2020 article

Application of Quantum Machine Learning to VLSI Placement

PROCEEDINGS OF THE 2020 ACM/IEEE 2ND WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD '20), pp. 61–66.

By: I. Turtletaub n, G. Li n, M. Ibrahim n & P. Franzon n 

co-author countries: United States of America πŸ‡ΊπŸ‡Έ
author keywords: Quantum Machine Learning; Balanced Min-Cut; Variational Quantum Eigensolver; Recursive Partitioning Placement
Source: Web Of Science
Added: August 16, 2021

Considerable advances in quantum computing with functioning noisy, near-term devices have allowed for the application space to grow as a emerging field for problems with large solution spaces. However, current quantum hardware is limited in scale and noisy in generated data, necessitating hybrid quantum-classical solutions for viability of results and convergence. A quantum backend generates data for classical algorithms to optimize control parameters with, creating a hybrid quantum-classical computing loop. VLSI placement problems have shown potential for utilization, where traditionally heuristic solutions such as Kernighan-Lin (KL) are used. The Variational Quantum Eigensolver (VQE) is used to formulate a recursive Balanced Min-Cut (BMC) algorithm, and we suggest that quantum machine learning techniques can lower error rates and allow for faster convergence to an optimal solution.