Plutarch: Toward Scalable Operational Parallelism on Racetrack-Shaped Trapped-Ion Processors

TL;DR

This paper analyzes the efficiency of racetrack-shaped trapped-ion quantum processors for general-purpose programs, revealing that increasing zones can hurt performance and proposing strategies to optimize parallelism and reduce overhead.

Contribution

It introduces Plutarch, a set of strategies that improve execution efficiency by optimizing zone utilization and reducing ion circulation overhead in quantum processors.

Findings

Expanding zones can degrade runtime performance due to increased ion circulation.

Plutarch's strategies improve parallelism and reduce overhead in quantum execution.

Optimizations lead to better scalability on racetrack-shaped quantum processors.

Abstract

A recent advancement in quantum computing shows a quantum advantage of certified randomness on the racetrack processor. This work investigates the execution efficiency of this architecture for general-purpose programs. We first explore the impact of increasing zones on runtime efficiency. Counterintuitively, our evaluations using variational programs reveal that expanding zones may degrade runtime performance under the existing scheduling policy. This degradation may be attributed to the increase in track length, which increases ion circulation overhead, offsetting the benefits of enhanced parallelism. To mitigate this, the proposed \textit{Plutarch} exploits 3 strategies: (i) unitary decomposition and translation to maximize zone utilization, (ii) prioritizing the execution of nearby gates over ion circulation, and (iii) implementing shortcuts to provide the alternative path.