# Código fonte para qiskit.ignis.verification.tomography.fitters.state_fitter

```
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Maximum-Likelihood estimation quantum state tomography fitter
"""
from typing import List, Union
import numpy as np
from qiskit.result import Result
from qiskit import QuantumCircuit
from ..basis import TomographyBasis
from .base_fitter import TomographyFitter
[documentos]class StateTomographyFitter(TomographyFitter):
"""Maximum-Likelihood estimation state tomography fitter."""
[documentos] def __init__(self,
result: Result,
circuits: List[QuantumCircuit],
meas_basis: Union[TomographyBasis, str] = 'Pauli'
):
"""Initialize state tomography fitter with experimental data.
Args:
result: a Qiskit Result object obtained from executing
tomography circuits.
circuits: a list of circuits or circuit names to extract
count information from the result object.
meas_basis: (default: 'Pauli') A function to return measurement
operators corresponding to measurement outcomes. See
Additional Information (default: 'Pauli')
"""
super().__init__(result, circuits, meas_basis, None)
[documentos] def fit(self, # pylint: disable=arguments-differ
method: str = 'auto',
standard_weights: bool = True,
beta: float = 0.5,
**kwargs) -> np.array:
r"""Reconstruct a quantum state using CVXPY convex optimization.
**Fitter method**
The ``cvx`` fitter method used CVXPY convex optimization package.
The ``lstsq`` method uses least-squares fitting (linear inversion).
The ``auto`` method will use 'cvx' if the CVXPY package is found on
the system, otherwise it will default to 'lstsq'.
**Objective function**
This fitter solves the constrained least-squares minimization:
:math:`minimize: ||a \cdot x - b ||_2`
subject to:
* :math:`x >> 0`
* :math:`\text{trace}(x) = 1`
where:
* a is the matrix of measurement operators
:math:`a[i] = \text{vec}(M_i).H`
* b is the vector of expectation value data for each projector
:math:`b[i] \sim \text{Tr}[M_i.H \cdot x] = (a \cdot x)[i]`
* x is the vectorized density matrix to be fitted
**PSD constraint**
The PSD keyword constrains the fitted matrix to be
postive-semidefinite. For the ``lstsq`` fitter method the fitted matrix
is rescaled using the method proposed in Reference [1]. For the ``cvx``
fitter method the convex constraint makes the optimization problem a
SDP. If PSD=False the fitted matrix will still be constrained to be
Hermitian, but not PSD. In this case the optimization problem becomes
a SOCP.
**Trace constraint**
The trace keyword constrains the trace of the fitted matrix. If
trace=None there will be no trace constraint on the fitted matrix.
This constraint should not be used for process tomography and the
trace preserving constraint should be used instead.
**CVXPY Solvers:**
Various solvers can be called in CVXPY using the `solver` keyword
argument. See the `CVXPY documentation
<https://www.cvxpy.org/tutorial/advanced/index.html#solve-method-options>`_
for more information on solvers.
References:
[1] J Smolin, JM Gambetta, G Smith, Phys. Rev. Lett. 108, 070502
(2012). Open access: arXiv:1106.5458 [quant-ph].
Args:
method: The fitter method 'auto', 'cvx' or 'lstsq'.
standard_weights: (default: True) Apply weights to
tomography data based on count probability
beta: (default: 0.5) hedging parameter for converting counts
to probabilities
**kwargs: kwargs for fitter method.
Raises:
QiskitError: In case the fitting method is unrecognized.
Returns:
The fitted matrix rho that minimizes
:math:`||\text{basis_matrix} \cdot
\text{vec}(\text{rho}) - \text{data}||_2`.
"""
return super().fit(method, standard_weights, beta,
trace=1, psd=True, **kwargs)
```