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Improve 1q decomposition pass with multiple matching basis #5431
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Improve 1q decomposition pass with multiple matching basis #5431
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This commit adds a new algorithms function, collect_runs(), which is used to find a list of runs in a PyDiGraph object that match a specified condition. A run in this context is any linear path inside the graph. The condition is specified using a python function that will be passed a node's data payload/weight object and will return whether it matches the condition or not. The intended use case for this function is with qiskit-terra's PyDiGraph method collect_runs() which is implementing this algorithm currently in Python on top of PyDiGraph (it will also be used by a future method collect_1q_runs which is being added in Qiskit/qiskit#5431).
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This commit adds a new algorithms function, collect_runs(), which is used to find a list of runs in a PyDiGraph object that match a specified condition. A run in this context is any linear path inside the graph. The condition is specified using a python function that will be passed a node's data payload/weight object and will return whether it matches the condition or not. The intended use case for this function is with qiskit-terra's PyDiGraph method collect_runs() which is implementing this algorithm currently in Python on top of PyDiGraph (it will also be used by a future method collect_1q_runs which is being added in Qiskit/qiskit#5431).
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* Add collect_runs() function This commit adds a new algorithms function, collect_runs(), which is used to find a list of runs in a PyDiGraph object that match a specified condition. A run in this context is any linear path inside the graph. The condition is specified using a python function that will be passed a node's data payload/weight object and will return whether it matches the condition or not. The intended use case for this function is with qiskit-terra's PyDiGraph method collect_runs() which is implementing this algorithm currently in Python on top of PyDiGraph (it will also be used by a future method collect_1q_runs which is being added in Qiskit/qiskit#5431). * Handle multiple edges to a single successor node There was a bug in the collect_runs() method when there were multiple edges between a graph and its single successor node. Since neighbors_directed() returns an iterator over the edges and not the nodes it would have multiple entries in the succesors list despite only being a single node. This commit addresses that edge case by building a HashSet of the node indices in the succesors list and checking the length of that instead of using the raw successors list. That will contain no duplicate nodes and will identify if there is a path instead of splitting on multiple edges incorrectly. * Add initial tests * Add more tests * Use Vec.dedup() and graph access instead of Vec of tuples and HashSet * Update src/lib.rs * Apply suggestions from code review Co-authored-by: Lauren Capelluto <laurencapelluto@gmail.com> * Document and test nodes can only exist in a single run This commit documents and adds tests to assert that a node can only be in a single run in the output from the collect_runs() method. In terra the equivalent python function didn't have this additional constraint so this commit attempts to make it clear so the differences between the two implementations are understood by users. Co-authored-by: Lauren Capelluto <laurencapelluto@gmail.com> Co-authored-by: Toshinari Itoko <itoko@jp.ibm.com>
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This commit makes an improvement to the Optimize1qGatesDecomposition pass to improve the output from it in two scenarios. The first scenario is if there is an over complete basis where there are multiple choices for decomposition basis. Previously in such scenarios only the first matching subset would be used. This improves that by finding runs in all matching decomposition basis to simplify any runs in those gate sets. The second improvement made here is that the decomposition is only used if it results in a decrease in depth. There were scenarios where the general decomposition of a run would result in more gates than the input to the OneQubitEulerDecomposer (typically 3 gates vs 2 input). In those cases we shouldn't use the decomposition and instead rely on the original run because we're adding gates which is the opposit of the expected behavior.
This commit adds a new DAGCircuit method, collect_1q_runs, which behaves like collect_runs(), but instead of collecting runs with gates on a name filter it looks at the number of qubits and returns runs of any op node that operate on a single qubit. This will be used by the optimize 1q decomposition pass to collect arbitrary single qubit gate runs and decompose them over the basis sets.
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…n.py Co-authored-by: Kevin Krsulich <kevin@krsulich.net>
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* Improve 1q decomposition pass with multiple matching basis This commit makes an improvement to the Optimize1qGatesDecomposition pass to improve the output from it in two scenarios. The first scenario is if there is an over complete basis where there are multiple choices for decomposition basis. Previously in such scenarios only the first matching subset would be used. This improves that by finding runs in all matching decomposition basis to simplify any runs in those gate sets. The second improvement made here is that the decomposition is only used if it results in a decrease in depth. There were scenarios where the general decomposition of a run would result in more gates than the input to the OneQubitEulerDecomposer (typically 3 gates vs 2 input). In those cases we shouldn't use the decomposition and instead rely on the original run because we're adding gates which is the opposit of the expected behavior. * Add DAGCircuit method to get 1q op node runs This commit adds a new DAGCircuit method, collect_1q_runs, which behaves like collect_runs(), but instead of collecting runs with gates on a name filter it looks at the number of qubits and returns runs of any op node that operate on a single qubit. This will be used by the optimize 1q decomposition pass to collect arbitrary single qubit gate runs and decompose them over the basis sets. * Switch to use all 1q runs instead of those just matching basis * Use retworkx.collect_runs for collect_1q_runs * Move parameterized gate split to collect_1q_runs * Reverse loops so basis is the inner loop and runs the outer * Ensure collect_1q_runs doesn't collect measurements * Only create temp circuit once per run * Move lone identity optimization into outer loop * Update qiskit/transpiler/passes/optimization/optimize_1q_decomposition.py * Fix lint * Use qc._append instead of qc.append Conflicts/Changes: For backport this makes 1 key change, the collect_1q_runs method is rewritten to be pure python instead of leveraging the retworkx collect_runs() method which was added in a newer version of retworkx than the minimum allowed version. This also partially pulls in the changes made in Qiskit#5570, but only some were backportable since the others relied on attributes that don't exist in gate objects in 0.16.x. (cherry picked from commit 5a1768f)
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…5431) (#5655) * Improve 1q decomposition pass with multiple matching basis (#5431) * Improve 1q decomposition pass with multiple matching basis This commit makes an improvement to the Optimize1qGatesDecomposition pass to improve the output from it in two scenarios. The first scenario is if there is an over complete basis where there are multiple choices for decomposition basis. Previously in such scenarios only the first matching subset would be used. This improves that by finding runs in all matching decomposition basis to simplify any runs in those gate sets. The second improvement made here is that the decomposition is only used if it results in a decrease in depth. There were scenarios where the general decomposition of a run would result in more gates than the input to the OneQubitEulerDecomposer (typically 3 gates vs 2 input). In those cases we shouldn't use the decomposition and instead rely on the original run because we're adding gates which is the opposit of the expected behavior. * Add DAGCircuit method to get 1q op node runs This commit adds a new DAGCircuit method, collect_1q_runs, which behaves like collect_runs(), but instead of collecting runs with gates on a name filter it looks at the number of qubits and returns runs of any op node that operate on a single qubit. This will be used by the optimize 1q decomposition pass to collect arbitrary single qubit gate runs and decompose them over the basis sets. * Switch to use all 1q runs instead of those just matching basis * Use retworkx.collect_runs for collect_1q_runs * Move parameterized gate split to collect_1q_runs * Reverse loops so basis is the inner loop and runs the outer * Ensure collect_1q_runs doesn't collect measurements * Only create temp circuit once per run * Move lone identity optimization into outer loop * Update qiskit/transpiler/passes/optimization/optimize_1q_decomposition.py * Fix lint * Use qc._append instead of qc.append Conflicts/Changes: For backport this makes 1 key change, the collect_1q_runs method is rewritten to be pure python instead of leveraging the retworkx collect_runs() method which was added in a newer version of retworkx than the minimum allowed version. This also partially pulls in the changes made in #5570, but only some were backportable since the others relied on attributes that don't exist in gate objects in 0.16.x. (cherry picked from commit 5a1768f) * Fix lint * Simplify collect_1q_runs * Move pylint disable to just collect_1q_runs * Reorder 1q node checks for performance
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Summary
This commit makes an improvement to the Optimize1qGatesDecomposition
pass to improve the output from it in two scenarios. The first scenario
is if there is an over complete basis where there are multiple choices
for decomposition basis. Previously in such scenarios only the first
matching subset would be used. This improves that by finding runs in all
matching decomposition basis to simplify any runs in those gate sets.
The second improvement made here is that the decomposition is only used
if it results in a decrease in depth. There were scenarios where the
general decomposition of a run would result in more gates than the
input to the OneQubitEulerDecomposer (typically 3 gates vs 2 input). In
those cases we shouldn't use the decomposition and instead rely on the
original run because we're adding gates which is the opposit of the
expected behavior.
Details and comments