mirror of https://github.com/Qiskit/qiskit.git
31 lines
1.1 KiB
YAML
31 lines
1.1 KiB
YAML
---
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features:
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- |
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Introduced a new optimizer to Qiskit library, which adds support to the
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optimization of parameters of variational quantum algorithms. This is
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the Univariate Marginal Distribution Algorithm (UMDA), which is a specific
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type of the Estimation of Distribution Algorithms. For example::
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from qiskit.opflow import X, Z, I
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from qiskit import Aer
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from qiskit.algorithms.optimizers import UMDA
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from qiskit.algorithms import QAOA
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from qiskit.utils import QuantumInstance
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H2_op = (-1.052373245772859 * I ^ I) + \
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(0.39793742484318045 * I ^ Z) + \
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(-0.39793742484318045 * Z ^ I) + \
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(-0.01128010425623538 * Z ^ Z) + \
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(0.18093119978423156 * X ^ X)
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p = 2 # Toy example: 2 layers with 2 parameters in each layer: 4 variables
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opt = UMDA(maxiter=100, size_gen=20)
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backend = Aer.get_backend('statevector_simulator')
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vqe = QAOA(opt,
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quantum_instance=QuantumInstance(backend=backend),
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reps=p)
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result = vqe.compute_minimum_eigenvalue(operator=H2_op)
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