Efficient Compilation for Shuttling Trapped-Ion Machines via the Position Graph Architectural Abstraction

2025-01-21

AI Summary

Quantum circuit compilation for QCCD-based trapped-ion quantum computers faces severe hardware constraints and low compilation efficiency. Method: This work introduces a unified hardware abstraction—termed the Position Graph—and proposes SHAPER, a novel heuristic scheduling algorithm that pioneers the adaptation of advanced superconducting-platform compilation techniques to the trapped-ion architecture. The approach integrates permutation-aware qubit mapping with physical-constraint-driven instruction generation. Contribution/Results: Evaluated on realistic trapped-ion hardware, our method successfully compiles complex circuits that fail under existing state-of-the-art (SOTA) compilers. It achieves an average 14% speedup in scheduling time, with peak improvements reaching 69%, thereby significantly overcoming current performance bottlenecks in trapped-ion quantum circuit compilation.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

With the growth of quantum platforms for gate-based quantum computation, compilation holds a crucial factor in deciding the success of the implementation. There has been rich research and development in compilation techniques for the superconducting-qubit regime. In contrast, the trapped-ion architectures, currently leading in robust quantum computations due to their reliable operations, do not have many competitive compilation strategies. This work presents a novel unifying abstraction, called the position graph, for different types of hardware architectures. Using this abstraction, we model trapped-ion Quantum Charge-Coupled Device (QCCD) architectures and enable high-quality, scalable superconducting compilation methods. In particular, we devise a scheduling algorithm called SHuttling-Aware PERmutative heuristic search algorithm (SHAPER) to tackle the complex constraints and dynamics of trapped-ion QCCD with the cooperation of state-of-the-art permutation-aware mapping. This approach generates native, executable circuits and ion instructions on the hardware that adheres to the physical constraints of shuttling-based quantum computers. Using the position graph abstraction, we evaluate our algorithm on theorized and actual architectures. Our algorithm can successfully compile programs for these architectures where other state-of-the-art algorithms fail. In the cases when other algorithms complete, our algorithm produces a schedule that is $14%$ faster on average, up to $69%$ in the best case.\ {f Reproducibility:} source code and computational results are available at $[$will be added upon acceptance$]$

Problem

• Ion Trap Quantum Computing
• Programming Efficiency
• Compilation Techniques

Innovation

• Position Map
• SHAPER Algorithm
• QCCD Compilation Efficiency