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Equation 1 · Part 4 · Comparing the Main Approaches to Post-CMOS, Neuromorphic, Photonic, and Quantum AI Compute

Symbol m

Tclassical=O ⁣(poly(k)log⁡(mn))≈Tquantum,T_{\text{classical}} = O\!\left(\mathrm{poly}(k)\log(mn)\right) \approx T_{\text{quantum}},
mm

What this part means

m is an input to the expression that computes the quantity on the left.

Its job in the formula

m is an input to the expression that computes the quantity on the left.

The passage around this formula

…up to polynomial factors, by a classical algorithm nobody had previously constructed: Tclassical=O ⁣(poly(k)log⁡(mn))≈TquantumT_{\text{classical}} = O\!\left(\mathrm{poly}(k)\log(mn)\right) \approx T_{\text{quantum}}. where the classical algorithm samples from an ℓ2\ell^2 -norm-weighted data structure rather than reading the whole m ×\times n matrix, closing a gap previously measured only against a classical algorithm that read every entry [ 13 ] . The general lesson, sometimes called dequantization, is that an apparent exponential quantum speedup often measures the gap between a quantum algorithm and an unnecessarily weak…

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