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Scaling and assigning resources on ion trap QCCD architectures

Anabel Ovide, Daniele Cuomo, Carmen G. Almudever

TL;DR

This work tackles the scalability of ion-trap QCCD quantum processors by focusing on qubit allocation via a Spatio-Temporal Aware Qubit Allocation (STA) algorithm. STA jointly accounts for how many distinct interactions each qubit has and when those interactions occur, using the metrics $R(q_i)=\frac{r_i}{N}$ and $T(q_i,q_j)=\sum^s I(s,q_i,q_j)\times 2^{-s}$ to guide initial placement and intra-trap rearrangements, while considering initial excess capacity. Through QCCDSim-based simulations, STA yields up to 50% reduction in circuit execution time compared with prior methods and demonstrates nuanced scalability across 1D-linear and ring QCCD topologies, with performance also sensitive to the chosen excess capacity. The results highlight that topology and algorithm structure interact to shape optimal designs, suggesting ring configurations can outperform linear ones in some regimes and that excess-capacity tuning must be tailored to the target quantum algorithm. Overall, STA advances scalable compilation for modular trapped-ion quantum computers and motivates topology-aware mapping optimizations for future multi-trap systems.

Abstract

Ion trap technologies have earned significant attention as potential candidates for quantum information processing due to their long decoherence times and precise manipulation of individual qubits, distinguishing them from other candidates in the field of quantum technologies. However, scalability remains a challenge, as introducing additional qubits into a trap increases noise and heating effects, consequently decreasing operational fidelity. Trapped-ion Quantum Charge-Coupled Device (QCCD) architectures have addressed this limitation by interconnecting multiple traps and employing ion shuttling mechanisms to transfer ions among traps. This new architectural design requires the development of novel compilation techniques for quantum algorithms, which efficiently allocate and route qubits, and schedule operations. The aim of a compiler is to minimize ion movements and, therefore, reduce the execution time of the circuit to achieve a higher fidelity. In this paper, we propose a novel approach for initial qubit placement, demonstrating enhancements of up to 50\% compared to prior methods. Furthermore, we conduct a scalability analysis on two distinct QCCD topologies: a 1D-linear array and a ring structure. Additionally, we evaluate the impact of the excess capacity -- i.e. the number of free spaces within a trap -- on the algorithm performance.

Scaling and assigning resources on ion trap QCCD architectures

TL;DR

This work tackles the scalability of ion-trap QCCD quantum processors by focusing on qubit allocation via a Spatio-Temporal Aware Qubit Allocation (STA) algorithm. STA jointly accounts for how many distinct interactions each qubit has and when those interactions occur, using the metrics and to guide initial placement and intra-trap rearrangements, while considering initial excess capacity. Through QCCDSim-based simulations, STA yields up to 50% reduction in circuit execution time compared with prior methods and demonstrates nuanced scalability across 1D-linear and ring QCCD topologies, with performance also sensitive to the chosen excess capacity. The results highlight that topology and algorithm structure interact to shape optimal designs, suggesting ring configurations can outperform linear ones in some regimes and that excess-capacity tuning must be tailored to the target quantum algorithm. Overall, STA advances scalable compilation for modular trapped-ion quantum computers and motivates topology-aware mapping optimizations for future multi-trap systems.

Abstract

Ion trap technologies have earned significant attention as potential candidates for quantum information processing due to their long decoherence times and precise manipulation of individual qubits, distinguishing them from other candidates in the field of quantum technologies. However, scalability remains a challenge, as introducing additional qubits into a trap increases noise and heating effects, consequently decreasing operational fidelity. Trapped-ion Quantum Charge-Coupled Device (QCCD) architectures have addressed this limitation by interconnecting multiple traps and employing ion shuttling mechanisms to transfer ions among traps. This new architectural design requires the development of novel compilation techniques for quantum algorithms, which efficiently allocate and route qubits, and schedule operations. The aim of a compiler is to minimize ion movements and, therefore, reduce the execution time of the circuit to achieve a higher fidelity. In this paper, we propose a novel approach for initial qubit placement, demonstrating enhancements of up to 50\% compared to prior methods. Furthermore, we conduct a scalability analysis on two distinct QCCD topologies: a 1D-linear array and a ring structure. Additionally, we evaluate the impact of the excess capacity -- i.e. the number of free spaces within a trap -- on the algorithm performance.
Paper Structure (14 sections, 2 equations, 7 figures, 3 tables, 3 algorithms)

This paper contains 14 sections, 2 equations, 7 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Trapped-ion quantum processors
  3. Mapping of quantum circuits on NISQ devices
  4. The quantum circuit mapping process
  5. From single-trap to multi-trap architectures
  6. Qubit allocation for QCCD architectures
  7. The Spatio-Temporal Aware Qubit Allocation Algorithm
  8. Simulation framework and benchmarks
  9. Evaluation and Scalability analysis
  10. Qubit allocation strategies: Random vs Greedy vs STA
  11. Topological Performance Analysis: Linear vs Ring
  12. Scalability analysis
  13. Evaluating the impact of the excess capacity
  14. Conclusion and future work

Figures (7)

  • Figure 1: Illustration of (a) a linear and (b) a ring QCCD topologies consisting of 4 traps and 4 ions per trap
  • Figure 2: Example of mapping a quantum circuit to a 1D-linear topology composed of two traps with a capacity of 4 ions each. (a) Program to be executed on the device. (b) Program after the mapping procedure in which two operations, a SWAP and a SHUTTLE (in purple), have been inserted. (c) Overview of the mapping process: (i) qubit allocation in which the first two CNOTs can be directly performed. Note that they can be performed in parallel as the corresponding pair of qubits are allocated in distinct traps; (ii) qubit 2 needs to be moved to the trap on the right to continue execution. It requires adding a SWAP gate between qubits 2 and 3 to position the ion at the end of the trap to be (iii) shuttle to the adjacent trap. After these extra operations, the last two CNOT gates can be performed.
  • Figure 3: (a) Quantum circuit to be executed along with the initial structures; green dash lines illustrate the different time slices. The "R" structure contains the qubit ratio values, while the "T" structure holds information about the qubit pair interaction considering the time aspect. (b) Qubit interaction graph.
  • Figure 4: Example of the STA algorithm for a device consisting of two traps and 4 ions per trap, traps possess an excess capacity of two free spaces. (a) Steps involved in positioning qubits within traps. (b) Qubit relocation process.
  • Figure 5: Time execution for a 1D-linear array and a ring topology. Both devices consist of 6 traps, 17 ions per trap, and an initial excess capacity of 2. Benchmarks have 64 logical qubits.
  • ...and 2 more figures