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Superconducting qubits in the millions: the potential and limitations of modularity

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arxiv 2406.06015 v3 pith:WVSYLF6K submitted 2024-06-10 quant-ph

classification quant-ph
keywords quantumphysicalqubitsftqcspotentialsuperconductingalgorithmsarchitectural
verification ladder T0 review T1 audit T2 compute T3 formal
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The development of fault-tolerant quantum computers (FTQCs) is receiving increasing attention within the quantum computing community. Like conventional digital computers, FTQCs, which utilize error correction and millions of physical qubits, have the potential to address some of humanity's grand challenges. However, accurate estimates of the tangible scale of future FTQCs, based on transparent assumptions, are uncommon. How many physical qubits are necessary to solve a practical problem intractable for classical hardware? What costs arise from distributing quantum computation across multiple machines? This paper presents an architectural model of a potential FTQC based on superconducting qubits, divided into discrete modules and interconnected via coherent links. We employ a resource estimation framework and software tool to assess the physical resources required to execute specific quantum algorithms compiled into their graph-state form and arranged onto a modular superconducting hardware architecture. Our tool can predict the size, power consumption, and execution time of these algorithms based on explicit assumptions about the system's physical layout, thermal load, and modular connectivity. We assess the resources needed for quantum computation examples that serve as building blocks of proposed applications, quantifying the architectural bottlenecks and trade-offs that remain to be addressed to deliver utility.

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

  1. Resource Estimation for Fault-Tolerant Quantum Programs

    quant-ph 2026-08 conditional novelty 6.0 of 10

    A quantum programming language with per-codeblock error-correction annotations and a compositional resource estimator that tracks space, time, and error rates through joint Pauli measurements and decoding latency.

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