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 X- andY-terms map|0⟩ ⟨1|to|1⟩ ⟨0|(feeding terms that average to zero), while theZ-term contributes a minus sign
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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.