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How to wire a 1000-qubit trapped ion quantum computer

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arxiv 2305.12773 v1 pith:UA3M5OYL submitted 2023-05-22 quant-ph

classification quant-ph
keywords quantumelectronicsqubitcomputerscontrolchipsignalwiring
verification ladder T0 review T1 audit T2 compute T3 formal

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One of the most formidable challenges of scaling up quantum computers is that of control signal delivery. Today's small-scale quantum computers typically connect each qubit to one or more separate external signal sources. This approach is not scalable due to the I/O limitations of the qubit chip, necessitating the integration of control electronics. However, it is no small feat to shrink control electronics into a small package that is compatible with qubit chip fabrication and operation constraints without sacrificing performance. This so-called "wiring challenge" is likely to impact the development of more powerful quantum computers even in the near term. In this paper, we address the wiring challenge of trapped-ion quantum computers. We describe a control architecture called WISE (Wiring using Integrated Switching Electronics), which significantly reduces the I/O requirements of ion trap quantum computing chips without compromising performance. Our method relies on judiciously integrating simple switching electronics into the ion trap chip - in a way that is compatible with its fabrication and operation constraints - while complex electronics remain external. To demonstrate its power, we describe how the WISE architecture can be used to operate a fully connected 1000-qubit trapped ion quantum computer using ~ 200 signal sources at a speed of ~ 40 - 2600 quantum gate layers per second.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. S-SYNC: Shuttle and Swap Co-Optimization in Quantum Charge-Coupled Devices

    quant-ph 2025-05 conditional novelty 6.0 of 10

    S-SYNC unifies shuttling and SWAP operations into a single 'generic swap' on a static graph, and a greedy heuristic co-optimizes them to cut shuttling by 3.69x and raise success rate by 1.73x on average in simulation.

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