@@ -17,7 +17,7 @@ Aqua includes domain application support for:
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* [ Machine Learning] ( #machine-learning )
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* [ Optimization] ( #optimization )
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- _ ** Note** : the Chemistry application stack was the first domain worked on. At the time of writing
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+ _ ** Note** : the chemistry module was the first domain worked on. At the time of writing
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the other domains have some logic in them but are not as fully realised. Future work is expected to
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build out functionality in all application areas._
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@@ -143,16 +143,16 @@ excited states and dipole moments of molecule, both open and closed-shell.
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The code comprises chemistry drivers, which when provided with a molecular
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configuration will return one and two-body integrals as well as other data that is efficiently
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computed classically. This output data from a driver can then be used as input to the chemistry
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- application stack that contains logic which is able to translate this into a form that is suitable
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+ module that contains logic which is able to translate this into a form that is suitable
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for quantum algorithms. The conversion first creates a FermionicOperator which must then be mapped,
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e.g. by a Jordan Wigner mapping, to a qubit operator in readiness for the quantum computation.
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### Optional Installs
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- To run chemistry experiments using Qiskit Chemistry , it is recommended that you to install a
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- classical computation chemistry software program/library interfaced by Qiskit Chemistry .
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+ To run chemistry experiments using Qiskit's chemistry module , it is recommended that you to install
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+ a classical computation chemistry software program/library interfaced by Qiskit.
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Several, as listed below, are supported, and while logic to interface these programs is supplied by
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- Qiskit Chemistry via the above pip installation, the dependent programs/libraries themselves need
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+ the chemistry module via the above pip installation, the dependent programs/libraries themselves need
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to be installed separately.
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Note: As ` PySCF ` can be installed via pip the installation of Qiskit (Aqua) will install PySCF
@@ -166,8 +166,8 @@ whether this might be possible manually.
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### HDF5 Driver
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- A useful functionality integrated into Qiskit Chemistry is its ability to serialize a file in
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- Hierarchical Data Format 5 (HDF5) format representing all the output data from a chemistry driver.
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+ A useful functionality integrated into Qiskit's chemistry module is its ability to serialize a file
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+ in hierarchical Data Format 5 (HDF5) format representing all the output data from a chemistry driver.
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The [ HDF5 driver] ( qiskit/chemistry/drivers/hdf5d/hdf5driver.py#L25 )
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accepts such such HDF5 files as input so molecular experiments can be run, albeit on the fixed data
@@ -182,9 +182,9 @@ repository. This
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[ HDF5 Driver tutorial] ( https://github.com/Qiskit/qiskit-community-tutorials/blob/master/chemistry/hdf5_files_and_driver.ipynb )
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contains further information about creating and using such HDF5 files.
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- ### Creating Your First Qiskit Chemistry Programming Experiment
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+ ### Creating Your First Chemistry Programming Experiment in Qiskit
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- Now that Qiskit is installed, it's time to begin working with Chemistry .
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+ Now that Qiskit is installed, it's time to begin working with the chemistry module .
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Let's try a chemistry application experiment using VQE (Variational Quantum Eigensolver) algorithm
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to compute the ground-state (minimum) energy of a molecule.
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@@ -255,7 +255,7 @@ of the circuits. By passing in a backend as is done above it is internally wrapp
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### Further examples
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- Jupyter notebooks containing further examples, for Qiskit Chemistry, may be found in the
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+ Jupyter notebooks containing further chemistry examples may be found in the
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following Qiskit GitHub repositories at
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[ qiskit-iqx-tutorials/qiskit/advanced/aqua/chemistry] ( https://github.com/Qiskit/qiskit-iqx-tutorials/tree/master/qiskit/advanced/aqua/chemistry )
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and
@@ -269,9 +269,9 @@ The `qiskit.finance` package contains uncertainty components for stock/securitie
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Ising translators for portfolio optimizations and data providers to source real or random data to
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finance experiments.
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- ### Creating Your First Qiskit Finance Programming Experiment
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+ ### Creating Your First Finance Programming Experiment in Qiskit
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- Now that Qiskit is installed, it's time to begin working with Finance .
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+ Now that Qiskit is installed, it's time to begin working with the finance module .
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Let's try a experiment using Amplitude Estimation algorithm to
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evaluate a fixed income asset with uncertain interest rates.
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@@ -305,7 +305,7 @@ When running the above the estimated value result should be 2.46 and probability
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### Further examples
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- Jupyter notebooks containing further examples, for Qiskit Finance, may be found in the
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+ Jupyter notebooks containing further finance examples may be found in the
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following Qiskit GitHub repositories at
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[ qiskit-iqx-tutorials/qiskit/advanced/aqua/finance] ( https://github.com/Qiskit/qiskit-iqx-tutorials/tree/master/qiskit/advanced/aqua/finance )
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and
@@ -320,7 +320,7 @@ classification algorithms such as QSVM and VQC (Variational Quantum Classifier),
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can be used for experiments, and there is also QGAN (Quantum Generative Adversarial Network)
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algorithm.
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- ### Creating Your First Qiskit Machine Learning Programming Experiment
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+ ### Creating Your First Machine Learning Programming Experiment in Qiskit
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Now that Qiskit is installed, it's time to begin working with Machine Learning.
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Let's try a experiment using VQC (Variational Quantum Classified) algorithm to
@@ -359,7 +359,7 @@ print('Testing accuracy: {:0.2f}'.format(result['testing_accuracy']))
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### Further examples
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- Jupyter notebooks containing further examples, for Qiskit Machine Learning, may be found in the
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+ Jupyter notebooks containing further Machine Learning examples may be found in the
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following Qiskit GitHub repositories at
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[ qiskit-iqx-tutorials/qiskit/advanced/aqua/machine_learning] ( https://github.com/Qiskit/qiskit-iqx-tutorials/tree/master/qiskit/advanced/aqua/machine_learning )
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and
@@ -376,9 +376,9 @@ as Max-Cut, Traveling Salesman and Vehicle Routing. It also has a has an automat
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generator for a problem model specified by the user as a model in
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[ docplex] ( qiskit/optimization/ising/docplex.py#L16 ) .
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- ### Creating Your First Qiskit Optimization Programming Experiment
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+ ### Creating Your First Optimization Programming Experiment in Qiskit
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- Now that Qiskit is installed, it's time to begin working with Optimization .
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+ Now that Qiskit is installed, it's time to begin working with the optimization module .
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Let's try a optimization experiment using QAOA (Quantum Approximate Optimization Algorithm)
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to compute the solution of a [ Max-Cut] ( https://en.wikipedia.org/wiki/Maximum_cut ) problem using
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a docplex model to create the Ising Hamiltonian operator for QAOA.
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### Further examples
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- Jupyter notebooks containing further examples, for Qiskit Optimization , may be found in the
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+ Jupyter notebooks containing further examples, for the optimization module , may be found in the
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following Qiskit GitHub repositories at
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[ qiskit-iqx-tutorials/qiskit/advanced/aqua/optimization] ( https://github.com/Qiskit/qiskit-iqx-tutorials/tree/master/qiskit/advanced/aqua/optimization )
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and
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