Quantum-Inspired Geometric Classification with Correlation Group Structures and VQC Decision Modeling
We propose a geometry-driven quantum-inspired classification framework that integrates Correlation Group Structures (CGR), compact SWAP-test-based overlap estimation, and selective variational quantum decision modelling. Rather than directly approximating class posteriors, the method adopts a geometry-first paradigm in which samples are evaluated relative to class medoids using overlap-derived Euclidean-like and angular similarity channels. CGR organizes features into anchor-centered correlation neighbourhoods, generating nonlinear, correlation-weighted representations that enhance robustness in heterogeneous tabular spaces. These geometric signals are fused through a non-probabilistic ma...
| Authors | Nishikanta Mohanty, Arya Ansuman Priyadarshi, Bikash K. Behera et al. |
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| Primary category | quant-ph |
| Published | 2 April 2026 |
| Last updated at source | 9 April 2026 |
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