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Equation 1 · Part 9 · Comparing the Main Approaches to Post-CMOS, Neuromorphic, Photonic, and Quantum AI Compute

subscript

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

What this part means

The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.

Its job in the formula

A subscript distinguishes a version, component, step, or member of a quantity. It does not automatically mean multiplication.

The passage around this formula

For quantum algorithms proposed to accelerate machine learning on ordinary classical data — the sense in which “quantum AI” is usually marketed — the evidence points the other way on two grounds. The first is trainability: McClean and colleagues showed in 2018 that for a wide class of the parameterized quantum circuits used in most proposed quantum machine learning models, the probability that a gradient in any direction is non-negligible shrinks exponentially with qubit count — a “barren plateau” that makes gradient-based training intractable at exactly the scale where an advantage would need to appear [ 12 ] . The second is the comparison baseline itself. In 2018, Ewin Tang showed that a…

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Learn the underlying idea

A subscript is a label attached below a symbol. It often selects a time step, component, category, or member of a sequence.

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Sources cited in the surrounding passage

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