Machine Learning Optimization of Quantum Circuit Layouts

• URL: http://arxiv.org/abs/2007.14608v2
• Date: Sun, 25 Sep 2022 20:30:47 GMT
• Title: Machine Learning Optimization of Quantum Circuit Layouts
• Authors: Alexandru Paler, Lucian M. Sasu, Adrian Florea, Razvan Andonie
• Abstract summary: We introduce a quantum circuit mapping, QXX, and its machine learning version, QXX-MLP. The latter infers automatically the optimal QXX parameter values such that the layed out circuit has a reduced depth. We present empiric evidence for the feasibility of learning the layout method using approximation.
• Score: 63.55764634492974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quantum circuit layout (QCL) problem is to map 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 infers automatically the optimal QXX parameter values such that the layed out circuit has a reduced depth. In order to speed up circuit compilation, before laying the circuits out, we are using 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.

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