How roaimkhan/qiskit_hackathon works
This is a simulated feeder load-shedding dashboard backed by a FastAPI dispatch service. The user views transformer topology, sets a shedding target, chooses a solver, and receives transformer states and optimization metrics; the dashboard also exposes an emergency simulated feeder trip. The backend offers QAOA, classical MILP, and exact branch-and-bound dispatch. The README describes broader interactive and diagnostic ambitions, but sampled UI excerpts do not establish all of those features as implemented. Some UI-to-API wiring is therefore shown as unverified.
Main entry points: endpoints.py HTTP dispatch API · App.jsx Dispatch dashboard · main.jsx React entry · quantum_solver.py QAOA dispatch
Components 8
Dispatch dashboard
- Dispatch dashboard
QUANTAGRID/src/App.jsx - React entry
QUANTAGRID/src/main.jsx
Dispatch API
- HTTP dispatch API
endpoints.py
Optimization engines
- QAOA dispatch
quantum_solver.py - MILP dispatch
classical_solver.py - Exact dispatch
exact_algorithm.py
Feeder model
- Feeder topology
main.py
Other components
- Grid operator
Connections 8
- Grid operator → Dispatch dashboard: uses
QUANTAGRID/src/App.jsx - React entry → Dispatch dashboard: renders
QUANTAGRID/src/main.jsx - Dispatch dashboard → HTTP dispatch API: requests dispatch
- HTTP dispatch API → QAOA dispatch: invokes
endpoints.py - HTTP dispatch API → MILP dispatch: invokes
endpoints.py - HTTP dispatch API → Exact dispatch: invokes
endpoints.py - HTTP dispatch API → Feeder topology: loads topology
endpoints.py - HTTP dispatch API → Dispatch dashboard: returns states