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.