Introduction
QHackathon Results
Equipo 15
Classical and quantum optimization for water stress scenarios in the Alto Atoyac basin.
Integrante 1Integrante 2Integrante 3Integrante 4
15
Introduction
Results with a quantum pulse
This page presents Team 15's results for the water allocation challenge. The core idea is to transform demand, availability and drought data into clear decisions: which source supplies each municipality, how much deficit remains and how performance changes as the scenario becomes more extreme.
Problem statement
Core problem
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Model goal
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Title, text and blocks
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Introduction
General context
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Core problem
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Model goal
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse potenti. Curabitur semper velit non sapien consequat, vitae luctus libero dictum.Drought signal
Puebla drought time series
Drought index over timePuebla
20032012201820212026
15-jun-24
Historical mean
15-mar-26
Map view
Puebla water network map
Cards extra 1/2
Blocks to tell the story
Problema
Asignacion inteligente de agua
Modelamos la distribucion entre fuentes, municipios y usos urbano/agricola bajo escenarios de sequia.Decision
Minimizar NWWD
La metrica penaliza el deficit normalizado, dando prioridad al consumo urbano sin perder visibilidad agricola.Comparativa
MILP vs QUBO
MILP entrega una referencia optima clasica; QUBO abre la puerta a annealing y algoritmos variacionales.Pipeline
CSV a modelo
Municipios, fuentes, escenarios y pesos se transforman en variables, restricciones y resultados normalizados.Riesgo
Sequia severa
El tablero deja ver que municipios absorben mas deficit cuando baja la disponibilidad efectiva.Siguiente
Escalar el demo
Al agregar mas municipios al JSON, las tarjetas se acomodan automaticamente sin tocar el componente.Cards extra 2/2
Blocks to tell the story
Metodologia
Ruta de trabajo
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Lectura principal
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Interpretacion
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Supuestos actuales
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Siguientes iteraciones
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Bloque reusable
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean viverra justo non augue ultrices.B.3 Expected Outputs
Base report per scenario
B.3 Expected Outputs
NWWD for each scenario
Report the NWWD value for Normal, Moderate drought and Severe drought to compare the social cost of deficit under each scenario.Allocation
Allocation decisions
Show which source supplies each municipality and at what volume. This turns the mathematical result into an operational decision.Validity
Feasibility
Indicate whether the model found a feasible solution and whether it satisfies capacity, demand balance and non-negativity.Performance
Runtime
Record execution time to compare computational cost between classical and quantum-ready formulations.QUBO
Number of binary variables
Report how many binary variables the QUBO formulation uses to estimate complexity and scalability.Decision
Allocation decisions detail
Break down allocation decisions by arc, urban/agricultural category and scenario to make the result auditable.B.3 Expected Outputs
Unmet demands
Urban demand
Unmet urban demand
Unmet urban volume in hm3. This is one of the most important signals because urban use is prioritized.Agricultural demand
Unmet agricultural demand
Unmet agricultural volume in hm3. It helps explain the scenario impact on production and rural use.Normalized urban
Normalized unmet urban demand
Urban deficit divided by total urban demand. It makes municipalities with different demand sizes comparable.Normalized agri
Normalized unmet agricultural demand
Agricultural deficit divided by total agricultural demand. It measures relative severity, not only absolute volume.Dashboards
MILP from JSON
MILP
MILP: classical optimal solution
NWWD2.526
Urban deficit24 hm3
Runtime26.13 ms
Scenario comparisonOptimal
91.58%urban
100%agricultural
Puebla
urban coverageSan Andres Cholula
urban coverageAtlixco
urban coverageDashboards
QUBO from JSON
QUBO
QUBO: quantum formulation
NWWD2.91
Urban deficit27.5 hm3
Runtime41.7 ms
QUBO energy-18.4
Scenario comparisonSampled
90.35%urban
96.73%agricultural
Puebla
urban coverageSan Andres Cholula
urban coverageAtlixco
urban coverageComparison
MILP vs QUBO
AspectMILPQUBO
Model type
Linear integer/continuous optimization with explicit constraints.
Binary quadratic function prepared for annealing or QAOA.
Variables
Continuous flows by source, municipality and demand category.
Binary variables that encode decisions and discretized levels.
Main output
Reference optimal solution for NWWD, deficits and allocations.
Sampled/approximate solution with energy and binary count.
Strength
Direct interpretation, stability and mathematical baseline.
Compatible with quantum hardware/heuristics and combinatorial search.
Risk
Runtime can grow if the model becomes larger or more integer-heavy.
Requires discretization, penalties and weight calibration.
Closing
References
Guided Challenge V3
Base challenge document for SDG 6.4 in the Alto Atoyac basin.
MILP implementation
PuLP/CBC model used to generate `milp_results.json`.
Benchmark CSV tables
Municipalities, water sources, drought scenarios, network and weights.
QUBO / Annealing notes
Study material on QUBO, Ising, simulated annealing and QAOA.