Quantum-IoT Urban Heat Island Framework
Budget: ₹600 – ₹1,500 INR
The goal is to create an end-to-end framework that marries an IoT sensing network with quantum machine learning so I can track Urban Heat Island effects in real time and react before temperatures spike. The heart of the work is the quantum model itself, so I need the Qiskit and PennyLane stacks woven seamlessly into a classical pipeline.
Sensor data will stream from temperature, humidity, CO₂, and particulate-matter probes positioned around a city block–level testbed. That raw feed must be captured, cleaned, time-synced, and formatted for rapid hand-off to the variational quantum model.
Key deliverables
• Architecture diagram of the edge-to-cloud sensor network, including communication protocols and security touchpoints
• Python-based acquisition and preprocessing code (MQTT/LoRaWAN ingestion through to a Pandas-ready dataset)
• Variational quantum circuit(s) implemented in both Qiskit and PennyLane, wrapped so classical pre- and post-processing can call them interchangeably
• Hybrid loop tying classical optimizers to the quantum backend, plus fall-back simulation for when real hardware is unavailable
• Performance report with RMSE, MAE, and R² benchmarks against a baseline classical model
• Publication-quality figures: model schematic, loss-curve plots, and a system framework diagram in SVG/PDF
Acceptance criteria
1. End-to-end run on a sample 24-hour dataset completes in under 30 minutes on an 8-qubit simulator.
2. Quantum model beats the classical baseline by at least 5 % on RMSE.
3. All code is reproducible via a single requirements.txt and clearly commented Jupyter notebooks.
If this aligns with your expertise, let’s get the quantum bits humming and cool our cities more intelligently.
Sensor data will stream from temperature, humidity, CO₂, and particulate-matter probes positioned around a city block–level testbed. That raw feed must be captured, cleaned, time-synced, and formatted for rapid hand-off to the variational quantum model.
Key deliverables
• Architecture diagram of the edge-to-cloud sensor network, including communication protocols and security touchpoints
• Python-based acquisition and preprocessing code (MQTT/LoRaWAN ingestion through to a Pandas-ready dataset)
• Variational quantum circuit(s) implemented in both Qiskit and PennyLane, wrapped so classical pre- and post-processing can call them interchangeably
• Hybrid loop tying classical optimizers to the quantum backend, plus fall-back simulation for when real hardware is unavailable
• Performance report with RMSE, MAE, and R² benchmarks against a baseline classical model
• Publication-quality figures: model schematic, loss-curve plots, and a system framework diagram in SVG/PDF
Acceptance criteria
1. End-to-end run on a sample 24-hour dataset completes in under 30 minutes on an 8-qubit simulator.
2. Quantum model beats the classical baseline by at least 5 % on RMSE.
3. All code is reproducible via a single requirements.txt and clearly commented Jupyter notebooks.
If this aligns with your expertise, let’s get the quantum bits humming and cool our cities more intelligently.