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Scaling Superconducting Quantum Computers with Chiplet Architectures

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arxiv 2210.10921 v1 pith:LEZJRM4X submitted 2022-10-19 quant-ph

classification quant-ph
keywords quantumqubitstransmongatescalingvariationbuildingcomputers
verification ladder T0 review T1 audit T2 compute T3 formal
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Fixed-frequency transmon quantum computers (QCs) have advanced in coherence times, addressability, and gate fidelities. Unfortunately, these devices are restricted by the number of on-chip qubits, capping processing power and slowing progress toward fault-tolerance. Although emerging transmon devices feature over 100 qubits, building QCs large enough for meaningful demonstrations of quantum advantage requires overcoming many design challenges. For example, today's transmon qubits suffer from significant variation due to limited precision in fabrication. As a result, barring significant improvements in current fabrication techniques, scaling QCs by building ever larger individual chips with more qubits is hampered by device variation. Severe device variation that degrades QC performance is referred to as a defect. Here, we focus on a specific defect known as a frequency collision. When transmon frequencies collide, their difference falls within a range that limits two-qubit gate fidelity. Frequency collisions occur with greater probability on larger QCs, causing collision-free yields to decline as the number of on-chip qubits increases. As a solution, we propose exploiting the higher yields associated with smaller QCs by integrating quantum chiplets within quantum multi-chip modules (MCMs). Yield, gate performance, and application-based analysis show the feasibility of QC scaling through modularity.

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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. Enhancing the Clique Local Decoder to Correct Length-2 Space Errors in the Surface Code

    quant-ph 2025-07 conditional novelty 5.0 of 10

    A modest extension of the Clique decoder, Clique_L2, corrects length-2 space error chains locally and cuts out-of-fridge decoding bandwidth by up to 18.38x under clustered noise models.

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