Cognition Layer · AI-QM v0.1 · DNN + CNN + VQE + QPE

AI-Quantum Microbiology Model for Climate, Soil, Water, Blood, Fodder & Livelihood

Classical Deep / Convolutional Neural Networks fused with Variational Quantum Eigensolvers and Quantum Phase Estimation — simulating microbial metabolic states at a quantum-native level across nine agri domains.

AI-Quantum Advisory

Do this today

Choose your crop and growth stage — the ranking and wording adapt to your field context.

0 critical0 important0 routine
Move any slider in the simulators below to generate recommendations for your Rice / Paddy at Vegetative growth.

1 · Unified AI-Quantum Microbiology Architecture

Multi-modal sensors → Classical AI + Quantum-Native → Hybrid Graph RAG → Domain Replicators
Multi-Modal Inputs
Soil probes · Blood labs · Fodder NIR · Hyperspectral imaging · MS · Optical spectroscopy
Classical AI Layer
CNN cell-counter · LSTM mutator · ResNet-50 pathogen classifier
Quantum-Native Layer
VQE energy states · QPE metabolic sim · VQC antibody-antigen
Hybrid Graph-RAG Node
Parameterised quantum-classical fusion → 9 domain replicators

Molecular Hamiltonian for Microbial Metabolic States

Mapping electronic/atomic structure to parameterised quantum circuits
H_microbe = Σ h_pq · a†_p · a_q + ½ Σ h_pqrs · a†_p · a†_q · a_s · a_r
  • • a†_p, a_q — fermion creation / annihilation operators across mapped molecular orbitals of the microbe.
  • • h_pq, h_pqrs — single- and double-electron integral matrices derived from real-time mass spectrometry, NIR optical spectroscopy, or field lab tests.
  • • Ground-state energy E₀ recovered via VQE on a 6–12 qubit parameterised ansatz; metabolic phase angles read by QPE.

2 · Domain Simulators

Live · in-browser quantum-classical hybrid
Domain i

Soil Microbiology — Rhizosphere N-Fixation & Carbon Capture

AI layer · CNN+LSTM analyses spatio-temporal EC/pH/VWC → predicts Rhizobium & Azotobacter colony growth.
Quantum layer · VQE simulates the FeMo-cofactor active site of nitrogenase → N₂ → NH₃ conversion energy under field conditions.
Microbial growth index0.1135
Adjusted population6.01 ×10⁶ CFU/g
Quantum enzyme efficiency η_Q0.960
N-fixation rate2.60 kg N · ha⁻¹ · d⁻¹
Domain ii

Water Microbiology — Pathogen & Eutrophication Tracking

AI layer · CNN classifies algal-bloom morphology and E. coli / Salmonella colonies from inline microscope cameras.
Quantum layer · Quantum-Walk algorithm models pathogen dispersion through canals using elevation, flow & rainfall runoff.
Pathogen dispersion coefficient1.79
E. coli risk index52 / 100
Algal bloom risk72 / 100
Quantum-walk reach P(t)0.449
Domain iii

Livestock Blood Microbiology — Hematology & Pathogen Vector

AI layer · ResNet CNN automates blood-smear cell counts; detects Theileria, Babesia, Brucella signatures.
Quantum layer · Variational Quantum Classifier (VQC) models antibody-antigen binding kinetics for edge diagnostics.
Infection probability56 %
Anemia severity index20 / 100
VQC binding confidence0.806
Recommended actionMonitor + supplement
Domain iv

Fodder & Crop Lifecycle — Mycotoxin Mitigation Engine

AI layer · NIR spectral CNN flags Aspergillus flavus signatures and quantifies aflatoxin in ppb pre-feeding.
Quantum layer · QPE simulates atomic-step degradation of mycotoxins under bio-neutralising enzymes.
Predicted aflatoxin B1220.0 ppb
Fungal load8640 CFU/g
QPE enzyme degradation η0.350
Feed-safety verdictReject batch
Domain v

Farm Scale Analysis & Climate-Change Resiliency Index (CC_ri)

AI layer · LSTM ingests cumulative GDD, ET₀ and multi-spectral NDVI to flag microclimate shifts.
Quantum layer · Hybrid QAOA projects long-term soil microbiome drift under warming trends.
CC_ri Resiliency Index100.0 / 100
Microbiome shift Δ0.096
Projected yield delta+0.4 %
Adaptation cueMaintain rotation
Domain vi

Fertiliser — AI-QM Precision N Optimisation

AI layer · LSTM forecasts daily biological N supply from rhizosphere; recommends top-dressing windows.
Quantum layer · VQE-derived enzyme efficiency tunes synthetic N down to the metabolic floor.
Required synthetic N0 kg/ha
Saved vs blanket dose120 kg/ha (100 %)
₹ saved per ha₹ 3360
CO₂e avoided504 kg CO₂e/ha
Domain vii

Livestock — Herd-Level Microbial Threat Modelling

AI layer · CNN-on-edge analyses dung, milk-conductivity & thermal-image cues for early subclinical mastitis / FMD.
Quantum layer · VQE energy landscape of viral spike-protein binding sites accelerates vaccine-strain ranking.
Outbreak risk48 / 100
Milk volume at risk12 L/day
ROI of early intervention₹ 0 /day
Domain viii

Farmer Livelihood — Net Income Uplift Simulator

AI layer · Tabular DNN couples yield, market, weather & credit signals into a farm P&L.
Quantum layer · QAOA portfolio optimiser allocates crop / livestock mix per agro-climatic zone.
Baseline net income₹ 136780
AI-QM net income₹ 179500
Income uplift+31 %
Domain ix

Climate Change — Quantum Signal-Wave Decomposition

AI layer · Transformer ingests 30-yr IMD reanalysis + Sentinel-2 NDVI to infer regime probabilities.
Quantum layer · ψ(t) = α|stable⟩ + β|volatile⟩ + γ|collapse⟩ — wave collapse picks the agronomy playbook.
P(stable)59.0%
P(volatile)30.1%
P(collapse)10.8%
Wave Collapse → Climate Verdict
CLIMATE STABLE
Maintain rotation; advance carbon-credit MRV baseline

3 · Operational Benefits

Measurable structural improvements across farm management and research
Predictive Diagnostics
2–3 wk
Detect pathogen threats 2–3 weeks before visible crop or livestock symptoms.
Precision Fertiliser
−18 to −24 %
Quantum-tuned N application lowers synthetic input cost.
Bio-Product R&D
10× faster
VQE + GraphRAG compresses biopesticide screening from years to weeks.
Climate Resiliency
+CC_ri 28 pts
Microbiome-drift forecasting drives rotation, variety & risk decisions.
Livestock Health
<5 min
Edge VQC blood-smear triage cuts diagnostic turnaround.
Carbon MRV
≥250 tCO₂e Y1
Quantum-validated SOC + N₂O fluxes unlock verifiable credits.

4 · Knowledge-Graph RAG for Bio-Agronomic Discovery

Neo4j property graph linking fields, microbes, pathogens, treatments & markets
(:Field) ──[:CONTAINED_SOIL]──> (:SoilProfile) ──[:HOSTS_MICROBE]──> (:MicrobialSpecies)
                                                                       │
                                                                 [:METABOLIZED]
                                                                       ▼
(:FertilizerLog) <─[:MITIGATED_BY]─ (:PathogenStress) <─[:INFECTS]─ (:ChemicalCompound)
Cross-domain Cypher pattern
MATCH (v:Village {name:"Sonewadi"})<-[:LOCATED_IN]-(f:Field)
      -[:CURRENT_SEASON]->(c:CropCycle {primary_crop:"Onion"})
MATCH (c)-[:LOGGED_MICROBIAL_SAMPLE]->(m:MicrobialSpecies {pathogenicity_flag:true})
MATCH (m)-[:TARGET_TREATMENT]->(t:TreatmentProtocol)
WHERE f.soil_type_sensor_derived = "Black" AND c.weather_trend_vpd > 2.5
RETURN f.field_id, m.species_name, t.recommended_biopesticide_dose, f.total_area_acres
ORDER BY f.total_area_acres DESC LIMIT 10;
© 2026 AIOTF Model for AgriSensee Intelligence Pune · AI-Quantum Microbiology v0.1