Quantum-IoT Urban Heat Island Framework

₹600-1500 INR

Complété

Publié il y a environ 1 mois

₹600-1500 INR

Payé lors de la livraison

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 [login to view URL] and clearly commented Jupyter notebooks. If this aligns with your expertise, let’s get the quantum bits humming and cool our cities more intelligently.

Matlab and Mathematica

Software Architecture

N° de projet : 40268368

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6 propositions

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Actif à il y a 1 mois

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@InjectAI

Hi, Your IoT + Quantum ML Urban Heat Island framework aligns well with my AI and advanced modeling experience. I can design a clean end-to-end pipeline that captures live sensor data, preprocesses it efficiently, and feeds it into a hybrid quantum-classical learning system built with both Qiskit and PennyLane. I will architect the edge-to-cloud flow (MQTT/LoRaWAN ingestion, secure transmission, preprocessing, time-syncing, feature engineering) and implement interchangeable variational quantum circuits in Qiskit and PennyLane. A hybrid loop will connect classical optimizers to the quantum backend, with simulator fallback to ensure reproducibility and performance stability. The system will be modular, documented, and reproducible via a single [login to view URL] and structured Jupyter notebooks. I will benchmark against a strong classical baseline and report RMSE, MAE, and R² to verify the required ≥5% RMSE improvement target. .......... Deliverables .......... • Edge-to-cloud architecture diagram (with protocols & security) • Python ingestion & preprocessing pipeline • Variational quantum circuits (Qiskit + PennyLane) • Hybrid optimization loop with simulator fallback • Performance report + publication-quality figures (SVG/PDF) .......... Tech Stack .......... • Python, Pandas, NumPy • MQTT / LoRaWAN • Qiskit & PennyLane • SciPy optimizers • Matplotlib / Jupyter Visit my profile for similar AI and research-grade systems. Ready to start immediately.

₹600 INR en 7 jours

5,0

(2 commentaires)

1,0

6 freelances proposent en moyenne ₹958 INR pour ce travail

@divyak866

I am experienced in Python-based data pipelines and machine learning workflows, and I have hands-on exposure to quantum computing frameworks including Qiskit and PennyLane. I can design an end-to-end hybrid classical-quantum pipeline integrating IoT data ingestion, preprocessing, and variational quantum circuits with reproducible benchmarking against classical baselines. The solution will include a modular architecture, simulator fallback, performance metrics (RMSE, MAE, R²), and clearly documented Jupyter notebooks for seamless execution.

₹1 300 INR en 7 jours

0,0

(0 commentaires)

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@hennaz1

I am a perfect fit for your project that requires a clean, professional, and seamless integration of IoT sensing with quantum machine learning. Your need for an end-to-end framework capturing sensor data, preprocessing it for variational quantum circuits in Qiskit and PennyLane, and tying classical optimizers to quantum backends reflects a complex yet fascinating challenge. With strong Python expertise, experience in MQTT/LoRaWAN ingestion, data preprocessing, and building hybrid quantum-classical pipelines, I can deliver automated, user-friendly solutions. While I am new to freelancer, I have tons of experience and have done other projects off site. I would love to chat more about your project! im willing to do it for less money but still best quality in exchange for a good review Regards, Henrux Faurie

₹1 150 INR en 14 jours

0,0

(0 commentaires)

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@sanemm1

I help businesses turn raw data into meaningful insights and predictive solutions. Skilled in data cleaning, EDA, machine learning model development, and data visualization. Experienced with Python (Pandas, NumPy, Scikit-learn), SQL, and dashboard tools. Focused on delivering accurate analysis, reliable models, and clear, data-driven results.

₹1 050 INR en 10 jours