create Hamiltonian
J = 1
h = 0.1
D = 0.1
H = Hamiltonian(pxp)
H.site_ops[1] = x
H.site_ops[2] = y
H.site_ops[3] = z
H.site_ops[4] = z2
H.model = np.array([[1, 1], [2, 2], [3], [4]])
H.model_coef = np.array([J, J, h, D])
H.gen()

Hp = np.zeros((pxp.dim, pxp.dim))
for n in range(0, np.size(pxp.basis, axis=0)):
    bits = np.copy(pxp.basis[n])
    for m in range(0, pxp.N):
        if bits[m] == 2:
            new_bits = np.copy(bits)
            new_bits[m] = 0
            new_ref = bin_to_int_base_m(new_bits, pxp.base)
            Hp[pxp.keys[new_ref], n] += 2 * (-1)**m
Hm = np.conj(np.transpose(Hp))
Hx = 1 / 2 * (Hp + Hm)

#scarred eigenstates

Example #9

for n in range(0, np.size(k, axis=0)):
    U[int(k[n])] = pxp_syms.basis_transformation(k[n])

wf = np.load("./z3,entangled_MPS_coef," + str(pxp.N) + ".npy")

#project wf to symmetry basis
wf_sym = dict()
for n in range(0, np.size(k, axis=0)):
    wf_sym[int(k[n])] = np.dot(np.conj(np.transpose(U[int(k[n])])), wf)

# dynamics + fidelity
V1_ops = dict()
V1_ops[0] = Hamiltonian(pxp, pxp_syms)
V1_ops[0].site_ops[1] = np.array([[0, 1], [1, 0]])
V1_ops[0].model = np.array([[0, 1, 0, 0]])
V1_ops[0].model_coef = np.array([1])
for n in range(0, np.size(k)):
    V1_ops[0].gen(k_vec=k[n], uc_size=3, uc_pos=1)

V1_ops[1] = Hamiltonian(pxp, pxp_syms)
V1_ops[1].site_ops[1] = np.array([[0, 1], [1, 0]])
V1_ops[1].model = np.array([[0, 0, 1, 0]])
V1_ops[1].model_coef = np.array([1])
for n in range(0, np.size(k)):
    V1_ops[1].gen(k_vec=k[n], uc_size=3, uc_pos=2)

V1_ops[2] = Hamiltonian(pxp, pxp_syms)
V1_ops[2].site_ops[1] = np.array([[0, 1], [1, 0]])
V1_ops[2].model = np.array([[0, 1, 0, 0]])
V1_ops[2].model_coef = np.array([1])
for n in range(0, np.size(k)):

Example #10

#rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
# matplotlib.rcParams['figure.dpi'] = 400

N=16
pxp = unlocking_System([0],"periodic",2,N)
pxp.gen_basis()
pxp_syms = model_sym_data(pxp,[translational(pxp)])

H = Hamiltonian(pxp,pxp_syms)
H.site_ops[1] = np.array([[0,1],[1,0]])
# H.model = np.array([[0,1,0],[0,1,1,1,0]])
H.model = np.array([[0,1,0],[0,0,1,0],[0,1,0,0]])
# H.model_coef = np.array([1,0.122959959])
a=0.108
H.model_coef = np.array([1,a,a])

z=zm_state(2,1,pxp)

k=pxp_syms.find_k_ref(z.ref)
for n in range(0,np.size(k,axis=0)):
    H.gen(k[n])
    H.sector.find_eig(k[n])
    eig_overlap(z,H,k[n]).plot()
plt.title(r"$H=PXP + \lambda PXXXP$, N="+str(pxp.N))
plt.show()

t=np.arange(0,20,0.001)
f0 = fidelity(z,H,"use sym").eval(t,z)
for n in range(0,np.size(f0,axis=0)):
    if f0[n] < 0.1:

Example #11

#rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
# matplotlib.rcParams['figure.dpi'] = 400

N = 15
pxp = unlocking_System([0], "periodic", 2, N)
pxp.gen_basis()
pxp_syms = model_sym_data(pxp, [translational_general(pxp, order=3), PT(pxp)])

z = zm_state(3, 1, pxp)
k = pxp_syms.find_k_ref(z.ref)

V1 = Hamiltonian(pxp, pxp_syms)
V1.site_ops[1] = np.array([[0, 1], [1, 0]])
V1.model = np.array([[0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 1, 0]])
V1.model_coef = np.array([1, 1, 1, 1])
V1.uc_size = np.array([3, 3, 3, 3])
V1.uc_pos = np.array([1, 2, 2, 1])
for n in range(0, np.size(k, axis=0)):
    V1.gen(k_vec=k[n])

V2 = Hamiltonian(pxp, pxp_syms)
V2.site_ops[1] = np.array([[0, 1], [1, 0]])
V2.model = np.array([[0, 0, 1, 0], [0, 1, 0, 0]])
V2.model_coef = np.array([1, 1])
V2.uc_size = np.array([3, 3])
V2.uc_pos = np.array([0, 0])
for n in range(0, np.size(k, axis=0)):
    V2.gen(k_vec=k[n])

V3 = Hamiltonian(pxp, pxp_syms)

Example #12

#rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
# matplotlib.rcParams['figure.dpi'] = 400

N = 14
pxp = unlocking_System([0],"periodic",2,N)
pxp.gen_basis()
pxp_syms=model_sym_data(pxp,[translational(pxp),parity(pxp)])

H0=spin_Hamiltonian(pxp,"x",pxp_syms)
H0.gen()

Hp0 = Hamiltonian(pxp,pxp_syms)
Hp0.site_ops[1] = np.array([[0,1],[0,0]])
Hp0.model = np.array([[1]])
Hp0.model_coef = np.array([1])
Hp0.gen()

Hp1 = Hamiltonian(pxp,pxp_syms)
Hp1.site_ops[1] = np.array([[0,1],[0,0]])
Hp1.site_ops[2] = np.array([[0,0],[1,0]])
Hp1.model = np.array([[0,1,2,1,0]])
Hp1.model_coef = np.array([1])
Hp1.gen()

Hm0 = Hamiltonian(pxp,pxp_syms)
Hm0.site_ops[1] = np.array([[0,0],[1,0]])
Hm0.model = np.array([[1]])
Hm0.model_coef = np.array([1])
Hm0.gen()

Example #13

File: xy_unconstrained_analytic_check.py Project: Cable273/physics_scripts

rc('text', usetex=True)
# matplotlib.rcParams['figure.dpi'] = 400
def com(a,b):
    return np.dot(a,b)-np.dot(b,a)

#init system
N=8
pxp = unlocking_System([0,1],"periodic",2,N)
pxp.gen_basis()
pxp_syms = model_sym_data(pxp,[translational(pxp)])

Hp = Hamiltonian(pxp,pxp_syms)
Hp.site_ops[1] = np.array([[0,0],[1,0]])
Hp.site_ops[2] = np.array([[0,1],[0,0]])
Hp.model = np.array([[1,2],[2,1]])
Hp.model_coef = np.array([1,1])
Hp.uc_size = np.array([2,2])
Hp.uc_pos = np.array([0,1])

Hp.gen()
Hm = Hp.herm_conj()
Hz = 1/2 * com(Hp.sector.matrix(),Hm.sector.matrix())

lie_algebra_num = com(Hz,Hp.sector.matrix())

Hz_an = Hamiltonian(pxp,pxp_syms)
Hz_an.site_ops[1] = np.array([[0,0],[1,0]])
Hz_an.site_ops[2] = np.array([[0,1],[0,0]])
Hz_an.site_ops[3] = np.array([[-1/2,0],[0,1/2]])
Hz_an.site_ops[4] = np.array([[0,0],[0,1]])
Hz_an.model = np.array([[0,4],[4,0],[0,4],[4,0],[1,3,2],[2,3,1],[1,3,2],[2,3,1]])

Example #14

#rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
# matplotlib.rcParams['figure.dpi'] = 400

N = 12
pxp = unlocking_System([0], "periodic", 2, N)
pxp.gen_basis()
pxp_syms = model_sym_data(pxp, [translational(pxp), parity(pxp)])

pert_coef = 0.051
H0 = spin_Hamiltonian(pxp, "x", pxp_syms)
V = Hamiltonian(pxp, pxp_syms)
V.site_ops[1] = np.array([[0, 1], [1, 0]])
V.site_ops[2] = np.array([[-1, 0], [0, 1]])
V.model = np.array([[2, 0, 1, 0], [0, 1, 0, 2]])
V.model_coef = np.array([1, 1])
V.gen()
H0.gen()
H = H_operations.add(H0, V, np.array([1, -pert_coef]))


#find rescaling such that E1-E0 = 1
def gap(H, c):
    e, u = np.linalg.eigh(c * H)
    gap = e[1] - e[0]
    # print(gap,c)
    return gap


from scipy.optimize import minimize_scalar
res = minimize_scalar(lambda c: np.abs(gap(H.sector.matrix(), c) - 1),

Example #15

File: LongRangeIsing.py Project: Cable273/physics_scripts

#create Hamiltonian
model =[]
model_coef = []
for n in range(2,N+1):
    temp = -np.ones(n)
    temp[0] = 1
    temp[np.size(temp,axis=0)-1] = 1
    d = np.size(temp)-1
    if d > int(N/2):
        d = N - d
    model_coef = np.append(model_coef,J/np.power(d,alpha))
    model.append(temp)
Hz = Hamiltonian(pxp,pxp_syms)
Hz.site_ops[1] = np.array([[-1,0],[0,1]])
Hz.model = model
Hz.model_coef = model_coef

Hx = Hamiltonian(pxp,pxp_syms)
Hx.site_ops[1] = np.array([[0,1],[1,0]])
Hx.model = np.array([[1]])
Hx.model_coef = np.array([-B])

k=[0]
z=ref_state(np.max(pxp.basis_refs),pxp)
Hz.gen(k)
Hx.gen(k)
H = H_operations.add(Hz,Hx,np.array([1,1]))
H.sector.find_eig(k)

overlap = eig_overlap(z,H,k).eval()
plt.scatter(H.sector.eigvalues(k),overlap)

Example #16

File: scar_approx_projected_time_evolved.py Project: Cable273/physics_scripts

#rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
# matplotlib.rcParams['figure.dpi'] = 400

N = 16
pxp = unlocking_System([0], "periodic", 2, N)
pxp.gen_basis()
pxp_syms = model_sym_data(pxp, [translational(pxp), parity(pxp)])

H0 = spin_Hamiltonian(pxp, "x", pxp_syms)
H0.gen()

Hpe = Hamiltonian(pxp, pxp_syms)
Hpe.site_ops[1] = np.array([[0, 1], [0, 0]])
Hpe.model = np.array([[0, 1, 0]])
Hpe.model_coef = np.array([1])
Hpe.gen(parity=0)

Hpo = Hamiltonian(pxp, pxp_syms)
Hpo.site_ops[1] = np.array([[0, 1], [0, 0]])
Hpo.model = np.array([[0, 1, 0]])
Hpo.model_coef = np.array([1])
Hpo.gen(parity=1)

Hme = Hamiltonian(pxp, pxp_syms)
Hme.site_ops[1] = np.array([[0, 0], [1, 0]])
Hme.model = np.array([[0, 1, 0]])
Hme.model_coef = np.array([1])
Hme.gen(parity=0)

Hmo = Hamiltonian(pxp, pxp_syms)

Example #17

count = 0
pxp_config = model_space.basis[indices[0][index_row_loc[count]]]
pxxxp_config = model_space.basis[indices[1][index_row_loc[count]]]

print(pxp_config, pxxxp_config)
# print(index_spacing_error[count])
# print(index_var_error[count])

Hp = gen_Hp(pxp_config, pxxxp_config)
Hm = np.conj(np.transpose(Hp))

Hp_test0 = Hamiltonian(pxp)
Hp_test0.site_ops[1] = np.array([[0, 0], [1, 0]])
Hp_test0.site_ops[2] = np.array([[0, 1], [0, 0]])
Hp_test0.model = np.array([[0, 1, 0], [0, 2, 0]])
Hp_test0.model_coef = np.array([1, 1])
Hp_test0.uc_size = np.array([2, 2])
Hp_test0.uc_pos = np.array([1, 0])

Hp_test = Hamiltonian(pxp)
Hp_test.site_ops[1] = np.array([[0, 0], [1, 0]])
Hp_test.site_ops[2] = np.array([[0, 1], [0, 0]])
Hp_test.model = np.array([[0, 2, 1, 2, 0]])
Hp_test.model_coef = np.array([1])

Hp_test0.gen()
Hp_test.gen()
from Hamiltonian_Classes import H_operations
Hp_test = H_operations.add(Hp_test0, Hp_test, np.array([1, coef_f0]))
print((np.abs(Hp_test.sector.matrix() - Hp) < 1e-5).all())

Example #18

File: pcp,2ndOrderErrors.py Project: Cable273/physics_scripts

    return exp(Q2, psi) - exp(Q, psi)**2


#init system
N = 14
pxp = unlocking_System([0], "periodic", 3, N)
pxp.gen_basis()
pxp_syms = model_sym_data(pxp, [translational_general(pxp, order=2), PT(pxp)])

#orig H
Ip = dict()
Ip[0] = Hamiltonian(pxp, pxp_syms)
Ip[0].site_ops[1] = np.array([[0, 1, 0], [0, 0, 0], [0, 0, 0]])
Ip[0].site_ops[2] = np.array([[0, 0, 0], [1, 0, 0], [0, 0, 0]])
Ip[0].model = np.array([[0, 1, 0], [0, 2, 0]])
Ip[0].model_coef = np.array([1, -1])
Ip[0].uc_size = np.array([2, 2])
Ip[0].uc_pos = np.array([1, 0])

Ip[1] = Hamiltonian(pxp, pxp_syms)
Ip[1].site_ops[1] = np.array([[0, 1, 0], [0, 0, 0], [0, 0, 0]])
Ip[1].site_ops[2] = np.array([[0, 0, 0], [1, 0, 0], [0, 0, 0]])
Ip[1].model = np.array([[0, 2, 0, 0], [0, 0, 2, 0], [0, 1, 0, 0], [0, 0, 1,
                                                                   0]])
Ip[1].model_coef = np.array([1, 1, -1, -1])
Ip[1].uc_size = np.array([2, 2, 2, 2])
Ip[1].uc_pos = np.array([0, 1, 1, 0])

Ip[2] = Hamiltonian(pxp, pxp_syms)
Ip[2].site_ops[1] = np.array([[0, 1, 0], [0, 0, 0], [0, 0, 0]])
Ip[2].site_ops[2] = np.array([[0, 0, 0], [1, 0, 0], [0, 0, 0]])

Example #19

File: fsa_clock_su2.py Project: Cable273/physics_scripts

z = zm_state(2, 1, pxp)

#krylov basis
krylov_dim = 2 * pxp.N
krylov_basis = gen_krylov_basis(H.sector.matrix(),
                                krylov_dim,
                                z.prod_basis(),
                                pxp,
                                orth="qr")

#FSA basis
# P+P on even sites
pe = Hamiltonian(pxp, pxp_syms)
pe.site_ops[1] = np.array([[0, 1, 1j], [-1, 0, 0], [-1j, 0, 0]])
pe.model = np.array([[0, 1, 0]])
pe.model_coef = np.array([1])
pe.gen(parity=1)
#P-P on odd sites
mo = Hamiltonian(pxp, pxp_syms)
mo.site_ops[1] = np.array([[0, -1, 1j], [1, 0, 0], [-1j, 0, 0]])
mo.model = np.array([[0, 1, 0]])
mo.model_coef = np.array([1])
mo.gen(parity=0)
#Raising op
Hp = H_operations.add(pe, mo, np.array([1, 1]))
Hp = Hp.sector.matrix()
Hm = np.conj(np.transpose(Hp))


def com(a, b):
    return np.dot(a, b) - np.dot(b, a)

Example #20

File: ising_su2_decomp_opt.py Project: Cable273/physics_scripts

pxp_syms = model_sym_data(pxp, [translational(pxp), parity(pxp)])

# J = -1
# hx = 0.05
# hz = -0.5

J = 1
hx = 1
hz = 1
Hp = dict()
Hp[0] = Hamiltonian(pxp, pxp_syms)
Hp[0].site_ops[1] = np.array([[0, 0], [1, 0]])
Hp[0].site_ops[2] = np.array([[0, 1], [0, 0]])
Hp[0].site_ops[3] = np.array([[-1 / 2, 0], [0, 1 / 2]])
Hp[0].model = np.array([[1, 1], [2, 2], [1, 2], [2, 1], [1], [2], [3]])
Hp[0].model_coef = np.array(
    [J / 4, J / 4, J / 4, J / 4, hx / 2, hx / 2, hz / 2])
Hp[0].uc_size = np.array([2, 2, 2, 2, 2, 2, 1])
Hp[0].uc_pos = np.array([0, 1, 0, 1, 0, 1, 0])

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].site_ops[3] = np.array([[-1 / 2, 0], [0, 1 / 2]])
Hp[1].site_ops[4] = np.array([[0, 0], [0, 1]])
Hp[1].model = np.array([[2, 2, 4], [2, 2, 0], [1, 1, 0], [1, 1, 4]])
Hp[1].model_coef = np.array([1, 1, 1, 1])
Hp[1].uc_size = np.array([2, 2, 2, 2])
Hp[1].uc_pos = np.array([1, 1, 0, 0])

Hp[2] = Hamiltonian(pxp, pxp_syms)
Hp[2].site_ops[1] = np.array([[0, 0], [1, 0]])

Example #21

File: dynamics.py Project: Cable273/physics_scripts

to_keep = np.zeros(pxp.dim)
decomp_ref = []
pbar = ProgressBar()
for n in pbar(range(0, np.size(decompBasis.basis, axis=0))):
    Hp = Hamiltonian(pxp)
    Hp.site_ops[1] = np.array([[0, 0], [1, 0]])
    Hp.site_ops[2] = np.array([[0, 1], [0, 0]])
    model = []
    for m in range(0, np.size(decompBasis.basis[n], axis=0)):
        if decompBasis.basis[n][m] == 1:
            model.append([0, 1, 0])
        else:
            model.append([0, 2, 0])
    Hp.model = model
    Hp.model_coef = np.ones(pxp.N)
    Hp.uc_size = pxp.N * np.ones(pxp.N)
    Hp.uc_pos = np.arange(0, pxp.N)

    Hp.gen()
    Hm = Hp.herm_conj()

    Hz = 1 / 2 * com(Hp.sector.matrix(), Hm.sector.matrix())
    Hx = 1 / 2 * (Hp.sector.matrix() + Hm.sector.matrix())
    Hy = 1 / 2j * (Hp.sector.matrix() - Hm.sector.matrix())
    C = np.dot(Hx, Hx) + np.dot(Hy, Hy) + np.dot(Hz, Hz)

    ec, uc = np.linalg.eigh(C)
    for m in range(0, np.size(ec, axis=0)):
        if np.abs(var(Hz, uc[:, 0])) < 1e-5:
            to_keep = np.vstack((to_keep, uc[:, m]))

Example #22

File: pxxxp_dynamics.py Project: Cable273/physics_scripts

from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Computer Modern'],'size':26})
## for Palatino and other serif fonts use:
#rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
# matplotlib.rcParams['figure.dpi'] = 400

N=16
pxp = unlocking_System([0],"periodic",2,N)
pxp.gen_basis()
pxp_syms = model_sym_data(pxp,[translational(pxp)])

#create Hamiltonian
a=0.1229
H = Hamiltonian(pxp,pxp_syms)
H.site_ops[1] = np.array([[0,1],[1,0]])
H.model = np.array([[1],[0,1,1,1,0]])
H.model_coef = np.array([0,1])

z=zm_state(2,1,pxp)
k=pxp_syms.find_k_ref(z.ref)
for n in range(0,np.size(k,axis=0)):
    H.gen(k[n])
    H.sector.find_eig(k[n])
    eig_overlap(z,H,k[n]).plot()
plt.show()

fidelity(z,H,"use sym").plot(np.arange(0,20,0.01),z)
plt.show()

Example #23

z = zm_state(2, 1, pxp)
Neel_state_spin_basis = np.zeros(np.size(pxp.basis_refs))
Neel_state_spin_basis[pxp.keys[z.ref]] = 1
Neel_state_clock_basis = np.dot(spin2clock_u, Neel_state_spin_basis)

z = zm_state(2, 1, pxp)
Neel_state_clock_basis = np.zeros(np.size(pxp.basis_refs))
Neel_state_clock_basis[pxp.keys[z.ref]] = 1
Neel_state_spin_basis = np.dot(clock2spin_u, Neel_state_clock_basis)
print(Neel_state_spin_basis)

H = Hamiltonian(pxp, pxp_syms)
H.site_ops[2] = P_spin_clock_basis
H.site_ops[1] = H1.site_ops[1]
H.model = np.array(([[2, 1, 2]]))
H.model_coef = np.array((1))
H.gen()
H.sector.find_eig()
print(H.sector.eigvalues())
# eig_overlap.plot(z.bits,H)
# plt.show()
# fidelity.plot(z.bits,np.arange(0,20,0.01),H)
# plt.show()

H_clock = Hamiltonian(pxp, pxp_syms)
H_clock.site_ops[2] = P
H_clock.site_ops[1] = H2.site_ops[1]
H_clock.model = np.array([[2, 1, 2]])
H_clock.model_coef = np.array((1))

# H_clock = clock_Hamiltonian(pxp,pxp_syms)

Example #24

z = zm_state(2, 1, pxp)
# z=zm_state(3,1,pxp)
# z=ref_state(1,pxp)
hamming_sectors = find_hamming_sectors(z.bits)
hamming_length = 0
for n in range(0, len(hamming_sectors)):
    if np.size(hamming_sectors[n]) != 0:
        hamming_length = hamming_length + 1

# H=spin_Hamiltonian(pxp,"x")
# H.gen()

V = Hamiltonian(pxp)
V.site_ops[1] = np.array([[0, 0], [0, 1]])
V.model = np.array([[1, 1]])
V.model_coef = np.array([1])
V.gen()

removed_count = np.zeros(len(hamming_sectors))
for n in range(0, len(hamming_sectors)):
    c = 0
    for m in range(0, np.size(hamming_sectors[n], axis=0)):
        ref = hamming_sectors[n][m]
        index = pxp.keys[ref]
        if V.sector.matrix()[index, index] != 0:
            c = c + 1
        # c=c+V.sector.matrix()[index,index]
    removed_count[n] = c
plt.plot(removed_count)
plt.show()

Example #25

File: fsa_2nd_band_directed_cube_recursive.py Project: Cable273/physics_scripts

# basis = np.hstack((basis,subcube_basis(root_refs,"Left",6)))

# print("\n")
# root_refs = find_root_refs(np.array([1,4]),np.array([2,4]),H,sector_refs,from_sector,pxp)
# basis = np.hstack((basis,subcube_basis(root_refs,"Right",6)))

# print("\n")
# root_refs = find_root_refs(np.array([3,0]),np.array([3,3]),H,sector_refs,from_sector,pxp)
# basis = np.hstack((basis,subcube_basis(root_refs,"Left",6)))

# include Neel fsa (orig)
#fsa Hamiltonian, H= H+ + H-
Hp1 = Hamiltonian(pxp)
Hp1.site_ops[1] = np.array([[0, 1], [0, 0]])
Hp1.model = np.array([[1]])
Hp1.model_coef = np.array([1])
Hp1.gen(parity=0)
Hp2 = Hamiltonian(pxp)
Hp2.site_ops[1] = np.array([[0, 0], [1, 0]])
Hp2.model = np.array([[1]])
Hp2.model_coef = np.array([1])
Hp2.gen(parity=1)
Hm = Hp1.sector.matrix() + Hp2.sector.matrix()
Hp = np.conj(np.transpose(Hm))

#fsa basis from Neel
z = zm_state(2, 1, pxp)
fsa_basis = z.prod_basis()
current_state = fsa_basis
fsa_dim = pxp.N
for n in range(0, fsa_dim):

Example #26

File: example_dynamics_Ising.py Project: Cable273/Exact_Diagonalization

rc('text', usetex=True)
# matplotlib.rcParams['figure.dpi'] = 400

N = 10
J = 1
hx = 1
hz = 1
#init system
system = unlocking_System([0, 1], "periodic", 2, N)
system.gen_basis()
system_syms = model_sym_data(system, [translational(system)])

#create Hamiltonian
H = Hamiltonian(system, system_syms)
H.site_ops[1] = np.array([[0, 1], [1, 0]])
H.site_ops[2] = np.array([[-1, 0], [0, 1]])
H.model = np.array([[1, 1], [2], [1]])
H.model_coef = np.array([J, hz, hx])

#dynamics following quench from |00000>
psi = ref_state(0, system)
k = system_syms.find_k_ref(psi.ref)
for n in range(0, np.size(k, axis=0)):
    H.gen(k[n])
    H.sector.find_eig(k[n])
    print(H.sector.eigvalues(k[n]))
    eig_overlap(psi, H, k[n]).plot()
plt.show()
fidelity(psi, H, "use sym").plot(np.arange(0, 20, 0.01), psi)
plt.show()

Example #27

def com(a, b):
    return np.dot(a, b) - np.dot(b, a)


#init system
N = 21
pxp = unlocking_System([0], "periodic", 2, N)
pxp.gen_basis()
pxp_syms = model_sym_data(pxp, [translational_general(pxp, order=3)])

H0 = Hamiltonian(pxp, pxp_syms)
H0.site_ops[1] = np.array([[0, 1], [1, 0]])
H0.model = np.array([[1]])
H0.model_coef = np.array([1])

H1 = Hamiltonian(pxp, pxp_syms)
H1.site_ops[1] = np.array([[0, 1], [1, 0]])
H1.model = np.array([[0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 1, 0]])
H1.model_coef = np.array([1, 1, 1, 1])
H1.uc_size = np.array([3, 3, 3, 3])
H1.uc_pos = np.array([1, 2, 2, 1])

k = [0]
H0.gen(k)
H1.gen(k)
# H0.gen()
# H1.gen()
H = H_operations.add(H0, H1, np.array([1, -1]))

Example #28

File: kicked_pxp,pxp_comp,minimal_angle.py Project: Cable273/physics_scripts

Y.site_ops[1] = np.array([[0, -1j], [1j, 0]])
Z.site_ops[1] = np.array([[-1, 0], [0, 1]])

X.model, X.model_coef = np.array([[1]]), np.array((1))
Y.model, Y.model_coef = np.array([[1]]), np.array((1))
Z.model, Z.model_coef = np.array([[1]]), np.array((1))

X.gen()
Y.gen()
Z.gen()

#n_i n_i+1
H0 = Hamiltonian(pxp, pxp_syms)
H0.site_ops[1] = np.array([[0, 0], [0, 1]])
H0.model = np.array([[1, 1]])
H0.model_coef = np.array((1))

#X_n
H_kick = Hamiltonian(pxp, pxp_syms)
H_kick.site_ops[1] = np.array([[0, 1], [1, 0]])
H_kick.model = np.array([[1]])
H_kick.model_coef = np.array((1))

H0.gen()
H_kick.gen()
H0.sector.find_eig()
H_kick.sector.find_eig()

# for tau in range
c = 0
pbar = ProgressBar()

Example #29

import numpy as np
import scipy as sp
import math
from Calculations import plot_adjacency_graph

N = 12
pxp = unlocking_System([0], "periodic", 2, N)
pxp.gen_basis()
pxp_syms = model_sym_data(pxp, [translational(pxp)])

H = Hamiltonian(pxp, pxp_syms)
H.site_ops[1] = np.array([[0, 0], [1, 0]])
H.site_ops[2] = np.array([[0, 1], [0, 0]])
H.model = np.array([[0, 1, 2, 0], [0, 2, 1, 0]])
H.model_coef = np.array([1, 1])
k = [0]
H.gen(k)
# H.gen()

# H0=spin_Hamiltonian(pxp,"x",pxp_syms)
# H0.gen(k)
# # H = H_operations.add(H0,H,np.array([1,1/2]))
# H = H_operations.add(H0,H,np.array([1,1]))

# all states
# basis_labels = dict()
# for n in range(0,np.size(pxp.basis,axis=0)):
# basis_labels[n] = pxp.basis[n]
# plot_adjacency_graph(np.abs(H.sector.matrix()),labels=basis_labels)
# # plt.title(r"$P^0 X P^1 + P^1 X P^0$, N="+str(pxp.N))

Example #30

    Q2 = np.dot(Q,Q)
    return exp(Q2,psi)-exp(Q,psi)**2

#init system
N=16
pxp = unlocking_System([0],"periodic",2,N)
pxp.gen_basis()
pxp_syms = model_sym_data(pxp,[translational_general(pxp,order=4)])
# pxp_syms = model_sym_data(pxp,[translational(pxp)])

Hp = dict()
Hp[0] = Hamiltonian(pxp,pxp_syms)
Hp[0].site_ops[1] = np.array([[0,0],[1,0]])
Hp[0].site_ops[2] = np.array([[0,1],[0,0]])
Hp[0].model = np.array([[0,2,0],[0,1,0],[0,1,0],[0,1,0]])
Hp[0].model_coef = np.array([1,1,1,1])
Hp[0].uc_size = np.array([4,4,4,4])
Hp[0].uc_pos = np.array([3,0,1,2])

# 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([[0,1,2,1,0]])
# Hp[1].model_coef = np.array([1])
# Hp[1].uc_size = np.array([4])
# Hp[1].uc_pos = np.array([0])

# 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].model = np.array([[0,2,1,2,0],[0,2,1,2,0]])