Use retworkx neighbors method in noise adaptive layout#5411
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mergify[bot] merged 6 commits intoDec 9, 2020
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In Qiskit#5117 the noise adaptive layout pass was updated to use retworkx internally for performance. In that PR there were a couple of TODO comments added about using neighbors methods when they were released. The retworkx 0.6.0 release was pushed last week so we can update the usage and remove the TODO comments. The neighbors methods should have lower overhead since it only returns a list of node indices instead of a dictionary of (node index, edge weight). This will also get significantly faster (for large neighbors lists) with Qiskit/rustworkx#198.
kdk
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delapuente
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Changes in Qiskit#5411, to use retworkx's neighbors() function, replaces the returned dict with an iterator. In the dict version, neighbor node indices are the keys and therefore, they are unique. In the iterator version, node indices appear multiple times, once per edge in multigraphs. This commit use a Python set to preserve the semantics of the old version ensuring the uniqueness of the nodes.
mergify Bot
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Changes in #5411, to use retworkx's neighbors() function, replaces the returned dict with an iterator. In the dict version, neighbor node indices are the keys and therefore, they are unique. In the iterator version, node indices appear multiple times, once per edge in multigraphs. This commit use a Python set to preserve the semantics of the old version ensuring the uniqueness of the nodes. Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
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Summary
In #5117 the noise adaptive layout pass was updated to use retworkx
internally for performance. In that PR there were a couple of TODO
comments added about using neighbors methods when they were released.
The retworkx 0.6.0 release was pushed last week so we can update the
usage and remove the TODO comments. The neighbors methods should have
lower overhead since it only returns a list of node indices instead of a
dictionary of (node index, edge weight). This will also get
significantly faster (for large neighbors lists) with
Qiskit/rustworkx#198.
Details and comments