qiskit-community · SooluThomas · Jan 8, 2024 · Dec 1, 2023 · Dec 1, 2023 · Dec 1, 2023
diff --git a/finance/docs/locale/ja_JP/LC_MESSAGES/getting_started.po b/finance/docs/locale/ja_JP/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 11:26\n"
+"PO-Revision-Date: 2024-01-02 12:08\n"
 "Last-Translator: \n"
 "Language: ja\n"
 "Language-Team: Japanese\n"
@@ -28,23 +28,23 @@ msgstr "インストール"
 
 #: ../../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は、インストール・オプションとそのサポート環境/プラットフォームを詳述した「`Qiskit はじめに <https://qiskit.org/documentation/getting_started.html>`__ 」にて説明されたQiskitに依存しています。 まずそれを参照してください。次に、ここで説明する情報に従って、Qiskit Financeに固有の追加インストールをしましょう。"
 
 #: ../../getting_started.rst
 msgid "Start locally"
 msgstr "ローカルからの開始"
 
 #: ../../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 "最も簡単な方法は「 `Qiskit はじめに <https://qiskit.org/documentation/getting_started.html>`__ 」の「ローカルで始める」のガイドを参照して開始することです。"
 
 #: ../../getting_started.rst:23
 msgid "In your virtual environment, where you installed Qiskit, install Qiskit Finance as follows:"
-msgstr ""
+msgstr "Qiskit をインストールした仮想環境で、次のように Qiskit Finance をインストールします。"
 
 #: ../../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 "Qiskit FinanceはQiskitに依存しているため、上記のようにQiskitを環境にインストールするだけで、Qiskitの互換バージョンがまだインストールされていない場合、pipがQiskitを自動的にインストールします。"
 
 #: ../../getting_started.rst
 msgid "Install from source"

diff --git a/finance/docs/locale/ja_JP/LC_MESSAGES/tutorials/00_amplitude_estimation.po b/finance/docs/locale/ja_JP/LC_MESSAGES/tutorials/00_amplitude_estimation.po
@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: qiskit-docs\n"
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+"PO-Revision-Date: 2024-01-02 12:08\n"
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@@ -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 "ここで :math:`\\mathcal{S}_0` と :math:`\\mathcal{S}_{\\Psi_1}` はそれぞれ :math:`|0\\rangle` と :math:`|\\Psi_1\\rangle` の状態に対する反転です。そして位相推定があります。しかしながら `Qiskit Algorithms <https://qiskit.org/ecosystem/algorithms/>`__ で `AmplitudeEstimation <https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.AmplitudeEstimation.html>`__ と呼ばれるこのアルゴリズムは、大きな回路を必要とし、計算コストが高いです。そのため、QAE の改良が提案されており、このチュートリアルでは簡単な例を紹介します。"
 
 #: ../../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 "これで :math:`\\mathcal{A}` と :math:`\\mathcal{Q}` を定義でき
 
 #: ../../tutorials/00_amplitude_estimation.ipynb:148
 msgid "Amplitude Estimation workflow"
-msgstr ""
+msgstr "振幅推定ワークフロー"
 
 #: ../../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 Algorithm はいくつかの QAE のアルゴリズムを全て `AmplitudeEstimator <https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.AmplitudeEstimator.html>`__ インタフェースを派生して実装します。初期化ではアルゴリズム固有の設定と ``estimate`` メソッドを指定します。このメソッドが全ての処理を行い、 `EstimationProblem <https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.EstimationProblem.html>`__ を入力にとり、 `AmplitudeEstimationResult <https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.AmplitudeEstimatorResult.html>`__ オブジェクトを返します。全ての QAE の実装は同じインタフェースに従うため、同じ問題インスタンスを解くために使えます。"
 
 #: ../../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/ja_JP/LC_MESSAGES/tutorials/01_portfolio_optimization.po b/finance/docs/locale/ja_JP/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 11:27\n"
+"PO-Revision-Date: 2023-12-19 13:50\n"
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 "Language: ja\n"
 "Language-Team: Japanese\n"
@@ -74,7 +74,7 @@ msgstr "ここで、次の簡略化を想定します。すべての資産は同
 
 #: ../../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 "等価制約 :math:`1^T x = B` は、パラメーターによってスケールされ、目的関数から減算される、ペナルティー項 :math:`(1^T x - B)^2` にマップされます。得られた問題は、基底状態が最適解に対応するハミルトニアンにマッピングすることができます。 このノートブックでは、与えられたパラメーターセットによる最適解を見つけるために、`Qiskit Algorithms <https://qiskit.org/ecosystem/algorithms/apidocs/qiskit_algorithms.html#minimum-eigensolvers>`__ の Sampling Variational Quantum Eigensolver (``SamplingVQE``) または Quantum Approximate Optimization Algorithm (``QAOA``) を使用する方法について説明します。"
 
 #: ../../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/ja_JP/LC_MESSAGES/tutorials/02_portfolio_diversification.po b/finance/docs/locale/ja_JP/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 11:27\n"
+"PO-Revision-Date: 2023-12-29 15:14\n"
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@@ -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 and Traveling Salesman Problem <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 "ソリューションは、星印で選択された銘柄が示され、
 
 #: ../../tutorials/02_portfolio_diversification.ipynb:496
 msgid "Quantum Computing solution using Qiskit"
-msgstr ""
+msgstr "Qiskit を使用した量子コンピューティング・ソリューション"
 
 #: ../../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`` : イジング・ハミルトニアンが :math:`Z`
 
 #: ../../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`` : サンプリング変数量子固有ソルバー (``SamplingVQE``) を介して問題 :math:`(M)` を解きます。"
 
 #: ../../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/ja_JP/LC_MESSAGES/tutorials/03_european_call_option_pricing.po b/finance/docs/locale/ja_JP/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 11:27\n"
+"PO-Revision-Date: 2023-12-19 02:00\n"
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 "Language-Team: Japanese\n"
@@ -132,7 +132,7 @@ msgstr "期待ペイオフの評価"
 
 #: ../../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 "これらの回路を手動で構築する代わりに、Qiskit Finance モジュールは ``EuropeanCallPricing`` 回路を提供しています。この回路は、この機能をビルディングブロックとしてすでに実装しています。"
 
 #: ../../tutorials/03_european_call_option_pricing.ipynb:660
 msgid "Evaluate Delta"

diff --git a/finance/docs/locale/te_IN/LC_MESSAGES/tutorials/02_portfolio_diversification.po b/finance/docs/locale/te_IN/LC_MESSAGES/tutorials/02_portfolio_diversification.po
@@ -3,7 +3,7 @@ msgstr ""
 "Project-Id-Version: qiskit-docs\n"
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-"PO-Revision-Date: 2023-11-14 11:52\n"
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@@ -272,7 +272,7 @@ msgstr "[4] DJ. Berndt and J. Clifford, *సమయ శ్రేణిలో న
 
 #: ../../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 and Traveling Salesman Problem <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 "పరిష్కారం ఎంచుకున్న స్టాక
 
 #: ../../tutorials/02_portfolio_diversification.ipynb:496
 msgid "Quantum Computing solution using Qiskit"
-msgstr ""
+msgstr "Qiskit ఉపయోగించి క్వాంటం కంప్యూటింగ్ పరిష్కారం"
 
 #: ../../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`` : `Z` బాసిస్ లో ఐసింగ్
 
 #: ../../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`` : నమూనా వేరియేషనల్ క్వాంటం ఈజెన్‌సోల్వర్ (``SamplingVQE``) ద్వారా :math:`(M)` సమస్యను పరిష్కరిస్తుంది;"
 
 #: ../../tutorials/02_portfolio_diversification.ipynb:502
 msgid "``qaoa_solution`` : solves the problem :math:`(M)` via a Quantum Approximate Optimization Algorithm (``QAOA``)."

diff --git a/machine-learning/docs/locale/bn_BN/LC_MESSAGES/getting_started.po b/machine-learning/docs/locale/bn_BN/LC_MESSAGES/getting_started.po
@@ -2,8 +2,8 @@ msgid ""
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diff --git a/machine-learning/docs/locale/bn_BN/LC_MESSAGES/index.po b/machine-learning/docs/locale/bn_BN/LC_MESSAGES/index.po
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diff --git a/machine-learning/docs/locale/bn_BN/LC_MESSAGES/migration/01_migration_guide_0.5.po b/machine-learning/docs/locale/bn_BN/LC_MESSAGES/migration/01_migration_guide_0.5.po
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diff --git a/machine-learning/docs/locale/bn_BN/LC_MESSAGES/migration/index.po b/machine-learning/docs/locale/bn_BN/LC_MESSAGES/migration/index.po
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diff --git a/machine-learning/docs/locale/bn_BN/LC_MESSAGES/tutorials/01_neural_networks.po b/machine-learning/docs/locale/bn_BN/LC_MESSAGES/tutorials/01_neural_networks.po
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diff --git a/...ing/docs/locale/bn_BN/LC_MESSAGES/tutorials/02_neural_network_classifier_and_regressor.po b/...ing/docs/locale/bn_BN/LC_MESSAGES/tutorials/02_neural_network_classifier_and_regressor.po
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diff --git a/...docs/locale/bn_BN/LC_MESSAGES/tutorials/02a_training_a_quantum_model_on_a_real_dataset.po b/...docs/locale/bn_BN/LC_MESSAGES/tutorials/02a_training_a_quantum_model_on_a_real_dataset.po
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diff --git a/machine-learning/docs/locale/bn_BN/LC_MESSAGES/tutorials/03_quantum_kernel.po b/machine-learning/docs/locale/bn_BN/LC_MESSAGES/tutorials/03_quantum_kernel.po
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diff --git a/machine-learning/docs/locale/bn_BN/LC_MESSAGES/tutorials/04_torch_qgan.po b/machine-learning/docs/locale/bn_BN/LC_MESSAGES/tutorials/04_torch_qgan.po
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diff --git a/machine-learning/docs/locale/bn_BN/LC_MESSAGES/tutorials/05_torch_connector.po b/machine-learning/docs/locale/bn_BN/LC_MESSAGES/tutorials/05_torch_connector.po
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diff --git a/machine-learning/docs/locale/bn_BN/LC_MESSAGES/tutorials/07_pegasos_qsvc.po b/machine-learning/docs/locale/bn_BN/LC_MESSAGES/tutorials/07_pegasos_qsvc.po
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