Quantum-Classical Performance Co-Optimization: A Unified Compiler Framework

Rosa et al.'s 2025 work on quantum gate decomposition reveals this interesting trade-off between compilation time and execution time that's unique to quantum computing. They propose different compilation profiles, but what if we could have both? The current approach treats quantum and classical parts as separate compilation problems. My idea is to create a unified optimization framework that views the entire quantum-classical program as one system. We'd use reinforcement learning agents that can dynamically decide how much effort to spend on quantum gate decomposition versus classical optimization, based on the program's characteristics. The agent would learn patterns like "this quantum subroutine is called frequently, so invest more in optimizing it, even if compilation takes longer" or "this quantum code is rarely executed, so use a fast decomposition method." This connects nicely with CoCoNet's approach to breaking abstraction barriers in distributed ML (Jangda et al., 2021), but applies it to the quantum-classical boundary. We could potentially achieve performance gains that exceed either the fast-compile or fast-execution profiles alone by intelligently allocating optimization resources across the entire program.

References:

1. Quantum Gate Decomposition: A Study of Compilation Time vs. Execution Time Trade-offs. E. Rosa, Jerusa Marchi, E. I. Duzzioni, Rafael de Santiago (2025). Brazilian Symposium on Programming Languages.
2. CoCoNet: Co-Optimizing Computation and Communication for Distributed Machine Learning. Abhinav Jangda, Jun Huang, Guodong Liu, Amir Hossein Nodehi Sabet, Saeed Maleki, Youshan Miao, M. Musuvathi, Todd Mytkowicz, Olli Saarikivi (2021). arXiv.org.