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Finals hardening - #2
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…es the failed Qiskit VQC The Quant-A-thon primer teaches both Qiskit and PennyLane and points to PennyLane for QML (differentiable circuits). The project was Qiskit-only, and its Qiskit VQC (gradient-free COBYLA/SPSA) underperformed (ROC-AUC 0.50-0.63, near random). radarsense/vqc_pennylane.py trains the same ansatz (AngleEmbedding + StronglyEntanglingLayers + expval) with true GRADIENT DESCENT (Adam), same 4 features / data / seeds. Result: ROC-AUC 0.979 +/- 0.004, PR-AUC 0.924 -- competitive with classical (0.979 / 0.939). So the earlier VQC failure was an optimizer problem, not a fundamental one -- exactly the primer's slide-12 point. Honest: this does NOT overturn the no-quantum-advantage conclusion (classical still edges the VQC); it turns a broken VQC into a working one. Device-agnostic verified (slide 14): identical output on default.qubit and lightning.qubit; qiskit.aer/IBM is a one-line device swap. Both SDKs now used.
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