  Bosonic quantum computing, based on the infinite-dimensional qumodes, has
shown promise for various practical applications that are classically hard.
However, the lack of compiler optimizations has hindered its full potential.
This paper introduces Bosehedral, an efficient compiler optimization framework
for (Gaussian) Boson sampling on Bosonic quantum hardware. Bosehedral overcomes
the challenge of handling infinite-dimensional qumode gate matrices by
performing all its program analysis and optimizations at a higher algorithmic
level, using a compact unitary matrix representation. It optimizes qumode gate
decomposition and logical-to-physical qumode mapping, and introduces a tunable
probabilistic gate dropout method. Overall, Bosehedral significantly improves
the performance by accurately approximating the original program with much
fewer gates. Our evaluation shows that Bosehedral can largely reduce the
program size but still maintain a high approximation fidelity, which can
translate to significant end-to-end application performance improvement.
