@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} }
 @article{khetawat_atrey_li_mueller_pakin_2019, title={Implementing NChooseK on IBM Q Quantum Computer Systems}, volume={11497}, ISBN={["978-3-030-21499-9"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-21500-2_13}, abstractNote={This work contributes a generalized model for quantum computation called NChooseK. NChooseK is based on a single parametrized primitive suitable to express a variety of problems that cannot be solved efficiently using classical computers but may admit an efficient quantum solution. We implement a code generator that, given arbitrary parameters for N and K, generates code suitable for execution on IBM Q quantum hardware. We assess the performance of the code generator, limitations in the size of circuit depth and number of gates, and propose optimizations. We identify future work to improve efficiency and applicability of the NChooseK model.}, journal={REVERSIBLE COMPUTATION (RC 2019)}, author={Khetawat, Harsh and Atrey, Ashlesha and Li, George and Mueller, Frank and Pakin, Scott}, year={2019}, pages={209–223} }