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pr-3313/README.md

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Additional links that may be helpful that are not listed above:
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- [References and automatic link generation in
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Doxygen](https://www.star.bnl.gov/public/comp/sofi/doxygen/autolink.html)
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- [Using Napoleon style for Python doc
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comments](https://docs.softwareheritage.org/devel/contributing/sphinx.html)
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- [Cross-referencing Python
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pr-3313/_images/qpus.png

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pr-3313/_sources/api/languages/cpp_api.rst.txt

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.. doxygentypedef:: cudaq::KernelThunkType
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.. doxygenstruct:: cudaq::CodeGenConfig
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Utilities
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=========
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pr-3313/_sources/api/languages/python_api.rst.txt

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.. autofunction:: cudaq::reset_target
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.. autofunction:: cudaq::set_noise
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.. autofunction:: cudaq::unset_noise
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.. autofunction:: cudaq::register_set_target_callback
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.. autofunction:: cudaq::unregister_set_target_callback
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.. function:: cudaq.apply_noise(error_type, parameters..., targets...)
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pr-3313/_sources/applications/python/generate_fermionic_ham.ipynb.txt

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pr-3313/_sources/applications/python/mps_encoding.ipynb.txt

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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install quimb==1.10.0 numba==0.60.0 -q"
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"!pip install quimb==1.11.2 numba==0.60.0 --break-system-packages -q"
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]
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},
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{

pr-3313/_sources/applications/python/skqd.ipynb.txt

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pr-3313/_sources/applications/python/unitary_compilation_diffusion_models.ipynb.txt

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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install genQC==0.2.3 -q"
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"!pip install genQC==0.2.3 torch --break-system-packages -q"
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]
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pr-3313/_sources/using/applications.rst.txt

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/applications/python/uccsd_wf_ansatz
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/applications/python/mps_encoding
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/applications/python/qm_mm_pe
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/applications/python/skqd
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.. |:spellcheck-enable:| replace:: \
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<img src="../_static/app_title_images/krylov_preview.png" alt="Krylov Preview" class="notebook-image">
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</div>
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<div class="notebook-entry" data-tags="chemistry,noiseless">
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<a href="../applications/python/skqd.html" class="notebook-title">Sample-Based Krylov Quantum Diagonalization (SKQD)</a>
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<div class="notebook-content">
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Learn how to implement the Sample-Based Krylov Quantum Diagonalization (SKQD) algorithm to predict the ground state energy of molecules.
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</div>
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</div>
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<div class="notebook-entry" data-tags="cpu,fundamental,noiseless,gpu">
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<a href="../applications/python/bernstein_vazirani.html" class="notebook-title">The Bernstein-Vazirani Algorithm</a>
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<div class="notebook-content">

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