Quantum Annealing Hardware
Master specialized quantum systems designed for optimization problems
Your Progress
0 / 5 completed←
Previous Module
Neutral Atom Qubits
Introduction to Quantum Annealing
Quantum annealing is a specialized quantum computing approach designed to solve optimization problems. Unlike gate-based quantum computers, quantum annealers use quantum fluctuations to find the lowest energy state of a system, making them ideal for combinatorial optimization.
5000+
Qubits
15 mK
Temperature
<1 ms
Annealing Time
Why Quantum Annealing?
🎯
Optimization Focus
Built specifically for combinatorial problems
⚡
Fast Solutions
Sub-millisecond annealing times
🔗
Large Scale
Thousands of variables simultaneously
☁️
Cloud Access
Available via AWS and other platforms
How It Works
1
Problem Mapping
Optimization problem mapped to Ising model or QUBO formulation
2
Quantum State
System starts in high-energy quantum superposition state
3
Annealing
Quantum fluctuations gradually decrease over time (annealing schedule)
4
Solution
System settles into low-energy state representing the solution
Key Concepts
QUBO Formulation
Quadratic Unconstrained Binary Optimization—standard form for annealing problems
Ising Model
Physics model with spin variables representing problem structure and interactions
Quantum Tunneling
Quantum effect allowing system to escape local minima by tunneling through barriers
Problem Embedding
Mapping logical problem onto hardware topology—crucial for performance
Quantum vs Classical
Quantum Advantage
Quantum annealers use superposition and tunneling to explore multiple solution paths simultaneously.
Best for: Large combinatorial problems, finding global optima
Classical Limitation
Classical algorithms often get stuck in local minima and scale exponentially with problem size.
Challenge: Exponential time complexity for NP-hard problems