@article{turtletaub_li_ibrahim_franzon_2020, title={Application of Quantum Machine Learning to VLSI Placement}, DOI={10.1145/3380446.3430644}, abstractNote={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.}, journal={PROCEEDINGS OF THE 2020 ACM/IEEE 2ND WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD '20)}, author={Turtletaub, Isaac and Li, George and Ibrahim, Mohannad and Franzon, Paul}, year={2020}, pages={61–66} }