A Shuttle-Efficient Qubit Mapper for Trapped-Ion Quantum Computers

TL;DR

This paper introduces a new qubit mapping policy for trapped-ion quantum computers that reduces the number of ion shuttles and improves fidelity by prioritizing early-stage gates, addressing limitations of existing methods.

Contribution

It proposes a program-adaptive qubit mapping policy that considers gate timing, significantly reducing shuttles and enhancing fidelity in multi-trap trapped-ion quantum systems.

Findings

Average 9% reduction in shuttles per program

Up to 21.3% reduction in shuttles in best cases

Fidelity improved up to 3.3 times

Abstract

Trapped-ion (TI) quantum computer is one of the forerunner quantum technologies. However, TI systems can have a limited number of qubits in a single trap. Execution of meaningful quantum algorithms requires a multiple trap system. In such systems, the computation may frequently involve ions from two different traps for which the qubits must be co-located in the same trap, hence one of the ions needs to be shuttled (moved) between traps, increasing the vibrational energy, degrading fidelity, and increasing the program execution time. The choice of initial mapping influences the number of shuttles. The existing Greedy policy counts the number of gates occurring between each pair of qubits and assigns edge weight. The qubits with high edge weights are placed close to each other. However, it neglects the stage of the program at which the gate is occurring. Intuitively, the contribution of the late-occurring gates to the initial mapping reduces since the ions might have already shuttled to a different trap to satisfy other gate operations. In this paper, we target this gap and propose a new policy especially for programs with considerable depth and high number of qubits (valid for practical-scale quantum programs). Our policy is program adaptive and prioritizes the gates re-occurring at the initial stages of the program over late occurring gates. Our technique achieves an average reduction of 9% shuttles/program (with 21.3% at best) for 120 random circuits and enhances the program fidelity up to 3.3X (1.41X on average).