Portfolio showcasing projects and research. Zola 2026-09-04T00:00:00+00:00 https://blog.sooraj.fun/tags/pytorch/atom.xml 2026-09-04T00:00:00+00:00 2026-09-04T00:00:00+00:00 Unknown https://blog.sooraj.fun/projects/quanevac/ <p>Quantum-enhanced evacuation routing system integrating PyTorch LSTM flood prediction, Qiskit QAOA / IBM Quantum hardware, and OSRM real road mapping.</p> <h3 id="why-it-exists">Why it exists</h3> <p>Severe coastal cyclones disrupt critical transit networks within hours. Classical heuristic routers struggle to assign congestion-free escape routes when storm surges submerge arterial highways. Quanevac couples temporal deep learning flood forecasts with quantum combinatorial optimization.</p> <h3 id="how-it-works">How it works</h3> <ul> <li>Implements a <strong>PyTorch LSTM</strong> risk model predicting road segment inundation probabilities across 3 timesteps ($T=0\text{h}$, $T+12\text{h}$, $T+24\text{h}$).</li> <li>Formulates congestion-aware route assignment into a Quadratic Unconstrained Binary Optimization (QUBO) problem.</li> <li>Executes variational circuits using <strong>QAOA</strong> via Qiskit Aer simulators or real <strong>IBM Quantum</strong> hardware via `qiskit-ibm-runtime`.</li> <li>Traces real driving geometry via <strong>OSRM</strong> and visualizes evacuation corridors on dark-mode interactive maps with live storm track manipulation.</li> </ul> <h3 id="what-happened">What happened</h3> <p>Expanded from an early hackathon prototype into a full end-to-end framework featuring real highway polylines, classical baseline fallbacks, and real QPU dispatch. Handling quantum execution latency while keeping the map interactive required careful asynchronous worker queuing.</p> <h3 id="what-i-d-change">What I'd change</h3> <p>I'd ingest live Doppler radar weather telemetry directly into the LSTM tensor pipeline to trigger automatic dynamic re-routing during active storm landfall.</p> <h3 id="links">Links</h3> <p><a href="https://github.com/whysooraj/Quanevac">GitHub</a></p>