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quantum-ods · Oct 10, 2023 · 3d68857 · 3d68857
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diff --git a/_images/copt_1_0.png b/_images/copt_1_0.png
diff --git a/_images/dwave_19_0.png b/_images/dwave_19_0.png
diff --git a/_images/eigenvals_17_0.png b/_images/eigenvals_17_0.png
diff --git a/_images/eigenvals_19_1.png b/_images/eigenvals_19_1.png
diff --git a/_images/gradients_13_0.png b/_images/gradients_13_0.png
diff --git a/_images/gradients_21_0.png b/_images/gradients_21_0.png
diff --git a/_images/gradients_23_0.png b/_images/gradients_23_0.png
diff --git a/_images/gradients_3_0.png b/_images/gradients_3_0.png
diff --git a/_images/hybridsvm_24_0.png b/_images/hybridsvm_24_0.png
diff --git a/_images/hybridsvm_6_0.png b/_images/hybridsvm_6_0.png
diff --git a/_images/qiskit_13_1.png b/_images/qiskit_13_1.png
diff --git a/_images/vqc_10_0.png b/_images/vqc_10_0.png
diff --git a/_images/vqc_18_0.png b/_images/vqc_18_0.png
diff --git a/_images/vqe_9_1.png b/_images/vqe_9_1.png
diff --git a/_sources/book/dwave/ru/annealing_paper_1998.ipynb b/_sources/book/dwave/ru/annealing_paper_1998.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9d145276",
+   "id": "d43ff40c",
    "metadata": {},
    "source": [
     "(annealing_paper_1998)=\n",

diff --git a/_sources/book/dwave/ru/dwave.ipynb b/_sources/book/dwave/ru/dwave.ipynb
diff --git a/_sources/book/grads/ru/gradients.ipynb b/_sources/book/grads/ru/gradients.ipynb
diff --git a/_sources/book/grads/ru/hogradients.ipynb b/_sources/book/grads/ru/hogradients.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "3489324f",
+   "id": "11fbc0c8",
    "metadata": {},
    "source": [
     "(hogradients)=\n",

diff --git a/_sources/book/linalg/ru/bra-ket.ipynb b/_sources/book/linalg/ru/bra-ket.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "cb3b8922",
+   "id": "93672090",
    "metadata": {},
    "source": [
     "(bra-ket)=\n",
@@ -93,7 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "2f3a83c8",
+   "id": "99d52de1",
    "metadata": {},
    "outputs": [
     {
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab93bd2f",
+   "id": "121138e6",
    "metadata": {},
    "source": [
     "Также в `Python` для многих операций есть соответствующие методы вместо функций и их сокращения, например `U.transpose()` -- то же самое, что `U.T`, а `U.conjugate()` -- `U.conj()`.\n",
@@ -170,7 +170,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "cab87f85",
+   "id": "2c06345f",
    "metadata": {},
    "outputs": [
     {
@@ -217,7 +217,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c4c8911",
+   "id": "b3a1ad07",
    "metadata": {},
    "source": [
     "### Пример: оператор-проектор\n",

diff --git a/_sources/book/linalg/ru/numpy.ipynb b/_sources/book/linalg/ru/numpy.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "470ae272",
+   "id": "d2079367",
    "metadata": {},
    "source": [
     "(numpy)=\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4cb8aaf0",
+   "id": "b4deee5a",
    "metadata": {},
    "outputs": [
     {
@@ -35,10 +35,10 @@
       "c = array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])\n",
-      "q = array([[0.47197358, 0.05370024, 0.84668284, 0.67487523, 0.68197123,\n",
-      "        0.54118889, 0.16472495, 0.86646168, 0.57442157, 0.13377434,\n",
-      "        0.1256861 , 0.68105608, 0.84393499, 0.24997468, 0.77879102,\n",
-      "        0.59350871, 0.36360664, 0.06457532, 0.57814658, 0.61087286]])\n"
+      "q = array([[0.91865041, 0.75172279, 0.62591658, 0.77350525, 0.06107411,\n",
+      "        0.50967999, 0.84198337, 0.128431  , 0.36115937, 0.72422143,\n",
+      "        0.22623052, 0.5361952 , 0.81099392, 0.02847385, 0.02073452,\n",
+      "        0.73325807, 0.50502209, 0.61976152, 0.64946743, 0.95862167]])\n"
      ]
     }
    ],
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f045b4d",
+   "id": "bde48b71",
    "metadata": {},
    "source": [
     "## Арифметические операции\n",
@@ -70,7 +70,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "320e685f",
+   "id": "62d1b04b",
    "metadata": {},
    "outputs": [
     {
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16189de0",
+   "id": "05be9a5a",
    "metadata": {},
    "source": [
     "## Indexing, slicing and sugar\n",
@@ -110,7 +110,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "9b3773e5",
+   "id": "050ed723",
    "metadata": {},
    "outputs": [
     {
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32af2845",
+   "id": "967cbce1",
    "metadata": {},
    "source": [
     "Для работы с размерностями часто используются еще три конструкции: `None`, `...` (ellipsis, многоточие) и `:` (двоеточие)."
@@ -175,7 +175,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "00868a77",
+   "id": "d33b5034",
    "metadata": {},
    "outputs": [
     {
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f12d5ee",
+   "id": "ba8f363d",
    "metadata": {},
    "source": [
     "В целом в `NumPy` очень здорово реализованы методы `__getitem__`/`__setitem__`."
@@ -235,7 +235,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "7c3eea83",
+   "id": "73ebd476",
    "metadata": {},
    "outputs": [
     {
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d77ff27",
+   "id": "aeeb16d3",
    "metadata": {},
    "source": [
     "Кроме того, мы можем делать индексацию по заданному условию с помощью `np.where`."
@@ -267,7 +267,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "7af4625c",
+   "id": "5d87ca91",
    "metadata": {},
    "outputs": [
     {
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d2219c8",
+   "id": "59a57ac5",
    "metadata": {},
    "source": [
     "## Broadcasting\n",
@@ -311,7 +311,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "676fec73",
+   "id": "c87d6fe4",
    "metadata": {},
    "outputs": [
     {
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfb31555",
+   "id": "f541a1f9",
    "metadata": {},
    "source": [
     "С точки зрения математики, ничего интересного тут не происходит: мы подразумевали умножение всего вектора на скаляр. Однако матричные операции в numpy справляются и с менее очевидными случаями, например, при сложении или вычитании вектора и скаляра:"
@@ -340,7 +340,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "3dd20b3b",
+   "id": "0244bdd6",
    "metadata": {},
    "outputs": [
     {
@@ -360,7 +360,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b455d03d",
+   "id": "8e22f133",
    "metadata": {},
    "source": [
     "В numpy приняты следующие правила работы с массивами разного размера:\n",
@@ -386,7 +386,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "f065f6a1",
+   "id": "3bbb414e",
    "metadata": {},
    "outputs": [
     {
@@ -416,7 +416,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9f48c23",
+   "id": "3274c341",
    "metadata": {},
    "source": [
     "Из-за этого для сравнения массивов с типом float используют `np.allclose`."
@@ -425,7 +425,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "e174c186",
+   "id": "5f0bb189",
    "metadata": {},
    "outputs": [
     {
@@ -444,7 +444,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7829c12e",
+   "id": "fb1b40a9",
    "metadata": {},
    "source": [
     "## NumPy и линейная алгебра\n",
@@ -455,7 +455,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "14f3fe07",
+   "id": "3aa409ed",
    "metadata": {},
    "outputs": [
     {
@@ -467,15 +467,15 @@
       "np.eye(3, k=-1, dtype=int) = array([[0, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [0, 1, 0]])\n",
-      "np.random.beta(1, 2) = 0.40317716507162454\n",
-      "np.random.randint(1, 5, (2, 3)) = array([[3, 3, 1],\n",
-      "       [2, 4, 3]])\n",
-      "a = array([[1, 2, 2],\n",
+      "np.random.beta(1, 2) = 0.040090662051107735\n",
+      "np.random.randint(1, 5, (2, 3)) = array([[2, 1, 4],\n",
+      "       [2, 1, 3]])\n",
+      "a = array([[2, 1, 3],\n",
+      "       [4, 3, 2],\n",
+      "       [3, 2, 1]])\n",
+      "a = array([[4, 1, 3],\n",
       "       [4, 4, 2],\n",
-      "       [2, 4, 4]])\n",
-      "a = array([[4, 2, 2],\n",
-      "       [4, 4, 2],\n",
-      "       [2, 4, 4]])\n",
+      "       [3, 2, 4]])\n",
       "np.diag(a) = array([4, 4, 4])\n"
      ]
     }
@@ -503,7 +503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0bc683c",
+   "id": "639e87a6",
    "metadata": {},
    "source": [
     "### Решение систем линейных уравнений\n",
@@ -514,7 +514,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "3314d394",
+   "id": "d9dafc70",
    "metadata": {},
    "outputs": [
     {
@@ -534,7 +534,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4109fb2c",
+   "id": "c41fc327",
    "metadata": {},
    "source": [
     "### Обращение матриц\n",
@@ -545,7 +545,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "3cbc417a",
+   "id": "a8f27af0",
    "metadata": {},
    "outputs": [
     {
@@ -565,7 +565,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a56498f2",
+   "id": "f83db8ca",
    "metadata": {},
    "source": [
     "### Собственные вектора и числа\n",
@@ -576,7 +576,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "e91929ff",
+   "id": "bbb18cb7",
    "metadata": {},
    "outputs": [
     {
@@ -595,7 +595,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50188f88",
+   "id": "2d34b9df",
    "metadata": {},
    "source": [
     "## Мы вкратце рассмотрели\n",

diff --git a/_sources/book/ml/ru/ml_l1.ipynb b/_sources/book/ml/ru/ml_l1.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "96020685",
+   "id": "f4e07643",
    "metadata": {},
    "source": [
     "(mll1)=\n",

diff --git a/_sources/book/problems/ru/copt.ipynb b/_sources/book/problems/ru/copt.ipynb