Overview
This page provides an overview of the diverse range of optimization algorithms available on Luna, each tailored to tackle complex optimization problems. Different algorithms may perform better depending on the specific use case or even the details of the problem instance, so Luna offers a wide selection to help you find the best fit for your needs.
Each algorithm includes key categories to help you navigate and select the best options for your projects:
- Short Name: The unique identifier for each algorithm in our SDK, used for quick reference and implementation.
- Algorithm Type: Indicates the role of quantum computing within the algorithm:
- Quantum: Operates exclusively on quantum hardware.
- Hybrid: Combines both quantum and classical processing.
- Classical: Runs solely on classical hardware, with no quantum components.
- Category: Defines how algorithms are deployed and which servers are involved, influencing usage within your Luna license. Categories are Quantum, Hybrid-Internal, Hybrid-External and Classical.
- Native Input Format: Indicates the algorithm's preferred input format. Luna will automatically convert any input provided in another format to the algorithm's native format, ensuring compatibility and seamless execution.
Algorithm Classes
Quantum Gate Model
Algorithms that use quantum gates to explore complex solution landscapes through quantum interference.
Quantum Annealing
Techniques leveraging quantum annealing to find optimal solutions in large, constrained spaces.
Optimization Solvers
Classical algorithms focused on solving large-scale, real-world optimization problems without quantum resources.
Simulated Annealing
Algorithms mimicking thermal processes to optimize large, complex problems.
Genetic Algorithms
Optimization methods inspired by natural selection, using strategies like trial and improvement to find the best solutions.
Search Algorithms
Structured searches with memory techniques to efficiently navigate complex solution spaces.