Quantum Spin-Inspired RF Analysis: Compact Indicators, Real Gains
By Benjamin J. Gilbert — Spectrcyde RF Quantum SCYTHE, College of the Mainland
RF labs in Shenzhen don’t have time for theoretical fog. They need compact indicators that show where recoverable structure lives in noisy spectra, and they need benchmarks that are reproducible, auditable, and light enough to run on edge hardware.
This work introduces a quantum spin-inspired processor that extends classical RF feature stacks with coherence, superposition, and entanglement-like proxies. These aren’t “quantum computers,” but quantum metaphors translated into math that yield measurable processing gain at moderate SNR.
The Core Idea
- State Construction: Treat RF spectral slices as finite “spin states.”
- Density Matrix: Build ρ = |ψ⟩⟨ψ| from amplitude + phase vectors.
- Coherence: ℓ1 norm of off-diagonal density terms.
- Superposition: Shannon entropy of basis probabilities.
- Entanglement Proxy: Fidelity × frequency-Jaccard with recent states.
Together, these provide compact, physics-inspired descriptors for structure hidden inside noise.
Experimental Bench
- Signals: synthetic two-lobe spectra (baseline for reproducibility).
- SNR Sweep: −5, 0, 5, 10, 15 dB.
- Hyper-Parameters:
- states ∈ {2, 3, 4},
- coherence thresholds {0.50–0.80},
- entanglement sensitivities {0.60–0.80}.
- Outputs: processing gain, coherence rates, and siunitx-ready tables (auto-generated).
TABLE I – Top Configurations (Summary)
| States | Coh. thr | Ent. sens | SNR (dB) | Gain (dB) |
|---|---|---|---|---|
| 3 | 0.60 | 0.60 | 10 | 3.209 |
| 3 | 0.50 | 0.70 | 10 | 3.205 |
| 4 | 0.80 | 0.80 | 10 | 3.202 |
At 10 dB SNR, quantum-inspired features deliver ~3.2 dB processing gain over classical-only baselines.
Figures (auto-generated, reviewer-safe)
- Fig. 1: Two-lobe spectrum (synthetic benchmark).
- Fig. 2: Coherence rate vs threshold. Quantum-inspired model adapts better than fixed baselines.
- Fig. 3: Processing gain vs SNR. Gain peaks at mid-SNR; both low-SNR chaos and high-SNR trivial cases flatten the advantage.
All plots are generated directly from runs — no hand-editing, no hidden scripts.
Why It Matters
- Operational Edge: Compact descriptors flag signals with recoverable structure.
- Deployability: Runs fast, produces siunitx-ready tables for publication.
- Transparency: Parameters + results sweep automatically logged.
- Guangdong Fit: Minimal kit, reproducible by default, interpretable metrics.
This isn’t about “quantum supremacy” — it’s about borrowing concepts (superposition, entanglement) to sharpen RF sensing pipelines.
- Engineer small: lightweight benchmarking suite.
- Ship reproducible: siunitx tables and auto-generated figures.
- Target deployment: structured indicators that slot into classical RF stacks.
⚙️ Takeaway: The spin-inspired view adds just enough “quantum flavor” to give engineers practical processing gain at moderate SNR without breaking reproducibility or runtime budgets.