Machine learning applied to the quantum circuit layout problem

André Filipe Mestre Mendes

Published Apr 1, 2025• 0 citations

Abstract

This dissertation presents an innovative approach to the Quantum Circuit Layout problem by leveraging advanced Machine Learning techniques, specifically Graph Neural Networks. The Quantum Circuit Layout problem is crucial for optimizing the placement and routing of qubits in quantum circuits, particularly under the constraints of current quantum hardware. By establishing graph representations for the quantum circuits and computers, we provide a foundation for understanding the complexity of the Quantum Circuit Layout problem, demonstrating its equivalence to the well-studied Subgraph Isomorphism problem, which is known to be NP-Complete. Our proposed solution, the Adaptive Edge-Deleting Network, employs a novel adaptive edgedeleting mechanism to enhance the extraction of relevant features from these graphs while maintaining the structural integrity essential for subgraph matching. By iteratively refining node representations through neural message passing and incorporating a unique attention mechanism, Adaptive Edge-Deleting Network significantly improves the efficiency of graph embedding allowing for a novel way to solve the Quantum Circuit Layout problem.

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