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Controlled comparison methodology showing continuous-variable (CV) QNNs outperform discrete-variable (DV) QNNs on spatial pattern recognition tasks like wafer-map defect classification.
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| name | cv-qnn-spatial-classification |
| description | Controlled comparison methodology showing continuous-variable (CV) QNNs outperform discrete-variable (DV) QNNs on spatial pattern recognition tasks like wafer-map defect classification. |
| category | quantum-ml |
| trigger_words | ["CV versus DV","continuous variable QNN","discrete variable QNN","wafer map classification","quantum spatial encoding","CV advantage","Fock cutoff"] |
Paper: arXiv:2607.00961v1 Authors: Yeonhong Kim, Jonghyeok Im, Monu Nath Baitha, Kyoungsik Kim
Under controlled conditions, CV-QNNs consistently outperform DV-QNNs on spatial pattern recognition tasks. At 4 qumodes/qubits, CV reaches 79.7% accuracy vs DV's 61.6% — an 18-point gap.
