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Bosehedral: Compiler Optimization for Bosonic Quantum Computing

Junyu Zhou, Yuhao Liu, Yunong Shi, Ali Javadi-Abhari, Gushu Li

TL;DR

Bosehedral targets the software stack for Gaussian Boson Sampling by bypassing the intractable infinite-dimensional gate matrices of qumodes and working directly with a compact, high-level unitary representation of the linear interferometer. The framework combines three core ideas: (i) optimized qumode gate decomposition that favors small Beamsplitter rotation angles, (ii) a permutation-based logical-to-physical qumode mapping absorbed into the unitary, and (iii) a tunable probabilistic dropout to approximate the interferometer with controllable fidelity. Together, these components reduce Beamsplitter counts by roughly 25–40% while maintaining high fidelity (≈ 98–99.9%), and deliver meaningful end-to-end performance gains across multiple benchmarks and hardware topologies. The approach demonstrates strong scalability, maintaining optimization efficiency up to hundreds of qumodes, and provides a practical pathway toward deploying Bosonic QC for real-world applications such as graph problems and molecular simulations.

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.

Bosehedral: Compiler Optimization for Bosonic Quantum Computing

TL;DR

Bosehedral targets the software stack for Gaussian Boson Sampling by bypassing the intractable infinite-dimensional gate matrices of qumodes and working directly with a compact, high-level unitary representation of the linear interferometer. The framework combines three core ideas: (i) optimized qumode gate decomposition that favors small Beamsplitter rotation angles, (ii) a permutation-based logical-to-physical qumode mapping absorbed into the unitary, and (iii) a tunable probabilistic dropout to approximate the interferometer with controllable fidelity. Together, these components reduce Beamsplitter counts by roughly 25–40% while maintaining high fidelity (≈ 98–99.9%), and deliver meaningful end-to-end performance gains across multiple benchmarks and hardware topologies. The approach demonstrates strong scalability, maintaining optimization efficiency up to hundreds of qumodes, and provides a practical pathway toward deploying Bosonic QC for real-world applications such as graph problems and molecular simulations.

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.
Paper Structure (22 sections, 16 equations, 9 figures, 3 tables)

This paper contains 22 sections, 16 equations, 9 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Continuous Variable Quantum Computing
  4. Gaussian Boson Sampling
  5. Application Encoding in GBS
  6. Problem Formulation
  7. Opportunities from Decomposition Flexibility
  8. Compilation Problem Formulation
  9. Qumodes Mapping Optimization
  10. Motivating Example
  11. Mapping via Permutation
  12. Two Properties of Elimination
  13. Finding the Permutations
  14. Probabilistic Gate Dropout
  15. Evaluation
  16. ...and 7 more sections

Figures (9)

  • Figure 1: Qubit vs Qumode
  • Figure 2: Overview of a Gaussian Boson sampling (GBS) program
  • Figure 3: Example of flexible decomposition
  • Figure 4: Example of logical-to-physical mapping
  • Figure 5: Mapping via Permutation
  • ...and 4 more figures