Machine Learning Optimization of Quantum Circuit Layouts

The quantum circuit layout (QCL) problem involves mapping out a quantum circuit such that the constraints of the device are satisfied. We introduce a quantum circuit mapping heuristic, QXX, and its machine learning version, QXX-MLP. The latter automatically infers the optimal QXX parameter values su...

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Bibliographic Details
Published in:ACM transactions on quantum computing (Print) 2023-02, Vol.4 (2), p.1-25, Article 12
Main Authors: Paler, Alexandru, Sasu, Lucian, Florea, Adrian-Cătălin, Andonie, Răzvan
Format: Article
Language:English
Subjects:
Compilers
Hardware
Quantum computation
Software and its engineering
Software tools for EDA
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Summary:The quantum circuit layout (QCL) problem involves mapping out a quantum circuit such that the constraints of the device are satisfied. We introduce a quantum circuit mapping heuristic, QXX, and its machine learning version, QXX-MLP. The latter automatically infers the optimal QXX parameter values such that the laid out circuit has a reduced depth. In order to speed up circuit compilation, before laying the circuits out, we use a Gaussian function to estimate the depth of the compiled circuits. This Gaussian also informs the compiler about the circuit region that influences most the resulting circuit’s depth. We present empiric evidence for the feasibility of learning the layout method using approximation. QXX and QXX-MLP open the path to feasible large-scale QCL methods.
ISSN:2643-6809
2643-6817
DOI:10.1145/3565271