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The model assigns to each sample a score s(x) =w ⊤x+β 0,(H14) wherewis the vector of feature weights andβ0 is an intercept parameter

Classifier To convert the shell summaries into a binary prediction, we use a regularized logistic classifier acting on the feature vectorx∈R d

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Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits

quant-ph · 2026-05-20 · conditional · novelty 6.0

Simulations of a known quantum learning task demonstrate clear performance separation between coherent noisy quantum processing and fixed-measurement classical strategies at 30-40 qubits, with data acquisition as the primary bottleneck.

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  • Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits quant-ph · 2026-05-20 · conditional · none · ref 30

    Simulations of a known quantum learning task demonstrate clear performance separation between coherent noisy quantum processing and fixed-measurement classical strategies at 30-40 qubits, with data acquisition as the primary bottleneck.