See the changelog for release notes and migration guidance.

Extensions

CeNTREX-TlF

Code for generating the CeNTREX TlF States, Hamiltonians, Transitions, Couplings and Lindblad equations.

Consists of six modules:

• states
• hamiltonian
• transitions
• couplings
• lindblad
• utils

states has code to generate states and the classes that describe the CoupledBasisState, UncoupledBasisState and State; where State holds multiple CoupledBasisStates or UncoupledBasisStates with different amplitudes, i.e. when superpositions arise.

Lindblad Solver Performance Options

Rust-backed Lindblad solves default to execution_mode="expanded_sparse". This mode uses a packed Hermitian density matrix and, by default, groups diagonal-real and complex input terms in the RHS kernel. The grouping can be controlled with use_split_input_rhs on solve_lindblad, solve_lindblad_batch, parameter_scan, initial_condition_scan, and grid_scan.

result = solve_lindblad(
    prepared,
    rho0,
    (0.0, t_end),
    execution_mode="expanded_sparse",
    use_split_input_rhs=True,  # default
)

Set use_split_input_rhs=False to keep the same expanded-sparse coefficient representation while disabling input-term grouping. This is mainly useful for benchmarking or checking full-system performance regressions; compact systems usually benefit from the default grouped path.

Dependencies

• numpy
• scipy
• sympy
• pandas

Installation

python -m pip install .
where . is the path to the directory. To install directly from Github use:
python -m pip install git+https://github.com/ograsdijk/CeNTREX-TlF

states

states contains the functions and classes to represent the TlF states:
CoupledBasisState is a class representing a TlF state with coupled quantum numbers, i.e. F, mF, F1, J, I1, I2, Ω, P.
UncoupledBasisState is a class representing a TlF state with uncoupled quantum numbers, i.e. J, mJ, I1, m1, I2, m2, Ω, P.
Finally State is a class representing a collection of states, since in most cases the TlF molecules are in a superposition state.

from centrex_tlf import states
states.CoupledBasisState(F=1, mF=0, F1 = 1/2, J = 0, I1 = 1/2, I2 = 1/2, Omega = 0, P = 1)

or using some of the functions to generate all hyperfine substates in a given J level:

from centrex_tlf import states
QN = states.generate_uncoupled_states_ground(Js = [0,1])

which returns an array containing the UncoupledBasisStates

array([|X, J = 0, mJ = 0, I₁ = 1/2, m₁ = -1/2, I₂ = 1/2, m₂ = -1/2, P = +, Ω = 0>,
       |X, J = 0, mJ = 0, I₁ = 1/2, m₁ = -1/2, I₂ = 1/2, m₂ = 1/2, P = +, Ω = 0>,
       |X, J = 0, mJ = 0, I₁ = 1/2, m₁ = 1/2, I₂ = 1/2, m₂ = -1/2, P = +, Ω = 0>,
       |X, J = 0, mJ = 0, I₁ = 1/2, m₁ = 1/2, I₂ = 1/2, m₂ = 1/2, P = +, Ω = 0>,
       |X, J = 1, mJ = -1, I₁ = 1/2, m₁ = -1/2, I₂ = 1/2, m₂ = -1/2, P = -, Ω = 0>,
       |X, J = 1, mJ = -1, I₁ = 1/2, m₁ = -1/2, I₂ = 1/2, m₂ = 1/2, P = -, Ω = 0>,
       |X, J = 1, mJ = -1, I₁ = 1/2, m₁ = 1/2, I₂ = 1/2, m₂ = -1/2, P = -, Ω = 0>,
       |X, J = 1, mJ = -1, I₁ = 1/2, m₁ = 1/2, I₂ = 1/2, m₂ = 1/2, P = -, Ω = 0>,
       |X, J = 1, mJ = 0, I₁ = 1/2, m₁ = -1/2, I₂ = 1/2, m₂ = -1/2, P = -, Ω = 0>,
       |X, J = 1, mJ = 0, I₁ = 1/2, m₁ = -1/2, I₂ = 1/2, m₂ = 1/2, P = -, Ω = 0>,
       |X, J = 1, mJ = 0, I₁ = 1/2, m₁ = 1/2, I₂ = 1/2, m₂ = -1/2, P = -, Ω = 0>,
       |X, J = 1, mJ = 0, I₁ = 1/2, m₁ = 1/2, I₂ = 1/2, m₂ = 1/2, P = -, Ω = 0>,
       |X, J = 1, mJ = 1, I₁ = 1/2, m₁ = -1/2, I₂ = 1/2, m₂ = -1/2, P = -, Ω = 0>,
       |X, J = 1, mJ = 1, I₁ = 1/2, m₁ = -1/2, I₂ = 1/2, m₂ = 1/2, P = -, Ω = 0>,
       |X, J = 1, mJ = 1, I₁ = 1/2, m₁ = 1/2, I₂ = 1/2, m₂ = -1/2, P = -, Ω = 0>,
       |X, J = 1, mJ = 1, I₁ = 1/2, m₁ = 1/2, I₂ = 1/2, m₂ = 1/2, P = -, Ω = 0>],
      dtype=object)

State objects, which are superpositions of BasisStates are also generated easily:

superposition = 1*QN[0] + 0.1j*QN[1]

which returns

1.00 x |X, J = 0, mJ = 0, I₁ = 1/2, m₁ = -1/2, I₂ = 1/2, m₂ = -1/2, P = +, Ω = 0>
0.00+0.10j x |X, J = 0, mJ = 0, I₁ = 1/2, m₁ = -1/2, I₂ = 1/2, m₂ = 1/2, P = +, Ω = 0>

A subset of State, CoupledBasisStates can be selected with the QuantumSelector as follows:

QN = states.generate_coupled_states_ground(Js = [0,1])
qn_select = states.QuantumSelector(J = 1, mF = 0, electronic = states.ElectronicState.X)
qn_select.get_indices(QN)

which returns all the indices with J=1 and mJ=0:

array([ 4,  6,  9, 13], dtype=int64)

hamiltonian

hamiltonian contains the functions to generate TlF hamiltonians in the X and B state in either coupled or uncoupled form.
Generating a ground state X hamiltonian can be accomplished easily using some convenience functions:

from centrex_tlf import states, hamiltonian

# generate the hyperfine sublevels in J=0 and J=1
QN = states.generate_uncoupled_states_ground(Js = [0,1])

# generate a dictionary with X hamiltonian terms
H = hamiltonian.generate_uncoupled_hamiltonian_X(QN)

# create a function outputting the hamiltonian as a function of E and B
Hfunc = hamiltonian.generate_uncoupled_hamiltonian_X_function(H)

All functions generating hamiltonians only require a list or array of TlF states. Generating the hamiltonian only for certain hyperfine sublevels is hence also straightforward. The function calculate_uncoupled_hamiltonian_X calculates the hamiltonians from scratch, whereas generate_uncoupled_hamiltonian_X pulls the non-zero elements from an sqlite database.

To convert a hamiltonian from one basis to another transformation matrices can be generated or calculated (generate_transform_matrix pulls non-zero matrix elements from an sqlite database, calculate_transform_matrix does the full element wise calculation):

from centrex_tlf import states, hamiltonian

# generate the hyperfine sublevels in J=0 and J=1
QN = states.generate_uncoupled_states_ground(Js = [0,1])
# generate the coupled hyperfine sublevels in J=0 and J=1
QNc = states.generate_coupled_states_ground(Js = [0,1])

# generate a dictionary with X hamiltonian terms
H = hamiltonian.generate_uncoupled_hamiltonian_X(QN)
Hfunc = hamiltonian.generate_uncoupled_hamiltonian_X_function(H)
H0 = Hfunc(E = [0,0,0], B = [0,0,1e-3])

# generate the transformation matrix
transform = hamiltonian.generate_transform_matrix(QN, QNc)

# calculate the transformed matrix
H0c = transform.conj().T@H0@transform

This is mostly used for optical bloch simulations where the coupled states representation is more convenient.

Stark Shift Example

To calculate the energy levels as a function of the electric field the following code can be used, which calculates all energies up to J=6 but only plots the |J=2, mJ=0> hyperfine levels. These are the states focussed by the electrostatic quadrupole lens in the CeNTREX experiment.

import numpy as np
import matplotlib.pyplot as plt

from centrex_tlf import states, hamiltonian

# generate states up to J=6
QN = states.generate_uncoupled_states_ground(Js=np.arange(7))

# generate the X hamiltonian terms
H = hamiltonian.generate_uncoupled_hamiltonian_X(QN)

# create a function outputting the hamiltonian as a function of E and B
Hfunc = hamiltonian.generate_uncoupled_hamiltonian_X_function(H)

# V/cm
Ez = np.linspace(0, 50e3, 101)

# generate the Hamiltonian for (almost) zero field, add a small field to make states
# non-degenerate
Hi = Hfunc(E=[0, 0, 1e-3], B=[0, 0, 1e-3])
E, V = np.linalg.eigh(Hi)

# get the true superposition-states of the system
QN_states = hamiltonian.matrix_to_states(V, QN)

# original eigenvectors used in tracking states as energies change order
V_track = V.copy()

# indices of the J=2, mJ=0 states focused by the lens
indices_J2_mJ0 = [
    idx
    for idx, s in enumerate(QN_states)
    if s.largest.J == 2 and s.largest.mJ == 0
]

indices_J012 = [
    idx for idx, s in enumerate(QN_states) if s.largest.J in [0, 1, 2]
]

# empty array for storing energies
energy = np.empty([Ez.size, len(QN)], dtype=np.complex128)

# iterate over the electric field values
for idx, Ei in enumerate(Ez):
    Hi = Hfunc(E=[0, 0, Ei], B=[0, 0, 1e-3])
    E, V = np.linalg.eigh(Hi)

    # sort indices to keep the state order the same
    indices = np.argmax(np.abs(V_track.conj().T @ V), axis=1)
    energy[idx, :] = E[indices]
    V_track[:, :] = V[:, indices]

# plot the J=2, mJ=0 Stark curves
fig, ax = plt.subplots(figsize=(12, 8))
ax.plot(
    Ez,
    (energy.real[:, indices_J2_mJ0] - energy.real[:, indices_J2_mJ0][0, 0])
    / (2 * np.pi * 1e9),
)
ax.set_xlabel("E [V/cm]")
ax.set_ylabel("Energy [GHz]")
ax.set_title("|J=2, mJ=0> Stark Curve")
ax.grid(True)
plt.show()

couplings

Code for generating the CeNTREX TlF couplings. Includes code for generating branching ratios, electric dipole coupling elements and coupling fields

Generating branching ratios

The code below generates branching ratios from |J'=1, F1'=1/2, mF=0> to all states in the J=1 manifold.

from centrex_tlf import states, couplings

excited_state = states.CoupledBasisState(
    J=1, F1=1 / 2, F=1, mF=0, I1=1 / 2, I2=1 / 2, Omega=1, P=1
)
qn_select = states.QuantumSelector(J=1)
ground_states = [1*s for s in states.generate_coupled_states_X(qn_select)]
br = couplings.calculate_br(1 * excited_state, ground_states)

Generating couplings

The code below generates the coupling fields for the J=1 manifold to the J'=1, F1'=1/2, F'=1 manifold. The returned value is a dataclass CouplingFields containing the following fields:

• ground_main
• excited_main
• main_coupling: the electric dipole coupling between ground_main and excited_main
• ground_states: list of all ground states
• excited_states: list of all excited states
• fields: a list of CouplingField dataclasses with the following fields:
  • polarization: polarization vector
  • field: coupling field in the ground_states + excited_states basis

from centrex_tlf import states, couplings

qn_select = states.QuantumSelector(J=1)
ground_states = states.generate_coupled_states_X(qn_select)

qn_select = states.QuantumSelector(J=1, F1=1 / 2, F=1, P=1, Ω=1)
excited_states = states.generate_coupled_states_B(qn_select)

# the generate_coupling_field_* functions requires lists as inputs, not np.ndarrays
QN = list(1 * np.append(ground_states, excited_states))
ground_states = [1*s for s in  ground_states]
excited_states = [1*s for s in excited_states]

H_rot = np.eye(len(QN), dtype=complex) * np.arange(len(QN))
V_ref = np.eye(len(QN))
pol_vecs = [np.array([0.0, 0.0, 1.0]), np.array([1.0, 0.0, 0.0])]
normalize_pol = True

coupling = couplings.generate_coupling_field_automatic(
    ground_states_approx = ground_states, 
    excited_states_approx = excited_states, 
    QN_basis = QN,
    H_rot = H_rot, 
    QN = QN, 
    V_ref = V_ref, 
    pol_vecs = pol_vecs, 
    normalize_pol = normalize_pol
)