From a3e40e2b14bc991b885b6ed3aed6a5b4e7fd219c Mon Sep 17 00:00:00 2001 From: robotAstray Date: Mon, 7 Aug 2023 01:31:03 +0100 Subject: [PATCH] Docs: migration to Qiskit Primitives (#115) --- ...rial_qiskit-braket-provider_overview.ipynb | 151 +++++++------- docs/tutorials/1_tutorial_vqe.ipynb | 62 +++--- .../2_tutorial_hybrid_jobs_byoc.ipynb | 98 ++++----- .../3_tutorial_minimum_eigen_optimizer.ipynb | 190 +++++++----------- .../data/2_hybrid_jobs/job_script.py | 34 ++-- tests/providers/test_adapter.py | 10 +- tests/providers/test_braket_backend.py | 35 ++-- 7 files changed, 259 insertions(+), 321 deletions(-) diff --git a/docs/tutorials/0_tutorial_qiskit-braket-provider_overview.ipynb b/docs/tutorials/0_tutorial_qiskit-braket-provider_overview.ipynb index 842372b2..558c5256 100644 --- a/docs/tutorials/0_tutorial_qiskit-braket-provider_overview.ipynb +++ b/docs/tutorials/0_tutorial_qiskit-braket-provider_overview.ipynb @@ -67,18 +67,14 @@ "metadata": {}, "outputs": [], "source": [ - "from qiskit.algorithms import VQE\n", - "from qiskit.opflow import (\n", - " I,\n", - " X,\n", - " Z,\n", - ")\n", + "from qiskit.algorithms.minimum_eigensolvers import VQE\n", + "from qiskit.quantum_info import SparsePauliOp\n", "from qiskit import transpile, QuantumCircuit\n", "from qiskit.circuit.random import random_circuit\n", "from qiskit.visualization import plot_histogram\n", "from qiskit.algorithms.optimizers import SLSQP\n", "from qiskit.circuit.library import TwoLocal\n", - "from qiskit.utils import QuantumInstance\n", + "from qiskit.primitives import BackendEstimator\n", "\n", "from braket.aws import AwsQuantumJob\n", "\n", @@ -358,7 +354,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -388,7 +384,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -420,7 +416,7 @@ { "data": { "text/plain": [ - "['11', '11', '00', '11', '11', '00', '00', '11', '11', '00']" + "['11', '11', '00', '11', '11', '11', '00', '00', '00', '00']" ] }, "execution_count": 11, @@ -455,10 +451,10 @@ "
     ┌────────────────────────┐                                       ┌─────────────────────────┐┌────┐   ┌───────────────────┐                                                     ┌──────┐                                                                                               ┌────────────────────┐┌───────────┐┌─────────────────────────┐                                   ┌──────────────┐       ┌───┐        ┌────────────┐                                   ┌────────────────────┐  ┌───┐                                                           ┌──────────────────┐                   \n",
        "q_0: ┤0                       ├────■──────────────────────────────────┤1                        ├┤ Sx ├───┤ U2(1.8116,4.2882) ├─────────────────────────────────────────────────────┤1     ├─────────────────────────────────────────X───────────────────────────────────────────────────X─┤ U2(0.56579,4.5387) ├┤ Rx(2.394) ├┤ U3(4.893,4.5044,2.8234) ├───────────────────────────────────┤0             ├───────┤ Y ├────────┤ Ry(3.7128) ├───────────────────────────■───────┤ U2(0.13579,5.1917) ├──┤ H ├──────────────────────────■────────────────────────■───────┤ R(4.1573,2.3478) ├───────────────────\n",
        "     │                        │    │ ┌───┐┌────────────┐┌────────────┐│                         │└─┬──┘   └───────────────────┘                                     ┌──────────────┐│      │                                         │                                                   │ └────────────────────┘└───────────┘└──────┬────────────┬─────┘        ┌───┐            ┌─────┐   │              │       └─┬─┘        └────────────┘             ┌──────┐      │       └────────────────────┘  ├───┴┐ ┌──────────────┐┌────┐  │        ┌───┐           │       └──────────────────┘                   \n",
-       "q_1: ┤  {XX+YY}(4.7824,4.939) ├─■──┼─┤ X ├┤ Ry(6.0991) ├┤ Rx(1.1905) ├┤                         ├──┼────────────────────────────────────────────────────────────────┤0             ├┤      ├─────────────────■───────────■───────────┼──────────────────────────────────────────────■────┼───────────■───────────────────────────────┤ Rx(1.8945) ├──────────────┤ Y ├────────────┤ Sdg ├───┤              ├─────────■─────────────────────────────────────┤1     ├──────┼─────────────■─────────────────┤ Sx ├─┤0             ├┤ Sx ├──┼────────┤ T ├───────────┼────────────────■─────────────────────────────\n",
+       "q_1: ┤  (XX+YY)(4.7824,4.939) ├─■──┼─┤ X ├┤ Ry(6.0991) ├┤ Rx(1.1905) ├┤                         ├──┼────────────────────────────────────────────────────────────────┤0             ├┤      ├─────────────────■───────────■───────────┼──────────────────────────────────────────────■────┼───────────■───────────────────────────────┤ Rx(1.8945) ├──────────────┤ Y ├────────────┤ Sdg ├───┤              ├─────────■─────────────────────────────────────┤1     ├──────┼─────────────■─────────────────┤ Sx ├─┤0             ├┤ Sx ├──┼────────┤ T ├───────────┼────────────────■─────────────────────────────\n",
        "     │                        │ │  │ └─┬─┘└──┬─────┬───┘└──┬─────┬───┘│                         │  │   ┌─────────────────────────┐                                  │              ││      │┌──────────────┐ │           │           │ ┌────────────────────────────────┐ ┌───┐     │    │     ┌─────┴──────┐                        └─────┬──────┘      ┌───────┴───┴────────┐   └─────┘   │  Rzx(5.3823) │                                               │      │      │             │P(5.8925)        └─┬──┘ │  Rxx(4.5678) │└─┬──┘  │    ┌───┴───┴────┐      │                │          ┌─────────────────┐\n",
-       "q_2: ┤1                       ├─┼──┼───┼─────┤ Sdg ├───────┤ Tdg ├────┤  {XX-YY}(5.6116,4.8907) ├──┼───┤1                        ├────────────────■─────────────────┤              ├┤  Ecr ├┤0             ├─┼───────────┼───────────┼─┤ U(3.5136,1.9098,0.19363,2.744) ├─┤ X ├─────┼────┼─────┤ Ry(1.0491) ├──────────■───────────────────┼─────────────┤ U2(1.7105,0.60564) ├─■───────────┤              ├────■──────────────────────────────■───────────┤      ├──────┼─────────────■───────────────────■────┤1             ├──┼─────┼────┤ U1(1.9926) ├──────X────────────────┼──────────┤ R(1.985,4.8507) ├\n",
-       "     └─────────┬────┬─────────┘ │  │   │     └──┬──┘       └─────┘    │                         │  │   │  {XX-YY}(2.3277,2.9503) │┌───────────────┴────────────────┐│  Rxx(2.4346) ││      ││  Rxx(2.8835) │ │           │           │ └───────────────┬────────────────┘┌┴───┴─┐   │    │     └──┬──────┬──┘          │                   │             └───────┬───┬────────┘ │           │              │    │U1(0.26147)    ┌────────────┐ │           │  Ecr │      │              ┌──────┐       ┌───────┐├──────────────┤  │     │   ┌┴────────────┤      │              ┌─┴─┐        └─────────────────┘\n",
+       "q_2: ┤1                       ├─┼──┼───┼─────┤ Sdg ├───────┤ Tdg ├────┤  (XX-YY)(5.6116,4.8907) ├──┼───┤1                        ├────────────────■─────────────────┤              ├┤  Ecr ├┤0             ├─┼───────────┼───────────┼─┤ U(3.5136,1.9098,0.19363,2.744) ├─┤ X ├─────┼────┼─────┤ Ry(1.0491) ├──────────■───────────────────┼─────────────┤ U2(1.7105,0.60564) ├─■───────────┤              ├────■──────────────────────────────■───────────┤      ├──────┼─────────────■───────────────────■────┤1             ├──┼─────┼────┤ U1(1.9926) ├──────X────────────────┼──────────┤ R(1.985,4.8507) ├\n",
+       "     └─────────┬────┬─────────┘ │  │   │     └──┬──┘       └─────┘    │                         │  │   │  (XX-YY)(2.3277,2.9503) │┌───────────────┴────────────────┐│  Rxx(2.4346) ││      ││  Rxx(2.8835) │ │           │           │ └───────────────┬────────────────┘┌┴───┴─┐   │    │     └──┬──────┬──┘          │                   │             └───────┬───┬────────┘ │           │              │    │U1(0.26147)    ┌────────────┐ │           │  Ecr │      │              ┌──────┐       ┌───────┐├──────────────┤  │     │   ┌┴────────────┤      │              ┌─┴─┐        └─────────────────┘\n",
        "q_3: ──────────┤ √X ├───────────┼──┼───■────────┼─────────────■───────┤                         ├──┼───┤0                        ├┤ U(4.3999,1.9627,5.2292,5.0565) ├┤              ├┤      ├┤1             ├─┼───────────┼───────────X─────────────────┼─────────────────┤ √Xdg ├───┼────┼────────┤1     ├─────────────■───────────────────┼─────────────────────┤ H ├──────────┼───────────┤1             ├────■───────────────┤ Ry(3.6509) ├─┼───────────┤      ├──────┼──────────────┤0     ├───────┤1      ├┤1             ├──■─────┼───┤ Ry(0.28218) ├──────┼──────────────┤ X ├───────────────────────────\n",
        "               └────┘           │  │            │             │       │                         │  │   └──────┬────────────┬─────┘└────────────────────────────────┘│              ││      │└──────────────┘ │           │P(3.4782)                    │                 └──────┘┌──┴──┐ │        │  Ecr │             │                   │                     ├───┤          │           └┬────────────┬┘┌──────────────────┐└────────────┘ │           │      │      │              │  Ecr │       │       ││  Rxx(4.8296) │      ┌─┴──┐└──────┬──────┘      │              └─┬─┘                           \n",
        "q_4: ───────────────────────────■──┼────────────■─────────────┼───────┤0                        ├──┼──────────┤ Rx(2.0472) ├────────────────────────────────────────┤1             ├┤0     ├─────────────────┼───────────■─────────────────────────────┼─────────────────────────┤ Sdg ├─┼────────┤0     ├─────────────■───────────────────┼─────────────────────┤ S ├──────────┼────────────┤ U1(4.7659) ├─┤ R(3.1038,2.0726) ├───────────────┼───────────┤0     ├──────┼──────────────┤1     ├───────┤0 Rccx ├┤0             ├──────┤ Sx ├───────┼─────────────X────────────────■─────────────────────────────\n",
@@ -470,10 +466,10 @@
        "     ┌────────────────────────┐                                       ┌─────────────────────────┐┌────┐   ┌───────────────────┐                                                     ┌──────┐                                                                                               ┌────────────────────┐┌───────────┐┌─────────────────────────┐                                   ┌──────────────┐       ┌───┐        ┌────────────┐                                   ┌────────────────────┐  ┌───┐                                                           ┌──────────────────┐                   \n",
        "q_0: ┤0                       ├────■──────────────────────────────────┤1                        ├┤ Sx ├───┤ U2(1.8116,4.2882) ├─────────────────────────────────────────────────────┤1     ├─────────────────────────────────────────X───────────────────────────────────────────────────X─┤ U2(0.56579,4.5387) ├┤ Rx(2.394) ├┤ U3(4.893,4.5044,2.8234) ├───────────────────────────────────┤0             ├───────┤ Y ├────────┤ Ry(3.7128) ├───────────────────────────■───────┤ U2(0.13579,5.1917) ├──┤ H ├──────────────────────────■────────────────────────■───────┤ R(4.1573,2.3478) ├───────────────────\n",
        "     │                        │    │ ┌───┐┌────────────┐┌────────────┐│                         │└─┬──┘   └───────────────────┘                                     ┌──────────────┐│      │                                         │                                                   │ └────────────────────┘└───────────┘└──────┬────────────┬─────┘        ┌───┐            ┌─────┐   │              │       └─┬─┘        └────────────┘             ┌──────┐      │       └────────────────────┘  ├───┴┐ ┌──────────────┐┌────┐  │        ┌───┐           │       └──────────────────┘                   \n",
-       "q_1: ┤  {XX+YY}(4.7824,4.939) ├─■──┼─┤ X ├┤ Ry(6.0991) ├┤ Rx(1.1905) ├┤                         ├──┼────────────────────────────────────────────────────────────────┤0             ├┤      ├─────────────────■───────────■───────────┼──────────────────────────────────────────────■────┼───────────■───────────────────────────────┤ Rx(1.8945) ├──────────────┤ Y ├────────────┤ Sdg ├───┤              ├─────────■─────────────────────────────────────┤1     ├──────┼─────────────■─────────────────┤ Sx ├─┤0             ├┤ Sx ├──┼────────┤ T ├───────────┼────────────────■─────────────────────────────\n",
+       "q_1: ┤  (XX+YY)(4.7824,4.939) ├─■──┼─┤ X ├┤ Ry(6.0991) ├┤ Rx(1.1905) ├┤                         ├──┼────────────────────────────────────────────────────────────────┤0             ├┤      ├─────────────────■───────────■───────────┼──────────────────────────────────────────────■────┼───────────■───────────────────────────────┤ Rx(1.8945) ├──────────────┤ Y ├────────────┤ Sdg ├───┤              ├─────────■─────────────────────────────────────┤1     ├──────┼─────────────■─────────────────┤ Sx ├─┤0             ├┤ Sx ├──┼────────┤ T ├───────────┼────────────────■─────────────────────────────\n",
        "     │                        │ │  │ └─┬─┘└──┬─────┬───┘└──┬─────┬───┘│                         │  │   ┌─────────────────────────┐                                  │              ││      │┌──────────────┐ │           │           │ ┌────────────────────────────────┐ ┌───┐     │    │     ┌─────┴──────┐                        └─────┬──────┘      ┌───────┴───┴────────┐   └─────┘   │  Rzx(5.3823) │                                               │      │      │             │P(5.8925)        └─┬──┘ │  Rxx(4.5678) │└─┬──┘  │    ┌───┴───┴────┐      │                │          ┌─────────────────┐\n",
-       "q_2: ┤1                       ├─┼──┼───┼─────┤ Sdg ├───────┤ Tdg ├────┤  {XX-YY}(5.6116,4.8907) ├──┼───┤1                        ├────────────────■─────────────────┤              ├┤  Ecr ├┤0             ├─┼───────────┼───────────┼─┤ U(3.5136,1.9098,0.19363,2.744) ├─┤ X ├─────┼────┼─────┤ Ry(1.0491) ├──────────■───────────────────┼─────────────┤ U2(1.7105,0.60564) ├─■───────────┤              ├────■──────────────────────────────■───────────┤      ├──────┼─────────────■───────────────────■────┤1             ├──┼─────┼────┤ U1(1.9926) ├──────X────────────────┼──────────┤ R(1.985,4.8507) ├\n",
-       "     └─────────┬────┬─────────┘ │  │   │     └──┬──┘       └─────┘    │                         │  │   │  {XX-YY}(2.3277,2.9503) │┌───────────────┴────────────────┐│  Rxx(2.4346) ││      ││  Rxx(2.8835) │ │           │           │ └───────────────┬────────────────┘┌┴───┴─┐   │    │     └──┬──────┬──┘          │                   │             └───────┬───┬────────┘ │           │              │    │U1(0.26147)    ┌────────────┐ │           │  Ecr │      │              ┌──────┐       ┌───────┐├──────────────┤  │     │   ┌┴────────────┤      │              ┌─┴─┐        └─────────────────┘\n",
+       "q_2: ┤1                       ├─┼──┼───┼─────┤ Sdg ├───────┤ Tdg ├────┤  (XX-YY)(5.6116,4.8907) ├──┼───┤1                        ├────────────────■─────────────────┤              ├┤  Ecr ├┤0             ├─┼───────────┼───────────┼─┤ U(3.5136,1.9098,0.19363,2.744) ├─┤ X ├─────┼────┼─────┤ Ry(1.0491) ├──────────■───────────────────┼─────────────┤ U2(1.7105,0.60564) ├─■───────────┤              ├────■──────────────────────────────■───────────┤      ├──────┼─────────────■───────────────────■────┤1             ├──┼─────┼────┤ U1(1.9926) ├──────X────────────────┼──────────┤ R(1.985,4.8507) ├\n",
+       "     └─────────┬────┬─────────┘ │  │   │     └──┬──┘       └─────┘    │                         │  │   │  (XX-YY)(2.3277,2.9503) │┌───────────────┴────────────────┐│  Rxx(2.4346) ││      ││  Rxx(2.8835) │ │           │           │ └───────────────┬────────────────┘┌┴───┴─┐   │    │     └──┬──────┬──┘          │                   │             └───────┬───┬────────┘ │           │              │    │U1(0.26147)    ┌────────────┐ │           │  Ecr │      │              ┌──────┐       ┌───────┐├──────────────┤  │     │   ┌┴────────────┤      │              ┌─┴─┐        └─────────────────┘\n",
        "q_3: ──────────┤ √X ├───────────┼──┼───■────────┼─────────────■───────┤                         ├──┼───┤0                        ├┤ U(4.3999,1.9627,5.2292,5.0565) ├┤              ├┤      ├┤1             ├─┼───────────┼───────────X─────────────────┼─────────────────┤ √Xdg ├───┼────┼────────┤1     ├─────────────■───────────────────┼─────────────────────┤ H ├──────────┼───────────┤1             ├────■───────────────┤ Ry(3.6509) ├─┼───────────┤      ├──────┼──────────────┤0     ├───────┤1      ├┤1             ├──■─────┼───┤ Ry(0.28218) ├──────┼──────────────┤ X ├───────────────────────────\n",
        "               └────┘           │  │            │             │       │                         │  │   └──────┬────────────┬─────┘└────────────────────────────────┘│              ││      │└──────────────┘ │           │P(3.4782)                    │                 └──────┘┌──┴──┐ │        │  Ecr │             │                   │                     ├───┤          │           └┬────────────┬┘┌──────────────────┐└────────────┘ │           │      │      │              │  Ecr │       │       ││  Rxx(4.8296) │      ┌─┴──┐└──────┬──────┘      │              └─┬─┘                           \n",
        "q_4: ───────────────────────────■──┼────────────■─────────────┼───────┤0                        ├──┼──────────┤ Rx(2.0472) ├────────────────────────────────────────┤1             ├┤0     ├─────────────────┼───────────■─────────────────────────────┼─────────────────────────┤ Sdg ├─┼────────┤0     ├─────────────■───────────────────┼─────────────────────┤ S ├──────────┼────────────┤ U1(4.7659) ├─┤ R(3.1038,2.0726) ├───────────────┼───────────┤0     ├──────┼──────────────┤1     ├───────┤0 Rccx ├┤0             ├──────┤ Sx ├───────┼─────────────X────────────────■─────────────────────────────\n",
@@ -597,17 +593,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "id": "34787aad",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 17,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -628,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "id": "764d4828",
    "metadata": {},
    "outputs": [
@@ -638,7 +634,7 @@
        ""
       ]
      },
-     "execution_count": 18,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -658,18 +654,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "id": "4a8a8760",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -704,63 +700,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "id": "02ad6d3e",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_21335/1309103975.py:9: DeprecationWarning: The class ``qiskit.utils.quantum_instance.QuantumInstance`` is deprecated as of qiskit-terra 0.24.0. It will be removed no earlier than 3 months after the release date. For code migration guidelines, visit https://qisk.it/qi_migration.\n",
-      "  qi = QuantumInstance(local_simulator, seed_transpiler=42, seed_simulator=42)\n",
-      "/tmp/ipykernel_21335/1309103975.py:13: DeprecationWarning: The class ``qiskit.algorithms.minimum_eigen_solvers.vqe.VQE`` is deprecated as of qiskit-terra 0.24.0. It will be removed no earlier than 3 months after the release date. Instead, use the class ``qiskit.algorithms.minimum_eigensolvers.VQE``. See https://qisk.it/algo_migration for a migration guide.\n",
-      "  vqe = VQE(ansatz, optimizer=slsqp, quantum_instance=qi)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{   'aux_operator_eigenvalues': None,\n",
+      "{   'aux_operators_evaluated': None,\n",
       "    'cost_function_evals': 9,\n",
-      "    'eigenstate': {   '00': 0.5854685623498498,\n",
-      "                      '01': 0.5837981778834189,\n",
-      "                      '10': 0.46770717334674267,\n",
-      "                      '11': 0.3125},\n",
-      "    'eigenvalue': (-1.2938597123240512+0j),\n",
-      "    'optimal_circuit': None,\n",
-      "    'optimal_parameters': {   ParameterVectorElement(θ[1]): -2.7502662569061473,\n",
-      "                              ParameterVectorElement(θ[0]): -1.4898253895470077,\n",
-      "                              ParameterVectorElement(θ[2]): 5.777835248686062,\n",
-      "                              ParameterVectorElement(θ[3]): 5.998464701792347,\n",
-      "                              ParameterVectorElement(θ[4]): 1.6774540702480465,\n",
-      "                              ParameterVectorElement(θ[5]): -3.1634349542322076,\n",
-      "                              ParameterVectorElement(θ[6]): 4.089977645429185,\n",
-      "                              ParameterVectorElement(θ[7]): -5.51571636504145},\n",
-      "    'optimal_point': array([-1.48982539, -2.75026626,  5.77783525,  5.9984647 ,  1.67745407,\n",
-      "       -3.16343495,  4.08997765, -5.51571637]),\n",
-      "    'optimal_value': -1.2938597123240512,\n",
+      "    'eigenvalue': -1.4272080711283344,\n",
+      "    'optimal_circuit': ,\n",
+      "    'optimal_parameters': {   ParameterVectorElement(θ[0]): 1.054197452954356,\n",
+      "                              ParameterVectorElement(θ[1]): 2.759415645946344,\n",
+      "                              ParameterVectorElement(θ[2]): -3.157245172580292,\n",
+      "                              ParameterVectorElement(θ[3]): -1.504397646177443,\n",
+      "                              ParameterVectorElement(θ[4]): -0.8460982537780612,\n",
+      "                              ParameterVectorElement(θ[5]): 3.3343214671170145,\n",
+      "                              ParameterVectorElement(θ[6]): 3.4110127659318756,\n",
+      "                              ParameterVectorElement(θ[7]): -5.015554393329303},\n",
+      "    'optimal_point': array([ 1.05419745,  2.75941565, -3.15724517, -1.50439765, -0.84609825,\n",
+      "        3.33432147,  3.41101277, -5.01555439]),\n",
+      "    'optimal_value': -1.4272080711283344,\n",
       "    'optimizer_evals': None,\n",
-      "    'optimizer_result': None,\n",
-      "    'optimizer_time': 1.0542807579040527}\n"
+      "    'optimizer_result': ,\n",
+      "    'optimizer_time': 3.5380678176879883}\n"
      ]
     }
    ],
    "source": [
-    "H2_op = (\n",
-    "    (-1.052373245772859 * I ^ I)\n",
-    "    + (0.39793742484318045 * I ^ Z)\n",
-    "    + (-0.39793742484318045 * Z ^ I)\n",
-    "    + (-0.01128010425623538 * Z ^ Z)\n",
-    "    + (0.18093119978423156 * X ^ X)\n",
+    "H2_op = SparsePauliOp(\n",
+    "    [\"II\", \"IZ\", \"ZI\", \"ZZ\", \"XX\"],\n",
+    "    coeffs=[\n",
+    "        -1.052373245772859,\n",
+    "        0.39793742484318045,\n",
+    "        -0.39793742484318045,\n",
+    "        -0.01128010425623538,\n",
+    "        0.18093119978423156,\n",
+    "    ],\n",
     ")\n",
     "\n",
-    "qi = QuantumInstance(local_simulator, seed_transpiler=42, seed_simulator=42)\n",
+    "estimator = BackendEstimator(local_simulator, skip_transpilation=False)\n",
     "ansatz = TwoLocal(rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
     "slsqp = SLSQP(maxiter=1)\n",
     "\n",
-    "vqe = VQE(ansatz, optimizer=slsqp, quantum_instance=qi)\n",
+    "vqe = VQE(estimator=estimator, ansatz=ansatz, optimizer=slsqp)\n",
     "\n",
     "result = vqe.compute_minimum_eigenvalue(H2_op)\n",
     "print(result)"
@@ -787,7 +772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 1,
    "id": "5968d451",
    "metadata": {},
    "outputs": [
@@ -797,15 +782,11 @@
      "text": [
       "\"\"\"Example of Hybrid Job payload with VQE.\"\"\"\n",
       "from braket.jobs import save_job_result\n",
-      "from qiskit.opflow import (\n",
-      "    I,\n",
-      "    X,\n",
-      "    Z,\n",
-      ")\n",
-      "from qiskit.algorithms import VQE\n",
+      "from qiskit.quantum_info import SparsePauliOp\n",
+      "from qiskit.algorithms.minimum_eigensolvers import VQE\n",
       "from qiskit.algorithms.optimizers import SLSQP\n",
       "from qiskit.circuit.library import TwoLocal\n",
-      "from qiskit.utils import QuantumInstance\n",
+      "from qiskit.primitives import BackendEstimator\n",
       "\n",
       "from qiskit_braket_provider import AWSBraketProvider\n",
       "\n",
@@ -813,28 +794,32 @@
       "def main():\n",
       "    backend = AWSBraketProvider().get_backend(\"SV1\")\n",
       "\n",
-      "    h2_op = (\n",
-      "        (-1.052373245772859 * I ^ I)\n",
-      "        + (0.39793742484318045 * I ^ Z)\n",
-      "        + (-0.39793742484318045 * Z ^ I)\n",
-      "        + (-0.01128010425623538 * Z ^ Z)\n",
-      "        + (0.18093119978423156 * X ^ X)\n",
+      "    h2_op = SparsePauliOp(\n",
+      "        [\"II\", \"IZ\", \"ZI\", \"ZZ\", \"XX\"],\n",
+      "        coeffs=[\n",
+      "            -1.052373245772859,\n",
+      "            0.39793742484318045,\n",
+      "            -0.39793742484318045,\n",
+      "            -0.01128010425623538,\n",
+      "            0.18093119978423156,\n",
+      "        ],\n",
       "    )\n",
       "\n",
-      "    quantum_instance = QuantumInstance(\n",
-      "        backend, seed_transpiler=42, seed_simulator=42, shots=10\n",
+      "    estimator = BackendEstimator(\n",
+      "        backend=backend,\n",
+      "        options={\"seed_simulator\": 42, \"seed_transpiler\": 42, \"shots\": 10},\n",
+      "        skip_transpilation=False,\n",
       "    )\n",
       "    ansatz = TwoLocal(rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
       "    slsqp = SLSQP(maxiter=1)\n",
       "\n",
-      "    vqe = VQE(ansatz, optimizer=slsqp, quantum_instance=quantum_instance)\n",
+      "    vqe = VQE(estimator=estimator, ansatz=ansatz, optimizer=slsqp)\n",
       "\n",
       "    vqe_result = vqe.compute_minimum_eigenvalue(h2_op)\n",
       "\n",
       "    save_job_result(\n",
       "        {\n",
       "            \"VQE\": {\n",
-      "                \"eigenstate\": vqe_result.eigenstate,\n",
       "                \"eigenvalue\": vqe_result.eigenvalue.real,\n",
       "                \"optimal_parameters\": list(vqe_result.optimal_parameters.values()),\n",
       "                \"optimal_point\": vqe_result.optimal_point.tolist(),\n",
@@ -864,7 +849,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 20,
    "id": "646bfb43",
    "metadata": {
     "slideshow": {
@@ -963,7 +948,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/1_tutorial_vqe.ipynb b/docs/tutorials/1_tutorial_vqe.ipynb
index ecc3ea66..451c12bf 100644
--- a/docs/tutorials/1_tutorial_vqe.ipynb
+++ b/docs/tutorials/1_tutorial_vqe.ipynb
@@ -10,20 +10,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "ebac1b5c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit.algorithms import VQE\n",
-    "from qiskit.opflow import (\n",
-    "    I,\n",
-    "    X,\n",
-    "    Z,\n",
-    ")\n",
+    "from qiskit.algorithms.minimum_eigensolvers import VQE\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "from qiskit.algorithms.optimizers import SLSQP\n",
     "from qiskit.circuit.library import TwoLocal\n",
-    "from qiskit.utils import QuantumInstance, algorithm_globals\n",
+    "from qiskit.utils import algorithm_globals\n",
+    "from qiskit.primitives import BackendEstimator\n",
     "\n",
     "from qiskit_braket_provider import AWSBraketProvider, BraketLocalBackend\n",
     "\n",
@@ -48,7 +45,7 @@
     {
      "data": {
       "text/plain": [
-       "BraketBackend[sv_simulator]"
+       "BraketBackend[default]"
       ]
      },
      "execution_count": 2,
@@ -106,45 +103,48 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{   'aux_operator_eigenvalues': None,\n",
+      "{   'aux_operators_evaluated': None,\n",
       "    'cost_function_evals': 9,\n",
-      "    'eigenstate': {   '00': 0.7824990015968072,\n",
-      "                      '01': 0.489139870078079,\n",
-      "                      '10': 0.3814548629916782,\n",
-      "                      '11': 0.05412658773652741},\n",
-      "    'eigenvalue': (-1.0606393547183097+0j),\n",
-      "    'optimal_parameters': {   ParameterVectorElement(θ[1]): 4.19301252102391,\n",
-      "                              ParameterVectorElement(θ[0]): 3.611860069224077,\n",
+      "    'eigenvalue': -1.0778032163726936,\n",
+      "    'optimal_circuit': ,\n",
+      "    'optimal_parameters': {   ParameterVectorElement(θ[0]): 3.611860069224077,\n",
+      "                              ParameterVectorElement(θ[1]): 4.19301252102391,\n",
       "                              ParameterVectorElement(θ[2]): 0.6019852007557844,\n",
       "                              ParameterVectorElement(θ[3]): 5.949536809130025,\n",
       "                              ParameterVectorElement(θ[4]): -3.3070470445355764,\n",
+      "                              ParameterVectorElement(θ[5]): 1.8462931831829383,\n",
       "                              ParameterVectorElement(θ[6]): -5.466043598406607,\n",
-      "                              ParameterVectorElement(θ[7]): 0.6984088030463615,\n",
-      "                              ParameterVectorElement(θ[5]): 1.8462931831829383},\n",
+      "                              ParameterVectorElement(θ[7]): 0.6984088030463615},\n",
       "    'optimal_point': array([ 3.61186007,  4.19301252,  0.6019852 ,  5.94953681, -3.30704704,\n",
       "        1.84629318, -5.4660436 ,  0.6984088 ]),\n",
-      "    'optimal_value': -1.0606393547183097,\n",
+      "    'optimal_value': -1.0778032163726936,\n",
       "    'optimizer_evals': None,\n",
-      "    'optimizer_time': 54.37140512466431}\n"
+      "    'optimizer_result': ,\n",
+      "    'optimizer_time': 4.060570001602173}\n"
      ]
     }
    ],
    "source": [
-    "H2_op = (\n",
-    "    (-1.052373245772859 * I ^ I)\n",
-    "    + (0.39793742484318045 * I ^ Z)\n",
-    "    + (-0.39793742484318045 * Z ^ I)\n",
-    "    + (-0.01128010425623538 * Z ^ Z)\n",
-    "    + (0.18093119978423156 * X ^ X)\n",
+    "H2_op = SparsePauliOp(\n",
+    "    [\"II\", \"IZ\", \"ZI\", \"ZZ\", \"XX\"],\n",
+    "    coeffs=[\n",
+    "        -1.052373245772859,\n",
+    "        0.39793742484318045,\n",
+    "        -0.39793742484318045,\n",
+    "        -0.01128010425623538,\n",
+    "        0.18093119978423156,\n",
+    "    ],\n",
     ")\n",
     "\n",
-    "qi = QuantumInstance(\n",
-    "    state_vector_simulator_backend, seed_transpiler=seed, seed_simulator=seed\n",
+    "estimator = BackendEstimator(\n",
+    "    local_simulator,\n",
+    "    options={\"seed_transpiler\": seed, \"seed_simulator\": seed},\n",
+    "    skip_transpilation=False,\n",
     ")\n",
     "ansatz = TwoLocal(rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
     "slsqp = SLSQP(maxiter=1)\n",
     "\n",
-    "vqe = VQE(ansatz, optimizer=slsqp, quantum_instance=qi)\n",
+    "vqe = VQE(estimator=estimator, ansatz=ansatz, optimizer=slsqp)\n",
     "\n",
     "result = vqe.compute_minimum_eigenvalue(H2_op)\n",
     "print(result)"
@@ -167,7 +167,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/2_tutorial_hybrid_jobs_byoc.ipynb b/docs/tutorials/2_tutorial_hybrid_jobs_byoc.ipynb
index b1ec24ce..659524e2 100644
--- a/docs/tutorials/2_tutorial_hybrid_jobs_byoc.ipynb
+++ b/docs/tutorials/2_tutorial_hybrid_jobs_byoc.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 1,
    "id": "b9184273",
    "metadata": {},
    "outputs": [
@@ -34,53 +34,53 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\"\"\"Example of Hybrid Job payload with VQE.\"\"\"\r\n",
-      "from braket.jobs import save_job_result\r\n",
-      "from qiskit.opflow import (\r\n",
-      "    I,\r\n",
-      "    X,\r\n",
-      "    Z,\r\n",
-      ")\r\n",
-      "from qiskit.algorithms import VQE\r\n",
-      "from qiskit.algorithms.optimizers import SLSQP\r\n",
-      "from qiskit.circuit.library import TwoLocal\r\n",
-      "from qiskit.utils import QuantumInstance\r\n",
-      "\r\n",
-      "from qiskit_braket_provider import AWSBraketProvider\r\n",
-      "\r\n",
-      "\r\n",
-      "def main():\r\n",
-      "    backend = AWSBraketProvider().get_backend(\"SV1\")\r\n",
-      "\r\n",
-      "    h2_op = (\r\n",
-      "        (-1.052373245772859 * I ^ I)\r\n",
-      "        + (0.39793742484318045 * I ^ Z)\r\n",
-      "        + (-0.39793742484318045 * Z ^ I)\r\n",
-      "        + (-0.01128010425623538 * Z ^ Z)\r\n",
-      "        + (0.18093119978423156 * X ^ X)\r\n",
-      "    )\r\n",
-      "\r\n",
-      "    quantum_instance = QuantumInstance(\r\n",
-      "        backend, seed_transpiler=42, seed_simulator=42, shots=10\r\n",
-      "    )\r\n",
-      "    ansatz = TwoLocal(rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\r\n",
-      "    slsqp = SLSQP(maxiter=1)\r\n",
-      "\r\n",
-      "    vqe = VQE(ansatz, optimizer=slsqp, quantum_instance=quantum_instance)\r\n",
-      "\r\n",
-      "    vqe_result = vqe.compute_minimum_eigenvalue(h2_op)\r\n",
-      "\r\n",
-      "    save_job_result(\r\n",
-      "        {\r\n",
-      "            \"VQE\": {\r\n",
-      "                \"eigenstate\": vqe_result.eigenstate,\r\n",
-      "                \"eigenvalue\": vqe_result.eigenvalue.real,\r\n",
-      "                \"optimal_parameters\": list(vqe_result.optimal_parameters.values()),\r\n",
-      "                \"optimal_point\": vqe_result.optimal_point.tolist(),\r\n",
-      "                \"optimal_value\": vqe_result.optimal_value.real,\r\n",
-      "            }\r\n",
-      "        }\r\n",
-      "    )\r\n"
+      "\"\"\"Example of Hybrid Job payload with VQE.\"\"\"\n",
+      "from braket.jobs import save_job_result\n",
+      "from qiskit.quantum_info import SparsePauliOp\n",
+      "from qiskit.algorithms.minimum_eigensolvers import VQE\n",
+      "from qiskit.algorithms.optimizers import SLSQP\n",
+      "from qiskit.circuit.library import TwoLocal\n",
+      "from qiskit.primitives import BackendEstimator\n",
+      "\n",
+      "from qiskit_braket_provider import AWSBraketProvider\n",
+      "\n",
+      "\n",
+      "def main():\n",
+      "    backend = AWSBraketProvider().get_backend(\"SV1\")\n",
+      "\n",
+      "    h2_op = SparsePauliOp(\n",
+      "        [\"II\", \"IZ\", \"ZI\", \"ZZ\", \"XX\"],\n",
+      "        coeffs=[\n",
+      "            -1.052373245772859,\n",
+      "            0.39793742484318045,\n",
+      "            -0.39793742484318045,\n",
+      "            -0.01128010425623538,\n",
+      "            0.18093119978423156,\n",
+      "        ],\n",
+      "    )\n",
+      "\n",
+      "    estimator = BackendEstimator(\n",
+      "        backend=backend,\n",
+      "        options={\"seed_simulator\": 42, \"seed_transpiler\": 42, \"shots\": 10},\n",
+      "        skip_transpilation=False,\n",
+      "    )\n",
+      "    ansatz = TwoLocal(rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
+      "    slsqp = SLSQP(maxiter=1)\n",
+      "\n",
+      "    vqe = VQE(estimator=estimator, ansatz=ansatz, optimizer=slsqp)\n",
+      "\n",
+      "    vqe_result = vqe.compute_minimum_eigenvalue(h2_op)\n",
+      "\n",
+      "    save_job_result(\n",
+      "        {\n",
+      "            \"VQE\": {\n",
+      "                \"eigenvalue\": vqe_result.eigenvalue.real,\n",
+      "                \"optimal_parameters\": list(vqe_result.optimal_parameters.values()),\n",
+      "                \"optimal_point\": vqe_result.optimal_point.tolist(),\n",
+      "                \"optimal_value\": vqe_result.optimal_value.real,\n",
+      "            }\n",
+      "        }\n",
+      "    )\n"
      ]
     }
    ],
@@ -256,7 +256,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/3_tutorial_minimum_eigen_optimizer.ipynb b/docs/tutorials/3_tutorial_minimum_eigen_optimizer.ipynb
index 2f5bfd24..c38277ee 100644
--- a/docs/tutorials/3_tutorial_minimum_eigen_optimizer.ipynb
+++ b/docs/tutorials/3_tutorial_minimum_eigen_optimizer.ipynb
@@ -13,54 +13,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "704bfb64",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting qiskit_optimization\n",
-      "  Downloading qiskit_optimization-0.5.0-py3-none-any.whl (156 kB)\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m156.5/156.5 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
-      "\u001b[?25hRequirement already satisfied: qiskit-terra>=0.22.4 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from qiskit_optimization) (0.24.1)\n",
-      "Requirement already satisfied: scipy>=1.4 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from qiskit_optimization) (1.10.1)\n",
-      "Requirement already satisfied: numpy>=1.17 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from qiskit_optimization) (1.23.5)\n",
-      "Collecting docplex!=2.24.231,>=2.21.207 (from qiskit_optimization)\n",
-      "  Downloading docplex-2.25.236.tar.gz (633 kB)\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m633.5/633.5 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
-      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
-      "\u001b[?25hRequirement already satisfied: setuptools>=40.1.0 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from qiskit_optimization) (67.7.2)\n",
-      "Requirement already satisfied: networkx>=2.6.3 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from qiskit_optimization) (3.1)\n",
-      "Requirement already satisfied: six in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from docplex!=2.24.231,>=2.21.207->qiskit_optimization) (1.16.0)\n",
-      "Requirement already satisfied: rustworkx>=0.12.0 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from qiskit-terra>=0.22.4->qiskit_optimization) (0.13.0)\n",
-      "Requirement already satisfied: ply>=3.10 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from qiskit-terra>=0.22.4->qiskit_optimization) (3.11)\n",
-      "Requirement already satisfied: psutil>=5 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from qiskit-terra>=0.22.4->qiskit_optimization) (5.9.5)\n",
-      "Requirement already satisfied: sympy>=1.3 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from qiskit-terra>=0.22.4->qiskit_optimization) (1.12)\n",
-      "Requirement already satisfied: dill>=0.3 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from qiskit-terra>=0.22.4->qiskit_optimization) (0.3.6)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.0 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from qiskit-terra>=0.22.4->qiskit_optimization) (2.8.2)\n",
-      "Requirement already satisfied: stevedore>=3.0.0 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from qiskit-terra>=0.22.4->qiskit_optimization) (5.1.0)\n",
-      "Requirement already satisfied: symengine<0.10,>=0.9 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from qiskit-terra>=0.22.4->qiskit_optimization) (0.9.2)\n",
-      "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from stevedore>=3.0.0->qiskit-terra>=0.22.4->qiskit_optimization) (5.11.1)\n",
-      "Requirement already satisfied: mpmath>=0.19 in /home/robotastray/qiskit-braket-provider/venv/lib/python3.8/site-packages (from sympy>=1.3->qiskit-terra>=0.22.4->qiskit_optimization) (1.3.0)\n",
-      "Building wheels for collected packages: docplex\n",
-      "  Building wheel for docplex (setup.py) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for docplex: filename=docplex-2.25.236-py3-none-any.whl size=671350 sha256=9249c6a84de2faf8bdcbab214a249d5d0f59baacde525363554a1e68347f1283\n",
-      "  Stored in directory: /home/robotastray/.cache/pip/wheels/b8/98/f8/22c3fe8d29be988cc4584363f494a459fb8f09c16d8e438ac7\n",
-      "Successfully built docplex\n",
-      "Installing collected packages: docplex, qiskit_optimization\n",
-      "Successfully installed docplex-2.25.236 qiskit_optimization-0.5.0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "! pip install qiskit_optimization"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 23,
    "id": "2c692b55",
    "metadata": {},
    "outputs": [],
@@ -69,8 +32,10 @@
     "\n",
     "import numpy as np\n",
     "\n",
-    "from qiskit.utils import algorithm_globals, QuantumInstance\n",
-    "from qiskit.algorithms import QAOA, NumPyMinimumEigensolver\n",
+    "from qiskit.utils import algorithm_globals\n",
+    "from qiskit.primitives import BackendSampler\n",
+    "from qiskit.algorithms.minimum_eigensolvers import QAOA, NumPyMinimumEigensolver\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
     "from qiskit.visualization import plot_histogram\n",
     "from qiskit_optimization.algorithms import (\n",
     "    MinimumEigenOptimizer,\n",
@@ -94,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "id": "f55baa58",
    "metadata": {},
    "outputs": [
@@ -136,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "id": "eae79719",
    "metadata": {},
    "outputs": [
@@ -164,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "id": "ec6b4990",
    "metadata": {},
    "outputs": [
@@ -209,38 +174,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
    "id": "e17f6bc4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_69023/2374560280.py:3: DeprecationWarning: The class ``qiskit.utils.quantum_instance.QuantumInstance`` is deprecated as of qiskit-terra 0.24.0. It will be removed no earlier than 3 months after the release date. For code migration guidelines, visit https://qisk.it/qi_migration.\n",
-      "  quantum_instance = QuantumInstance(\n",
-      "/tmp/ipykernel_69023/2374560280.py:8: DeprecationWarning: The class ``qiskit.algorithms.minimum_eigen_solvers.qaoa.QAOA`` is deprecated as of qiskit-terra 0.24.0. It will be removed no earlier than 3 months after the release date. Instead, use the class ``qiskit.algorithms.minimum_eigensolvers.QAOA``. See https://qisk.it/algo_migration for a migration guide.\n",
-      "  qaoa_mes = QAOA(quantum_instance=quantum_instance, initial_point=[0.0, 0.0])\n",
-      "/tmp/ipykernel_69023/2374560280.py:9: DeprecationWarning: The class ``qiskit.algorithms.minimum_eigen_solvers.numpy_minimum_eigen_solver.NumPyMinimumEigensolver`` is deprecated as of qiskit-terra 0.24.0. It will be removed no earlier than 3 months after the release date. Instead, use the class ``qiskit.algorithms.minimum_eigensolvers.NumPyMinimumEigensolver``. See https://qisk.it/algo_migration for a migration guide.\n",
-      "  exact_mes = NumPyMinimumEigensolver()\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "algorithm_globals.random_seed = 10598\n",
     "\n",
-    "quantum_instance = QuantumInstance(\n",
-    "    AWSBraketProvider().backends(local=True)[0],\n",
-    "    seed_simulator=algorithm_globals.random_seed,\n",
-    "    seed_transpiler=algorithm_globals.random_seed,\n",
+    "sampler = BackendSampler(\n",
+    "    backend=AWSBraketProvider().backends(local=True)[0],\n",
+    "    options={\n",
+    "        \"seed_simulator\": algorithm_globals.random_seed,\n",
+    "        \"seed_transpiler\": algorithm_globals.random_seed,\n",
+    "    },\n",
+    "    skip_transpilation=False,\n",
     ")\n",
-    "qaoa_mes = QAOA(quantum_instance=quantum_instance, initial_point=[0.0, 0.0])\n",
+    "qaoa_mes = QAOA(sampler=sampler, optimizer=COBYLA(), initial_point=[0.0, 0.0])\n",
     "exact_mes = NumPyMinimumEigensolver()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 25,
    "id": "9e3248ad",
    "metadata": {},
    "outputs": [],
@@ -253,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 26,
    "id": "81d2e400",
    "metadata": {},
    "outputs": [
@@ -272,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 27,
    "id": "59e7a201",
    "metadata": {},
    "outputs": [
@@ -300,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 28,
    "id": "c54be53e",
    "metadata": {},
    "outputs": [
@@ -309,13 +264,14 @@
      "output_type": "stream",
      "text": [
       "variable order: ['x', 'y', 'z']\n",
-      "SolutionSample(x=array([0., 1., 0.]), fval=-2.0, probability=0.123046875, status=)\n",
-      "SolutionSample(x=array([1., 1., 0.]), fval=0.0, probability=0.302734375, status=)\n",
-      "SolutionSample(x=array([1., 0., 0.]), fval=1.0, probability=0.0888671875, status=)\n",
-      "SolutionSample(x=array([0., 0., 1.]), fval=3.0, probability=0.22851562500000003, status=)\n",
-      "SolutionSample(x=array([1., 0., 1.]), fval=3.0, probability=0.12500000000000003, status=)\n",
-      "SolutionSample(x=array([0., 1., 1.]), fval=3.0, probability=0.025390624999999997, status=)\n",
-      "SolutionSample(x=array([1., 1., 1.]), fval=4.0, probability=0.10644531250000001, status=)\n"
+      "SolutionSample(x=array([0., 1., 0.]), fval=-2.0, probability=0.4013671875, status=)\n",
+      "SolutionSample(x=array([1., 1., 0.]), fval=0.0, probability=0.162109375, status=)\n",
+      "SolutionSample(x=array([0., 0., 0.]), fval=0.0, probability=0.2216796875, status=)\n",
+      "SolutionSample(x=array([1., 0., 0.]), fval=1.0, probability=0.1376953125, status=)\n",
+      "SolutionSample(x=array([0., 1., 1.]), fval=3.0, probability=0.0146484375, status=)\n",
+      "SolutionSample(x=array([1., 0., 1.]), fval=3.0, probability=0.0322265625, status=)\n",
+      "SolutionSample(x=array([0., 0., 1.]), fval=3.0, probability=0.0283203125, status=)\n",
+      "SolutionSample(x=array([1., 1., 1.]), fval=4.0, probability=0.001953125, status=)\n"
      ]
     }
    ],
@@ -327,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 29,
    "id": "651b9550",
    "metadata": {},
    "outputs": [],
@@ -350,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 30,
    "id": "224a2a4a",
    "metadata": {},
    "outputs": [
@@ -358,13 +314,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "SolutionSample(x=array([0., 1., 0.]), fval=-2.0, probability=0.123046875, status=)\n",
-      "SolutionSample(x=array([1., 1., 0.]), fval=0.0, probability=0.302734375, status=)\n",
-      "SolutionSample(x=array([1., 0., 0.]), fval=1.0, probability=0.0888671875, status=)\n",
-      "SolutionSample(x=array([0., 0., 1.]), fval=3.0, probability=0.22851562500000003, status=)\n",
-      "SolutionSample(x=array([1., 0., 1.]), fval=3.0, probability=0.12500000000000003, status=)\n",
-      "SolutionSample(x=array([0., 1., 1.]), fval=3.0, probability=0.025390624999999997, status=)\n",
-      "SolutionSample(x=array([1., 1., 1.]), fval=4.0, probability=0.10644531250000001, status=)\n"
+      "SolutionSample(x=array([0., 1., 0.]), fval=-2.0, probability=0.4013671875, status=)\n",
+      "SolutionSample(x=array([1., 1., 0.]), fval=0.0, probability=0.162109375, status=)\n",
+      "SolutionSample(x=array([0., 0., 0.]), fval=0.0, probability=0.2216796875, status=)\n",
+      "SolutionSample(x=array([1., 0., 0.]), fval=1.0, probability=0.1376953125, status=)\n",
+      "SolutionSample(x=array([0., 1., 1.]), fval=3.0, probability=0.0146484375, status=)\n",
+      "SolutionSample(x=array([1., 0., 1.]), fval=3.0, probability=0.0322265625, status=)\n",
+      "SolutionSample(x=array([0., 0., 1.]), fval=3.0, probability=0.0283203125, status=)\n"
      ]
     }
    ],
@@ -380,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 31,
    "id": "9da37a65",
    "metadata": {},
    "outputs": [],
@@ -391,17 +347,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 32,
    "id": "3768185e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.7142857142857142"
+       "1.5"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,17 +368,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 33,
    "id": "63c14a9b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.979486637221574"
+       "1.9364916731037085"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -433,23 +389,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 34,
    "id": "e4c4baac",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'x=0 y=1 z=0': 0.123046875,\n",
-       " 'x=1 y=1 z=0': 0.302734375,\n",
-       " 'x=1 y=0 z=0': 0.0888671875,\n",
-       " 'x=0 y=0 z=1': 0.22851562500000003,\n",
-       " 'x=1 y=0 z=1': 0.12500000000000003,\n",
-       " 'x=0 y=1 z=1': 0.025390624999999997,\n",
-       " 'x=1 y=1 z=1': 0.10644531250000001}"
+       "{'x=0 y=1 z=0': 0.4013671875,\n",
+       " 'x=1 y=1 z=0': 0.162109375,\n",
+       " 'x=0 y=0 z=0': 0.2216796875,\n",
+       " 'x=1 y=0 z=0': 0.1376953125,\n",
+       " 'x=0 y=1 z=1': 0.0146484375,\n",
+       " 'x=1 y=0 z=1': 0.0322265625,\n",
+       " 'x=0 y=0 z=1': 0.0283203125}"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -466,18 +422,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 35,
    "id": "d101a797",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -497,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 36,
    "id": "cca567a1",
    "metadata": {},
    "outputs": [],
@@ -509,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 37,
    "id": "4eaa9994",
    "metadata": {},
    "outputs": [
@@ -528,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 38,
    "id": "0772b179",
    "metadata": {},
    "outputs": [],
@@ -542,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 39,
    "id": "96a34ccb",
    "metadata": {},
    "outputs": [
@@ -552,7 +508,7 @@
        "{'x=0 y=1 z=0': 1.0}"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -569,18 +525,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 40,
    "id": "d3a65a6b",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -588,14 +544,6 @@
    "source": [
     "plot_histogram(samples_for_plot)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "672737ce",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -614,7 +562,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/data/2_hybrid_jobs/job_script.py b/docs/tutorials/data/2_hybrid_jobs/job_script.py
index 28c0a68c..1c3e84eb 100644
--- a/docs/tutorials/data/2_hybrid_jobs/job_script.py
+++ b/docs/tutorials/data/2_hybrid_jobs/job_script.py
@@ -1,14 +1,10 @@
 """Example of Hybrid Job payload with VQE."""
 from braket.jobs import save_job_result
-from qiskit.opflow import (
-    I,
-    X,
-    Z,
-)
-from qiskit.algorithms import VQE
+from qiskit.quantum_info import SparsePauliOp
+from qiskit.algorithms.minimum_eigensolvers import VQE
 from qiskit.algorithms.optimizers import SLSQP
 from qiskit.circuit.library import TwoLocal
-from qiskit.utils import QuantumInstance
+from qiskit.primitives import BackendEstimator
 
 from qiskit_braket_provider import AWSBraketProvider
 
@@ -16,28 +12,32 @@
 def main():
     backend = AWSBraketProvider().get_backend("SV1")
 
-    h2_op = (
-        (-1.052373245772859 * I ^ I)
-        + (0.39793742484318045 * I ^ Z)
-        + (-0.39793742484318045 * Z ^ I)
-        + (-0.01128010425623538 * Z ^ Z)
-        + (0.18093119978423156 * X ^ X)
+    h2_op = SparsePauliOp(
+        ["II", "IZ", "ZI", "ZZ", "XX"],
+        coeffs=[
+            -1.052373245772859,
+            0.39793742484318045,
+            -0.39793742484318045,
+            -0.01128010425623538,
+            0.18093119978423156,
+        ],
     )
 
-    quantum_instance = QuantumInstance(
-        backend, seed_transpiler=42, seed_simulator=42, shots=10
+    estimator = BackendEstimator(
+        backend=backend,
+        options={"seed_simulator": 42, "seed_transpiler": 42, "shots": 10},
+        skip_transpilation=False,
     )
     ansatz = TwoLocal(rotation_blocks="ry", entanglement_blocks="cz")
     slsqp = SLSQP(maxiter=1)
 
-    vqe = VQE(ansatz, optimizer=slsqp, quantum_instance=quantum_instance)
+    vqe = VQE(estimator=estimator, ansatz=ansatz, optimizer=slsqp)
 
     vqe_result = vqe.compute_minimum_eigenvalue(h2_op)
 
     save_job_result(
         {
             "VQE": {
-                "eigenstate": vqe_result.eigenstate,
                 "eigenvalue": vqe_result.eigenvalue.real,
                 "optimal_parameters": list(vqe_result.optimal_parameters.values()),
                 "optimal_point": vqe_result.optimal_point.tolist(),
diff --git a/tests/providers/test_adapter.py b/tests/providers/test_adapter.py
index fbb0ebde..ac9809cf 100644
--- a/tests/providers/test_adapter.py
+++ b/tests/providers/test_adapter.py
@@ -10,7 +10,7 @@
 from qiskit import QuantumCircuit, execute, BasicAer
 from qiskit.circuit import Parameter
 from qiskit.circuit.library import PauliEvolutionGate
-from qiskit.opflow import I, Z, X
+from qiskit.quantum_info import SparsePauliOp
 
 from qiskit.circuit.library.standard_gates import (
     HGate,
@@ -234,7 +234,13 @@ def test_exponential_gate_decomp(self):
         backend = BraketLocalBackend()
         qiskit_circuit = QuantumCircuit(2)
 
-        operator = (Z ^ Z) - 0.1 * (X ^ I)
+        operator = SparsePauliOp(
+            ["ZZ", "XI"],
+            coeffs=[
+                1,
+                -0.1,
+            ],
+        )
         evo = PauliEvolutionGate(operator, time=2)
 
         qiskit_circuit.append(evo, range(2))
diff --git a/tests/providers/test_braket_backend.py b/tests/providers/test_braket_backend.py
index 81291d50..4e7b6803 100644
--- a/tests/providers/test_braket_backend.py
+++ b/tests/providers/test_braket_backend.py
@@ -6,20 +6,18 @@
 
 from botocore import errorfactory
 from qiskit import QuantumCircuit, transpile, BasicAer
-from qiskit.algorithms import VQE, VQEResult
+
+from qiskit.algorithms.minimum_eigensolvers import VQE, VQEResult
+
 from qiskit.algorithms.optimizers import (
     SLSQP,
 )
 from qiskit.circuit.library import TwoLocal
 from qiskit.circuit.random import random_circuit
-from qiskit.opflow import (
-    I,
-    X,
-    Z,
-)
+from qiskit.quantum_info import SparsePauliOp
 from qiskit.result import Result
 from qiskit.transpiler import Target
-from qiskit.utils import QuantumInstance
+from qiskit.primitives import BackendEstimator
 
 from qiskit_braket_provider import AWSBraketProvider, version
 from qiskit_braket_provider.providers import AWSBraketBackend, BraketLocalBackend
@@ -142,22 +140,23 @@ def test_local_backend_circuit_shots0(self):
     def test_vqe(self):
         """Tests VQE."""
         local_simulator = BraketLocalBackend(name="default")
-
-        h2_op = (
-            (-1.052373245772859 * I ^ I)
-            + (0.39793742484318045 * I ^ Z)
-            + (-0.39793742484318045 * Z ^ I)
-            + (-0.01128010425623538 * Z ^ Z)
-            + (0.18093119978423156 * X ^ X)
+        h2_op = SparsePauliOp(
+            ["II", "IZ", "ZI", "ZZ", "XX"],
+            coeffs=[
+                -1.052373245772859,
+                0.39793742484318045,
+                -0.39793742484318045,
+                -0.01128010425623538,
+                0.18093119978423156,
+            ],
         )
 
-        quantum_instance = QuantumInstance(
-            local_simulator, seed_transpiler=42, seed_simulator=42
-        )
+        estimator = BackendEstimator(backend=local_simulator, skip_transpilation=False)
+
         ansatz = TwoLocal(rotation_blocks="ry", entanglement_blocks="cz")
         slsqp = SLSQP(maxiter=1)
 
-        vqe = VQE(ansatz, optimizer=slsqp, quantum_instance=quantum_instance)
+        vqe = VQE(estimator=estimator, ansatz=ansatz, optimizer=slsqp)
 
         result = vqe.compute_minimum_eigenvalue(h2_op)