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

• URL: http://arxiv.org/abs/2204.03695v1
• Date: Thu, 7 Apr 2022 18:57:57 GMT
• Title: A Shuttle-Efficient Qubit Mapper for Trapped-Ion Quantum Computers
• Authors: Suryansh Upadhyay, Abdullah Ash Saki, Rasit Onur Topaloglu and Swaroop Ghosh
• Abstract summary: We propose a new policy for quantum computer programs with considerable depth and high number of qubits. 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.
• Score: 4.903455321543022
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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).

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