Symbol m
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where the classical algorithm samples from an -norm-weighted data structure rather than reading the whole m 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 classical baseline, and the gap can close once someone finds a better classical algorithm.
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Symbol n
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Equation 3 · Future Hardware
Comparing the Main Approaches to Post-CMOS, Neuromorphic, Photonic, and Quantum AI Compute
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.
where the classical algorithm samples from an -norm-weighted data structure rather than reading the whole m 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 classical baseline, and the gap can close once someone finds a better classical algorithm.
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