AI for data science & inferencing · Quantum for deep dive

Quantum Signal Wave & Geo Neural OS

Climate signals decomposed into frequency, amplitude, phase, wavelength and spectrum, with fidelity and coherence scores, then fed into geo-located y = f(x) models for farms, villages, towns and cities across India. Data is synthetic; fidelity and coherence are classical signal metrics borrowed from quantum notation, not qubit measurements.

Signal selection

360-day synthetic series · DFT
Raw NDVI (vegetation) (index) · dominant wave overlay below
Frequency
0.0028
cycles/day
Wavelength
360.0 d
λ = 1/f
Amplitude
0.220
index
Phase
85°
φ at t=0
Spectral fidelity
0.986
P_dom / ΣP
Spectral entropy
0.027
0 pure · 1 noise
Coherence
0.160
C_xy vs Rainfall
State fidelity
0.747
|⟨ψa|ψb⟩|²

Amplitude spectrum

|X(k)|, k = 1…40
Top modes: 360-day · 7-day · 5-day · 3-day. Seasonal (monsoon) cycle dominates.

Maths

X(k) = Σₜ x(t)·e^(−2πikt/N), A_k = 2|X(k)|/N, φ_k = arg X(k), f_k = k/N, λ = 1/f
x̂(t) = μ + A·cos(2πf t + φ)
Fidelity F = |X(k*)|² / Σ_k |X(k)|² · Entropy H = −Σ p_k log₂ p_k / log₂ K
Coherence C_xy(f) = |⟨S_xy⟩|² / (⟨S_xx⟩⟨S_yy⟩) · State fidelity |⟨ψ_a|ψ_b⟩|², ψ = A/‖A‖

AI insight: high coherence between rainfall and NDVI at the seasonal mode means rainfall forecasts can drive canopy and yield nudges; low coherence means a local driver (irrigation, pests) is decoupling them.