Avoid Operator construction overhead in Optimize 1q decomposition pass #5909
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This commit further tunes the performance of the optimize 1q decomposition pass. Previously, the pass was constructing an operator using the Operator qi class and then calling compose() on each gate in the run to find the overall unitary of the run. However, doing this was constructing a lot of intermediate operators that weren't needed and just added extra overhead. To avoid that overhead this commit relies on the raw numpy calculations to build the matrix for the overall operator and then wraps that in an Operator() object only once when we go to call the OneQubitEulerDecomposer.
kdk
reviewed
Feb 25, 2021
qiskit/transpiler/passes/optimization/optimize_1q_decomposition.py
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I did some quick manual benchmarking (only one sample each run so not sure what the error is) with a
From when I profiled the pass recently: #5775 (comment) the biggest bottleneck is the decomposer, so it looks like the 2x difference, which is only ~40us, ends up lost in the noise. |
kdk
approved these changes
Feb 26, 2021
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Summary
This commit further tunes the performance of the optimize 1q
decomposition pass. Previously, the pass was constructing an operator
using the Operator qi class and then calling compose() on each gate in
the run to find the overall unitary of the run. However, doing this was
constructing a lot of intermediate operators that weren't needed and
just added extra overhead. To avoid that overhead this commit relies on
the raw numpy calculations to build the matrix for the overall operator
and then wraps that in an Operator() object only once when we go to call
the OneQubitEulerDecomposer.
Details and comments