New Crowdin updates (#1194)

* New translations 01_neural_networks.po (Spanish (United)) * New translations 03_quantum_kernel.po (Spanish (United)) * New translations 01_migration_guide_0.5.po (Spanish (United)) * New translations 03_quantum_kernel.po (Spanish (United)) * New translations 02_neural_network_classifier_and_regressor.po (Spanish (United)) * New translations 05_torch_connector.po (Spanish (United)) * New translations 08_quantum_kernel_trainer.po (Spanish (United)) * New translations 11_quantum_convolutional_neural_networks.po (Spanish (United)) * New translations 04_torch_qgan.po (Spanish (United)) * New translations 09_saving_and_loading_models.po (Spanish (United)) * New translations 12_quantum_autoencoder.po (Spanish (United)) * New translations getting_started.po (Spanish (United)) * New translations index.po (Spanish (United)) * New translations getting_started.po (Spanish (United)) * New translations 01_portfolio_optimization.po (Spanish (United)) * New translations 02_portfolio_diversification.po (Spanish (United)) * New translations 03_european_call_option_pricing.po (Spanish (United)) * New translations 00_amplitude_estimation.po (Spanish (United)) * New translations 10_effective_dimension.po (Spanish (United)) * New translations adapt_vqe.po (Spanish (United)) * New translations index.po (Spanish (United)) * New translations numpy_eigensolver.po (Spanish (United)) * New translations numpy_minimum_eigensolver.po (Spanish (United)) * New translations vqe_ucc.po (Spanish (United)) * New translations vqe_uvcc.po (Spanish (United)) * New translations 0.5_b_solving_problems.po (Spanish (United)) * New translations getting_started.po (Spanish (United)) * New translations index.po (Spanish (United)) * New translations 03_ground_state_solvers.po (Spanish (United)) * New translations index.po (Spanish (United)) * New translations 11_using_classical_optimization_solvers_and_models.po (Spanish (United)) * New translations index.po (Spanish (United)) * New translations qrao.po (Spanish (United)) * New translations qrao.po (Spanish (United)) * New translations getting_started.po (Spanish (United)) * New translations index.po (Spanish (United)) * New translations 01_quadratic_program.po (Spanish (United)) * New translations 02_converters_for_quadratic_programs.po (Spanish (United)) * New translations 03_minimum_eigen_optimizer.po (Spanish (United)) * New translations 04_grover_optimizer.po (Spanish (United)) * New translations 05_admm_optimizer.po (Spanish (United)) * New translations 06_examples_max_cut_and_tsp.po (Spanish (United)) * New translations 07_examples_vehicle_routing.po (Spanish (United)) * New translations 08_cvar_optimization.po (Spanish (United)) * New translations 10_warm_start_qaoa.po (Spanish (United)) * New translations index.po (Spanish (United)) * New translations 02_migration_guide_to_v0.6.po (Spanish (United)) * New translations 12_quantum_random_access_optimizer.po (Spanish (United)) * New translations 12_quantum_random_access_optimizer.po (Spanish (United)) * New translations 12_quantum_random_access_optimizer.po (Spanish (United)) * New translations qrao.po (Spanish (United))
qiskit-community · Jan 19, 2024 · 13ce13c · 13ce13c
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diff --git a/finance/docs/locale/es_UN/LC_MESSAGES/getting_started.po b/finance/docs/locale/es_UN/LC_MESSAGES/getting_started.po
@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: qiskit-docs\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2023-11-14 09:35+0000\n"
-"PO-Revision-Date: 2023-11-14 11:56\n"
+"PO-Revision-Date: 2024-01-14 18:33\n"
 "Last-Translator: \n"
 "Language: es_UN\n"
 "Language-Team: Spanish (United)\n"
@@ -28,23 +28,23 @@ msgstr "Instalación"
 
 #: ../../getting_started.rst:10
 msgid "Qiskit Finance depends on Qiskit, which has its own `Qiskit Getting Started <https://qiskit.org/documentation/getting_started.html>`__ detailing the installation options and its supported environments/platforms. You should refer to that first. Then the information here can be followed which focuses on the additional installation specific to Qiskit Finance."
-msgstr ""
+msgstr "Qiskit Finance depende de Qiskit, que tiene su propio `Qiskit Primeros Pasos <https://qiskit.org/documentation/getting_started.html>`__ que detalla las opciones de instalación y sus entornos/plataformas compatibles. Deberías referirte a eso primero. Luego, se puede seguir la información que está aquí, la cual se centra en la instalación adicional específica de Qiskit Finance."
 
 #: ../../getting_started.rst
 msgid "Start locally"
 msgstr "Comenzar localmente"
 
 #: ../../getting_started.rst:20
 msgid "The simplest way to get started is to first follow the `getting started 'Start locally' guide for Qiskit <https://qiskit.org/documentation/getting_started.html>`__"
-msgstr ""
+msgstr "La forma más sencilla de comenzar es seguir primero la `guía de primeros pasos 'Comenzar localmente' para Qiskit <https://qiskit.org/documentation/getting_started.html>`__"
 
 #: ../../getting_started.rst:23
 msgid "In your virtual environment, where you installed Qiskit, install Qiskit Finance as follows:"
-msgstr ""
+msgstr "En tu entorno virtual, donde instalaste Qiskit, instala Qiskit Finance de la siguiente manera:"
 
 #: ../../getting_started.rst:31
 msgid "As Qiskit Finance depends on Qiskit, you can though simply install it into your environment, as above, and pip will automatically install a compatible version of Qiskit if one is not already installed."
-msgstr ""
+msgstr "Como Qiskit Finance depende de Qiskit, simplemente puedes instalarlo en tu entorno, como se indicó anteriormente, y pip instalará automáticamente una versión compatible de Qiskit si aún no hay una instalada."
 
 #: ../../getting_started.rst
 msgid "Install from source"

diff --git a/finance/docs/locale/es_UN/LC_MESSAGES/tutorials/00_amplitude_estimation.po b/finance/docs/locale/es_UN/LC_MESSAGES/tutorials/00_amplitude_estimation.po
@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: qiskit-docs\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2023-11-14 09:35+0000\n"
-"PO-Revision-Date: 2023-11-14 11:57\n"
+"PO-Revision-Date: 2024-01-14 18:33\n"
 "Last-Translator: \n"
 "Language: es_UN\n"
 "Language-Team: Spanish (United)\n"
@@ -52,7 +52,7 @@ msgstr "\\mathcal{Q} = \\mathcal{A}\\mathcal{S}_0\\mathcal{A}^\\dagger\\mathcal{
 
 #: ../../tutorials/00_amplitude_estimation.ipynb:32
 msgid "where :math:`\\mathcal{S}_0` and :math:`\\mathcal{S}_{\\Psi_1}` are reflections about the :math:`|0\\rangle` and :math:`|\\Psi_1\\rangle` states, respectively, and phase estimation. However this algorithm, called `AmplitudeEstimation <https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.AmplitudeEstimation.html>`__ in `Qiskit Algorithms <https://qiskit.org/ecosystem/algorithms/>`__, requires large circuits and is computationally expensive. Therefore, other variants of QAE have been proposed, which we will showcase in this tutorial for a simple example."
-msgstr ""
+msgstr "donde :math:`\\mathcal{S}_0` y :math:`\\mathcal{S}_{\\Psi_1}` son reflexiones sobre los estados :math:`|0\\rangle` y :math:`|\\Psi_1\\rangle`, respectivamente, y la estimación de fase. Sin embargo, este algoritmo, llamado `AmplitudeEstimation <https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.AmplitudeEstimation.html>`__ en `Qiskit Algorithms <https://qiskit.org/ecosystem/algorithms/>`__, requiere circuitos grandes y es computacionalmente costoso. Por lo tanto, se han propuesto otras variantes de QAE, que mostraremos en este tutorial para un ejemplo simple."
 
 #: ../../tutorials/00_amplitude_estimation.ipynb:35
 msgid "In our example, :math:`\\mathcal{A}` describes a Bernoulli random variable with (assumed to be unknown) success probability :math:`p`:"
@@ -92,11 +92,11 @@ msgstr "Ahora podemos definir circuitos para :math:`\\mathcal{A}` y :math:`\\mat
 
 #: ../../tutorials/00_amplitude_estimation.ipynb:148
 msgid "Amplitude Estimation workflow"
-msgstr ""
+msgstr "Flujo de Trabajo de Estimación de Amplitud"
 
 #: ../../tutorials/00_amplitude_estimation.ipynb:159
 msgid "Qiskit Algorithms implements several QAE algorithms that all derive from the `AmplitudeEstimator <https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.AmplitudeEstimator.html>`__ interface. In the initializer we specify algorithm specific settings and the ``estimate`` method, which does all the work, takes an `EstimationProblem <https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.EstimationProblem.html>`__ as input and returns an `AmplitudeEstimationResult <https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.AmplitudeEstimatorResult.html>`__ object. Since all QAE variants follow the same interface, we can use them all to solve the same problem instance."
-msgstr ""
+msgstr "Qiskit Algorithms implementa varios algoritmos QAE que se derivan de la interfaz `AmplitudeEstimator <https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.AmplitudeEstimator.html>`__. En el inicializador especificamos la configuración específica del algoritmo y el método de ``estimate`` que hace todo el trabajo, toma un `EstimationProblem <https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.EstimationProblem.html>`__ como entrada y devuelve un objeto `AmplitudeEstimationResult <https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.AmplitudeEstimatorResult.html>`__. Dado que todas las variantes de QAE siguen la misma interfaz, podemos usarlas todas para resolver la misma instancia de problema."
 
 #: ../../tutorials/00_amplitude_estimation.ipynb:162
 msgid "Next, we'll run all different QAE algorithms. To do so, we first define the estimation problem which will contain the :math:`\\mathcal{A}` and :math:`\\mathcal{Q}` operators as well as how to identify the :math:`|\\Psi_1\\rangle` state, which in this simple example is just :math:`|1\\rangle`."

diff --git a/finance/docs/locale/es_UN/LC_MESSAGES/tutorials/01_portfolio_optimization.po b/finance/docs/locale/es_UN/LC_MESSAGES/tutorials/01_portfolio_optimization.po
@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: qiskit-docs\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2023-11-14 09:35+0000\n"
-"PO-Revision-Date: 2023-11-14 11:57\n"
+"PO-Revision-Date: 2024-01-14 18:33\n"
 "Last-Translator: \n"
 "Language: es_UN\n"
 "Language-Team: Spanish (United)\n"
@@ -74,7 +74,7 @@ msgstr "Suponemos las siguientes simplificaciones: - todos los activos tienen el
 
 #: ../../tutorials/01_portfolio_optimization.ipynb:42
 msgid "The equality constraint :math:`1^T x = B` is mapped to a penalty term :math:`(1^T x - B)^2` which is scaled by a parameter and subtracted from the objective function. The resulting problem can be mapped to a Hamiltonian whose ground state corresponds to the optimal solution. This notebook shows how to use the Sampling Variational Quantum Eigensolver (``SamplingVQE``) or the Quantum Approximate Optimization Algorithm (``QAOA``) from `Qiskit Algorithms <https://qiskit.org/ecosystem/algorithms/apidocs/qiskit_algorithms.html#minimum-eigensolvers>`__ to find the optimal solution for a given set of parameters."
-msgstr ""
+msgstr "La restricción de igualdad :math:`1^T x = B` se mapea a un término de penalización :math:`(1^T x - B)^2` que se escala mediante un parámetro y se resta de la función objetivo. El problema resultante se puede mapear a un Hamiltoniano cuyo estado fundamental corresponde a la solución óptima. Este cuaderno muestra cómo usar el Solucionador Propio Variacional Cuántico de Muestreo (Sampling Variational Quantum Eigensolver, ``SamplingVQE``) o el Algoritmo Cuántico de Optimización Aproximada (Quantum Approximate Optimization Algorithm, ``QAOA``) de `Qiskit Algorithms <https://qiskit.org/ecosystem/algorithms/apidocs/qiskit_algorithms.html#minimum-eigensolvers>`__ para encontrar la solución óptima para un conjunto de parámetros dado."
 
 #: ../../tutorials/01_portfolio_optimization.ipynb:45
 msgid "Experiments on real quantum hardware for this problem are reported for instance in the following paper: `Improving Variational Quantum Optimization using CVaR. Barkoutsos et al. 2019. <https://arxiv.org/abs/1907.04769>`__"

diff --git a/finance/docs/locale/es_UN/LC_MESSAGES/tutorials/02_portfolio_diversification.po b/finance/docs/locale/es_UN/LC_MESSAGES/tutorials/02_portfolio_diversification.po
@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: qiskit-docs\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2023-11-14 09:35+0000\n"
-"PO-Revision-Date: 2023-11-14 11:57\n"
+"PO-Revision-Date: 2024-01-14 18:33\n"
 "Last-Translator: \n"
 "Language: es_UN\n"
 "Language-Team: Spanish (United)\n"
@@ -272,7 +272,7 @@ msgstr "[4] DJ. Berndt and J. Clifford, *Using dynamic time warping to find patt
 
 #: ../../tutorials/02_portfolio_diversification.ipynb:190
 msgid "[5] `Max-Cut and Traveling Salesman Problem <https://qiskit.org/ecosystem/optimization/tutorials/06_examples_max_cut_and_tsp.html>`__"
-msgstr ""
+msgstr "[5] `Max-Cut y Problema del Vendedor Viajero <https://qiskit.org/ecosystem/optimization/tutorials/06_examples_max_cut_and_tsp.html>`__"
 
 #: ../../tutorials/02_portfolio_diversification.ipynb:202
 msgid "The Implementation"
@@ -304,7 +304,7 @@ msgstr "La solución muestra las acciones seleccionadas a través de las estrell
 
 #: ../../tutorials/02_portfolio_diversification.ipynb:496
 msgid "Quantum Computing solution using Qiskit"
-msgstr ""
+msgstr "Solución con Computación Cuántica usando Qiskit"
 
 #: ../../tutorials/02_portfolio_diversification.ipynb:498
 msgid "For the quantum solution, we use Qiskit. We first define a class QuantumOptimizer that encodes the quantum approach to solve the problem and then we instantiate it and solve it. We define the following methods inside the class:"
@@ -316,7 +316,7 @@ msgstr "``exact_solution`` : para asegurarnos de que el Hamiltoniano de Ising es
 
 #: ../../tutorials/02_portfolio_diversification.ipynb:501
 msgid "``sampling_vqe_solution`` : solves the problem :math:`(M)` via the Sampling Variational Quantum Eigensolver (``SamplingVQE``);"
-msgstr ""
+msgstr "``sampling_vqe_solution`` : resuelve el problema :math:`(M)` a través del Solucionador Propio Variacional Cuántico de Muestreo (Sampling Variational Quantum Eigensolver, ``SamplingVQE``);"
 
 #: ../../tutorials/02_portfolio_diversification.ipynb:502
 msgid "``qaoa_solution`` : solves the problem :math:`(M)` via a Quantum Approximate Optimization Algorithm (``QAOA``)."

diff --git a/finance/docs/locale/es_UN/LC_MESSAGES/tutorials/03_european_call_option_pricing.po b/finance/docs/locale/es_UN/LC_MESSAGES/tutorials/03_european_call_option_pricing.po
@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: qiskit-docs\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2023-11-14 09:35+0000\n"
-"PO-Revision-Date: 2023-11-14 11:57\n"
+"PO-Revision-Date: 2024-01-14 18:33\n"
 "Last-Translator: \n"
 "Language: es_UN\n"
 "Language-Team: Spanish (United)\n"
@@ -132,7 +132,7 @@ msgstr "Evaluar el Rendimiento Esperado"
 
 #: ../../tutorials/03_european_call_option_pricing.ipynb:576
 msgid "Instead of constructing these circuits manually, the Qiskit Finance module offers the ``EuropeanCallPricing`` circuit, which already implements this functionality as a building block."
-msgstr ""
+msgstr "En lugar de construir estos circuitos manualmente, el módulo Qiskit Finance ofrece el circuito ``EuropeanCallPricing``, que ya implementa esta funcionalidad como un bloque de construcción."
 
 #: ../../tutorials/03_european_call_option_pricing.ipynb:660
 msgid "Evaluate Delta"

diff --git a/machine-learning/docs/locale/es_UN/LC_MESSAGES/getting_started.po b/machine-learning/docs/locale/es_UN/LC_MESSAGES/getting_started.po
@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: qiskit-docs\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2023-12-01 12:18+0000\n"
-"PO-Revision-Date: 2023-12-01 13:26\n"
+"PO-Revision-Date: 2024-01-14 18:32\n"
 "Last-Translator: \n"
 "Language: es_UN\n"
 "Language-Team: Spanish (United)\n"
@@ -28,7 +28,7 @@ msgstr "Instalación"
 
 #: ../../getting_started.rst:10
 msgid "Qiskit Machine Learning depends on Qiskit, which has its own `Qiskit Getting Started <https://qiskit.org/documentation/getting_started.html>`__ detailing installation options and its supported environments/platforms. You should refer to that first. Then the information here can be followed which focuses on the additional installation specific to Qiskit Machine Learning."
-msgstr ""
+msgstr "Qiskit Machine Learning depende de Qiskit, que tiene su propio `Qiskit Primeros Pasos <https://qiskit.org/documentation/getting_started.html>`__ que detalla opciones de instalación y sus entornos/plataformas compatibles. Deberías referirte a eso primero. Luego, se puede seguir la información que está aquí, la cual se centra en la instalación adicional específica de Qiskit Machine Learning."
 
 #: ../../getting_started.rst:16
 msgid "Qiskit Machine Learning has some functions that have been made optional where the dependent code and/or support program(s) are not (or cannot be) installed by default. Those are PyTorch and Sparse. See :ref:`optional_installs` for more information."
@@ -40,15 +40,15 @@ msgstr "Comenzar localmente"
 
 #: ../../getting_started.rst:24
 msgid "The simplest way to get started is to first follow the `getting started 'Start locally' guide for Qiskit <https://qiskit.org/documentation/getting_started.html>`__"
-msgstr ""
+msgstr "La forma más sencilla de comenzar es seguir primero la `guía de primeros pasos 'Comenzar localmente' para Qiskit <https://qiskit.org/documentation/getting_started.html>`__"
 
 #: ../../getting_started.rst:27
 msgid "In your virtual environment, where you installed Qiskit, install Qiskit Machine Learning as follows:"
-msgstr ""
+msgstr "En tu entorno virtual, donde instalaste Qiskit, instala Qiskit Machine Learning de la siguiente manera:"
 
 #: ../../getting_started.rst:35
 msgid "As Qiskit Machine Learning depends on Qiskit, you can though simply install it into your environment, as above, and pip will automatically install a compatible version of Qiskit if one is not already installed."
-msgstr ""
+msgstr "Como Qiskit Machine Learning depende de Qiskit, simplemente puedes instalarlo en tu entorno, como se indicó anteriormente, y pip instalará automáticamente una versión compatible de Qiskit si aún no hay una instalada."
 
 #: ../../getting_started.rst
 msgid "Install from source"

diff --git a/machine-learning/docs/locale/es_UN/LC_MESSAGES/index.po b/machine-learning/docs/locale/es_UN/LC_MESSAGES/index.po
@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: qiskit-docs\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2023-12-01 12:18+0000\n"
-"PO-Revision-Date: 2023-12-01 13:26\n"
+"PO-Revision-Date: 2024-01-14 18:32\n"
 "Last-Translator: \n"
 "Language: es_UN\n"
 "Language-Team: Spanish (United)\n"
@@ -56,7 +56,7 @@ msgstr "Qiskit Machine Learning presenta bloques de construcción computacionale
 
 #: ../../index.rst:14
 msgid "Qiskit Machine Learning provides the :class:`~qiskit_machine_learning.kernels.FidelityQuantumKernel` class class that makes use of the :class:`~qiskit_algorithms.state_fidelities.BaseStateFidelity` algorithm introduced in Qiskit and can be easily used to directly compute kernel matrices for given datasets or can be passed to a Quantum Support Vector Classifier (:class:`~qiskit_machine_learning.algorithms.QSVC`) or Quantum Support Vector Regressor (:class:`~qiskit_machine_learning.algorithms.QSVR`) to quickly start solving classification or regression problems. It also can be used with many other existing kernel-based machine learning algorithms from established classical frameworks."
-msgstr ""
+msgstr "Qiskit Machine Learning proporciona la clase :class:`~qiskit_machine_learning.kernels.FidelityQuantumKernel` que hace uso del algoritmo :class:`~qiskit_algorithms.state_fidelities.BaseStateFidelity` introducido en Qiskit y se puede usar fácilmente para calcular directamente las matrices del kernel para conjuntos de datos dados o se puede pasar a un Clasificador Cuántico de Vectores de Soporte (Quantum Support Vector Classifier) (:class:`~qiskit_machine_learning.algorithms.QSVC`) o Regresor Cuántico de Vectores de Soporte (Quantum Support Vector Regressor) (:class:`~qiskit_machine_learning.algorithms.QSVR`) para empezar a resolver rápidamente problemas de clasificación o regresión. También se puede utilizar con muchos otros algoritmos de machine learning basados en kernels existentes en frameworks clásicos establecidos."
 
 #: ../../index.rst:24
 msgid "Qiskit Machine Learning defines a generic interface for neural networks that is implemented by different quantum neural networks. Two core implementations are readily provided, such as the :class:`~qiskit_machine_learning.neural_networks.EstimatorQNN` and the :class:`~qiskit_machine_learning.neural_networks.SamplerQNN`. The :class:`~qiskit_machine_learning.neural_networks.EstimatorQNN` leverages the :class:`~qiskit.primitives.BaseEstimator` primitive from Qiskit and allows users to combine parametrized quantum circuits with quantum mechanical observables. The circuits can be constructed using, for example, building blocks from Qiskit's circuit library, and the QNN's output is given by the expected value of the observable. The :class:`~qiskit_machine_learning.neural_networks.SamplerQNN` leverages another primitive introduced in Qiskit, the :class:`~qiskit.primitives.BaseSampler` primitive. This neural network translates quasi-probabilities of bitstrings estimated by the primitive into a desired output. This translation step can be used to interpret a given bitstring in a particular context, e.g. translating it into a set of classes."
@@ -68,7 +68,7 @@ msgstr "Las redes neuronales incluyen la funcionalidad para evaluarlas para una
 
 #: ../../index.rst:50
 msgid "In addition to the models provided directly in Qiskit Machine Learning, it has the :class:`~qiskit_machine_learning.connectors.TorchConnector`, which allows users to integrate all of our quantum neural networks directly into the `PyTorch <https://pytorch.org>`__ open source machine learning library. Thanks to Qiskit Algorithm's gradient algorithms, this includes automatic differentiation - the overall gradients computed by `PyTorch <https://pytorch.org>`__ during the backpropagation take into account quantum neural networks, too. The flexible design also allows the building of connectors to other packages in the future."
-msgstr ""
+msgstr "Además de los modelos provistos directamente en Qiskit Machine Learning, se tiene la clase :class:`~qiskit_machine_learning.connectors.TorchConnector`, que permite a los usuarios integrar todas nuestras redes neuronales cuánticas directamente en la biblioteca de machine learning de código abierto `PyTorch <https://pytorch.org>`__. Gracias los algoritmos de gradiente de Qiskit Algorithm, esto incluye la diferenciación automática, los gradientes generales calculados por `PyTorch <https://pytorch.org>`__ durante la retropropagación también toman en cuenta las redes neuronales cuánticas. El diseño flexible también permite la construcción de conectores a otros paquetes en el futuro."
 
 #: ../../index.rst:64
 msgid "Next Steps"