Bosehedral: Compiler Optimization for Bosonic Quantum Computing

TL;DR

Bosehedral is a novel compiler optimization framework for bosonic quantum computing that enhances performance by reducing gate counts and maintaining high fidelity through advanced program analysis and optimization techniques.

Contribution

It introduces a high-level optimization approach for bosonic quantum programs, overcoming the challenge of infinite-dimensional qumodes with a compact matrix representation.

Findings

Significant reduction in program size while maintaining fidelity

Improved end-to-end application performance

Effective optimization of qumode gate decomposition and mapping

Abstract

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.