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Update notebooks #157

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Feb 20, 2024
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Update notebooks #157

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0b76740
update notebooks
jcjaskula-aws Feb 13, 2024
ce71237
clean data folder and black
jcjaskula-aws Feb 13, 2024
00e40e8
simplify tutorial #3 first cell
jcjaskula-aws Feb 13, 2024
9e7e4ee
isort and exclude eigen optimizer notebook
jcjaskula-aws Feb 13, 2024
79bed42
fix linters
jcjaskula-aws Feb 14, 2024
4a3f04e
fix typos
jcjaskula-aws Feb 14, 2024
999504e
fix typo
jcjaskula-aws Feb 14, 2024
a406104
fix a second typo
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be9700c
fix more typos
jcjaskula-aws Feb 14, 2024
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fix even more typos
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Update docs/tutorials/1_tutorial_vqe.ipynb
jcjaskula-aws Feb 19, 2024
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652 changes: 158 additions & 494 deletions docs/tutorials/0_tutorial_qiskit-braket-provider_overview.ipynb
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81 changes: 38 additions & 43 deletions docs/tutorials/1_tutorial_vqe.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -5,22 +5,39 @@
"id": "7f19aecc",
"metadata": {},
"source": [
"# Tutorial: Runing VQE on Braket backend"
"# Running VQE on an Amazon Braket backend"
]
},
{
"cell_type": "markdown",
"id": "55c313b7",
"metadata": {},
"source": [
"Let's review an example of running algorithms using Qiskit on Braket devices.\n",
"\n",
"Qiskit provides a number of Algorithms and they are grouped by category according to the task they can perform. For instance Minimum Eigensolvers to find the smallest eigen value of an operator, for example ground state energy of a chemistry Hamiltonian or a solution to an optimization problem when expressed as an Ising Hamiltonian.\n",
"\n",
"Algorithms are configurable and often part of the configuration will be in the form of smaller building blocks, of which different instances of the building block type can be given. For instance with `VQE`, the Variational Quantum Eigensolver, it takes a trial wavefunction, in the form of a `QuantumCircuit` and a classical optimizer among other things.\n",
"\n",
"Let’s take a look at an example to construct a VQE instance. Here `TwoLocal` is the variational form (trial wavefunction), a parameterized circuit which can be varied, and SLSQP a classical optimizer. Then we pass the Hamiltonian to `compute_minimum_eigenvalue` method of `VQE` to get result.\n",
"\n",
"Full example and explanation of algorithm detail you can find [here](https://qiskit-community.github.io/qiskit-algorithms/tutorials/01_algorithms_introduction.html).\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"id": "ebac1b5c",
"metadata": {},
"outputs": [],
"source": [
"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 algorithm_globals\n",
"from qiskit.primitives import BackendEstimator\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_algorithms.utils import algorithm_globals\n",
"\n",
"from qiskit_braket_provider import AWSBraketProvider, BraketLocalBackend\n",
"\n",
Expand All @@ -38,7 +55,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"id": "e94c63b1",
"metadata": {},
"outputs": [
Expand All @@ -48,7 +65,7 @@
"BraketBackend[default]"
]
},
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -59,34 +76,12 @@
"local_simulator"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b618fa9e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BraketBackend[SV1]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state_vector_simulator_backend = provider.get_backend(\"SV1\")\n",
"state_vector_simulator_backend"
]
},
{
"cell_type": "markdown",
"id": "140737fe",
"metadata": {},
"source": [
"Running VQE\n",
"## Running VQE\n",
"\n",
"\n",
"\n",
Expand All @@ -95,7 +90,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 4,
"id": "9081f3b3",
"metadata": {},
"outputs": [
Expand All @@ -105,22 +100,22 @@
"text": [
"{ 'aux_operators_evaluated': None,\n",
" 'cost_function_evals': 9,\n",
" 'eigenvalue': -1.0778032163726936,\n",
" 'optimal_circuit': <qiskit.circuit.library.n_local.two_local.TwoLocal object at 0x13801f5d0>,\n",
" 'optimal_parameters': { ParameterVectorElement(θ[0]): 3.611860069224077,\n",
" ParameterVectorElement(θ[1]): 4.19301252102391,\n",
" 'eigenvalue': -1.0522310440132328,\n",
" 'optimal_circuit': <qiskit.circuit.library.n_local.two_local.TwoLocal object at 0x17af130d0>,\n",
" 'optimal_parameters': { ParameterVectorElement(θ[1]): 4.19301252102391,\n",
" ParameterVectorElement(θ[0]): 3.611860069224077,\n",
" ParameterVectorElement(θ[2]): 0.6019852007557844,\n",
" ParameterVectorElement(θ[6]): -5.466043598406607,\n",
" ParameterVectorElement(θ[7]): 0.6984088030463615,\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",
" 'optimal_point': array([ 3.61186007, 4.19301252, 0.6019852 , 5.94953681, -3.30704704,\n",
" 1.84629318, -5.4660436 , 0.6984088 ]),\n",
" 'optimal_value': -1.0778032163726936,\n",
" 'optimal_value': -1.0522310440132328,\n",
" 'optimizer_evals': None,\n",
" 'optimizer_result': <qiskit.algorithms.optimizers.optimizer.OptimizerResult object at 0x138be0fd0>,\n",
" 'optimizer_time': 4.060570001602173}\n"
" 'optimizer_result': <qiskit_algorithms.optimizers.optimizer.OptimizerResult object at 0x12411d700>,\n",
" 'optimizer_time': 0.6713869571685791}\n"
]
}
],
Expand Down Expand Up @@ -167,7 +162,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.9.18"
}
},
"nbformat": 4,
Expand Down
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