🎯 Quantum Optimization Problems
Solve complex combinatorial problems with quantum algorithms
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Drug Discovery with Quantum
🎯 The Optimization Challenge
Many real-world problems—from logistics to finance to drug design—boil down to combinatorial optimization: finding the best solution from an exponentially large search space. Quantum computers offer exponential speedups for these NP-hard problems through algorithms like QAOA and quantum annealing.
💡 Why Optimization Matters
Optimization problems are everywhere: routing delivery trucks (TSP), partitioning social networks (Max-Cut), scheduling flights, designing circuits, folding proteins. Classical algorithms struggle as problem size grows—quantum algorithms promise polynomial or even exponential advantages.
Global optimization market:$3.5 trillion/year
🎯 What You'll Master
🔄
QAOA Algorithm
Quantum Approximate Optimization
🧊
Quantum Annealing
D-Wave adiabatic approach
📊
Classic Problems
Max-Cut, TSP, SAT, Knapsack
🏭
Real Applications
Logistics, finance, ML training
⚡ Classical vs Quantum
🖥️Classical
Algorithm:Heuristics
Complexity:O(2n)
Solution:Approximate
Quality:80-90%
⚛️Quantum
Algorithm:QAOA/Annealing
Complexity:O(poly(n))
Solution:Near-optimal
Quality:95-99%
🎯 Classic Optimization Problems
Max-CutGraph
Network partitioning
NP-Complete
Traveling SalesmanRouting
Delivery optimization
NP-Hard
3-SATBoolean
Circuit verification
NP-Complete
KnapsackSelection
Resource allocation
NP-Complete