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UID:dac_DAC 2023_sess128_RESEARCH1225@linklings.com
SUMMARY:Compiler Optimization for Quantum Computing Using Reinforcement Le
 arning
DESCRIPTION:Research Manuscript\n\nNils Quetschlich (Technische Universitä
 t München), Lukas Burgholzer (Johannes Kepler University Linz), and Robert
  Wille (Technische Universität München)\n\nSimilar to classical compilatio
 n, quantum compilation is a sequential process with many compilation steps
  and possible optimization passes. \nIn this work, we propose a framework 
 which models the compilation flow as a Markov Decision Process and applies
  Reinforcement Learning to try to learn the best sequence of compilation p
 asses.\nBy defining a unifying interface for each compilation step, the fr
 amework supports the combination of techniques from different compilers an
 d optimization tools in a single compilation flow.\nExperimental evaluatio
 ns demonstrate an average increase in expected fidelity.\nFurthermore, the
  compilation flow can be optimized with regard to different criteria.\n\nT
 opic: Design\n\nKeyword: Quantum Computing
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