TrapSIMD: SIMD-Aware Compiler Optimization for 2D Trapped-Ion Quantum Machines
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918002327289856 |
|---|---|
| author | Ruan, Jixuan Zhang, Hezi Fang, Xiang Li, Ang Campbell, Wesley C. Hudson, Eric Hayes, David Haeffner, Hartmut Humble, Travis Palsberg, Jens Ding, Yufei |
| author_facet | Ruan, Jixuan Zhang, Hezi Fang, Xiang Li, Ang Campbell, Wesley C. Hudson, Eric Hayes, David Haeffner, Hartmut Humble, Travis Palsberg, Jens Ding, Yufei |
| contents | Modular trapped-ion (TI) architectures offer a scalable quantum computing (QC) platform, with native transport behaviors that closely resemble the Single Instruction Multiple Data (SIMD) paradigm. We present FluxTrap, a SIMD-aware compiler framework that establishes a hardware-software co-design interface for TI systems. FluxTrap introduces a novel abstraction that unifies SIMD-style instructions -- including segmented intra-trap shift SIMD (S3) and global junction transfer SIMD (JT-SIMD) operations -- with a SIMD-enriched architectural graph, capturing key features such as transport synchronization, gate-zone locality, and topological constraints. It applies two passes -- SIMD aggregation and scheduling -- to coordinate grouped ion transport and gate execution within architectural constraints. On NISQ benchmarks, FluxTrap reduces execution time by up to $3.82 \times$ and improves fidelity by several orders of magnitude. It also scales to fault-tolerant workloads under diverse hardware configurations, providing feedback for future TI hardware design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17886 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TrapSIMD: SIMD-Aware Compiler Optimization for 2D Trapped-Ion Quantum Machines Ruan, Jixuan Zhang, Hezi Fang, Xiang Li, Ang Campbell, Wesley C. Hudson, Eric Hayes, David Haeffner, Hartmut Humble, Travis Palsberg, Jens Ding, Yufei Quantum Physics Modular trapped-ion (TI) architectures offer a scalable quantum computing (QC) platform, with native transport behaviors that closely resemble the Single Instruction Multiple Data (SIMD) paradigm. We present FluxTrap, a SIMD-aware compiler framework that establishes a hardware-software co-design interface for TI systems. FluxTrap introduces a novel abstraction that unifies SIMD-style instructions -- including segmented intra-trap shift SIMD (S3) and global junction transfer SIMD (JT-SIMD) operations -- with a SIMD-enriched architectural graph, capturing key features such as transport synchronization, gate-zone locality, and topological constraints. It applies two passes -- SIMD aggregation and scheduling -- to coordinate grouped ion transport and gate execution within architectural constraints. On NISQ benchmarks, FluxTrap reduces execution time by up to $3.82 \times$ and improves fidelity by several orders of magnitude. It also scales to fault-tolerant workloads under diverse hardware configurations, providing feedback for future TI hardware design. |
| title | TrapSIMD: SIMD-Aware Compiler Optimization for 2D Trapped-Ion Quantum Machines |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2504.17886 |