import numpy as np
from qutip import Qobj
import tensorflow as tf
import warnings
from qsttoolkit.tomography.QST import QuantumStateTomography, parametrize_density_matrix, reconstruct_density_matrix
from qsttoolkit.tomography.tradqst.MLE_reconstructor.train import train
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class MLEQuantumStateTomography(QuantumStateTomography):
"""A class for performing maximum likelihood estimation quantum state tomography."""
def __init__(self, dim=None):
if dim: warnings.warn("dim is no longer required for this class and will be removed in a future version.", DeprecationWarning, stacklevel=2)
super().__init__()
self.params = tf.Variable([], dtype=tf.complex128)
[docs]
def reconstruct(self, measurement_data: list, measurement_operators: list, initial_dm: np.ndarray, epochs: int=500, optimizer: tf.keras.optimizers.Optimizer=tf.keras.optimizers.Adam(learning_rate=0.01), L1_reg: float=0.0, verbose_interval: int=None, num_progress_saves: int=None, true_dm: tf.Tensor=None, time_log_interval: int=None, method=None, verbose=None):
"""
Fits the density matrix to the measurement data using maximum likelihood estimation.
Parameters
----------
measurement_data : list of np.ndarray
Frequency of each measurement outcome.
measurement_operators : list of np.ndarray
Projective operators corresponding to the measurement outcomes.
initial_dm : np.ndarray
Initial density matrix guess.
epochs : int
Number of optimization epochs. Defaults to 500.
optimizer : tf.keras.optimizers.Optimizer
Optimizer to use for the reconstruction. Defaults to Adam with learning rate 0.01.
L1_reg : float
L1 regularisation parameter. Defaults to 0.
verbose_interval : int
Interval at which to print progress updates. Defaults to None.
num_progress_saves : int
Number of intermediate progress saves to make. Defaults to None.
true_dm : tf.Tensor
True density matrix used for calculating fidelities. Defaults to None.
time_log_interval : int
Interval at which to log the time taken after each epoch. Defaults to None.
"""
if method: warnings.warn("method is deprecated and will be removed in a future version.", DeprecationWarning, stacklevel=2)
if verbose:
warnings.warn("verbose is deprecated and will be removed in a future version. Please use verbose_interval instead.", DeprecationWarning, stacklevel=2)
# Input error handling
if len(measurement_data[0]) != len(measurement_operators):
raise ValueError("measurement_data[0] and measurement_operators must have the same length.")
if not all([isinstance(data, np.ndarray) for data in measurement_data]):
raise ValueError("All elements of measurement_data must be numpy arrays.")
if not all([isinstance(op, np.ndarray) for op in measurement_operators]):
raise ValueError("All elements of measurement_operators must be numpy arrays.")
if not isinstance(verbose_interval, int) and verbose_interval is not None:
raise ValueError("verbose_interval must be an integer.")
if not isinstance(num_progress_saves, int) and num_progress_saves is not None:
raise ValueError("num_progress_saves must be an integer.")
if not isinstance(time_log_interval, int) and time_log_interval is not None:
raise ValueError("time_log_interval must be an integer.")
# Ensure initial density matrix is array-like
if type(initial_dm) == Qobj:
initial_dm = initial_dm.full()
elif type(initial_dm) == tf.Tensor:
initial_dm = initial_dm.numpy()
self.initial_dm = tf.cast(initial_dm, dtype=tf.complex128)
# Convert initial density matrix into trainable Cholesky decomposition
self.params = tf.Variable(parametrize_density_matrix(self.initial_dm), dtype=tf.complex128)
self.optimizer = optimizer
self.losses, self.progress_saves, self.fidelities, self.times = train(self.params,
measurement_data,
measurement_operators,
epochs=epochs,
optimizer=self.optimizer,
L1_reg=L1_reg,
verbose_interval=verbose_interval,
num_progress_saves=num_progress_saves,
true_dm=true_dm,
time_log_interval=time_log_interval)
# Convert final parameters back to density matrix
self.reconstructed_dm = reconstruct_density_matrix(self.params).numpy()
if verbose_interval: print("Reconstruction complete.")
[docs]
def plot_cost_values(self):
"""Deprecated alias for plot_losses."""
warnings.warn("plot_cost_values is deprecated and will be removed in a future version. Please use plot_losses instead.", DeprecationWarning, stacklevel=2)
self.plot_losses()