Equation 15 · From Origins to Frontier: A History of Post-CMOS, Neuromorphic, Photonic, and Quantum AI Compute
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which decays exponentially in circuit depth for any fixed error rate > 0 . This is precisely the noise floor Preskill’s paper is about, and it is why serious NISQ-era quantum machine learning proposals are built around short, shallow circuits matched to a specific problem rather than long, general-purpose programs — the strategy quantum computing borrowed, whether its practitioners framed it this way or not, from the same lesson optical computing learned two decades earlier: work within what the noisy substrate actually does well, rather than against it.
Sources cited in the article section
- [6] Quantum Algorithm for Linear Systems of Equations ↗
- [8] Quantum Machine Learning ↗
- [7] Quantum Computing in the NISQ Era and Beyond ↗
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