True multi-objective optimization with Pareto fronts. Self-tuning quantum-inspired algorithms that find trade-off solutions traditional optimizers miss.
Traditional optimizers force you to choose one objective. QuantumEdge explores the entire trade-off space.
Get 20-100+ optimal solutions representing all trade-offs between objectives. No artificial weighting required.
Quantum-inspired adaptive algorithms that automatically adjust exploration vs exploitation based on problem structure.
Novel quantum-inspired operators that maintain solution diversity and prevent premature convergence.
Maximizes Pareto front coverage with built-in hypervolume indicator tracking and convergence guarantees.
Quantum superposition principles enable parallel exploration, reaching optimal fronts faster than NSGA-II or MOEA/D.
Simple API compatible with existing optimization workflows. Define objectives, get Pareto solutions.
QuantumEdge excels when you need to understand trade-offs, not just find "the best" answer.
Optimize for return, risk, and ESG scores simultaneously. Get the full efficient frontier, not just one portfolio.
Balance cost, quality, and throughput. See exactly how much quality you sacrifice for each dollar saved.
Multi-physics optimization: minimize weight, maximize strength, reduce cost. Explore the design space completely.
Optimize delivery time, cost, and carbon footprint together. Make informed decisions with full visibility.
Define your objectives, set bounds, and get Pareto-optimal solutions. QuantumEdge handles the complexity.
import thalosforge as tf
# Define multiple objectives
def cost(x):
return x[0]**2 + x[1]**2
def quality(x):
return (x[0]-1)**2 + (x[1]-1)**2
# Run QuantumEdge
result = tf.quantumedge.optimize(
objectives=[cost, quality],
bounds=[(-5, 5), (-5, 5)],
n_solutions=50
)
# Get Pareto-optimal solutions
for solution in result.pareto_front:
print(f"Cost: {solution.objectives[0]:.3f}")
print(f"Quality: {solution.objectives[1]:.3f}")
print(f"Variables: {solution.x}")
print("---")
# Hypervolume indicator
print(f"Hypervolume: {result.hypervolume:.4f}")See the full trade-off space. Make informed decisions.