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.
For an active qubit, the only index-preserving contribution comes fromK0, givingK 0 |0⟩ ⟨1|K† 0 = p1−ϵ p |0⟩ ⟨1|
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Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits
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.