Precomputed Embedding Library for Quantum Annealer Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Determining optimal embeddings for quantum annealers is computationally difficult and time-consuming, as it requires significant computational effort on classical computers, which can be inefficient given the NP-hard nature of the problem.
Innovation Solution
A library of precomputed optimized embeddings for clique graphs of varying sizes is created and stored, allowing for quick retrieval and modification to generate problem-specific embeddings for quantum annealers, reducing the computational burden on classical computers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optimal embeddings are computed using conventional algorithms on classical computers, then embedding accuracy is improved, but computational time increases significantly
Solution Approach 1:
The patent precomputes and stores optimal embeddings for clique graphs of various sizes in a library before they are needed. When a quantum annealing problem requires an embedding, the system retrieves the precomputed embedding from the library rather than computing it in real-time. This preliminary action resolves the contradiction by providing accurate embeddings (maintaining embedding accuracy) while eliminating the computational time requirement at the moment of use.
Solution Approach 2:
The patent creates copies of precomputed clique graph embeddings and stores them in a library for reuse. Instead of recomputing embeddings each time they are needed, the system copies and retrieves stored embeddings that match the problem requirements. This copying approach maintains embedding accuracy through reuse of proven embeddings while significantly reducing computational time by avoiding redundant calculations.
2Speed
If precomputed embeddings are stored in a library for quick retrieval, then computational speed is improved, but memory storage requirements increase
Solution Approach 1:
The patent organizes the embedding library by parameters such as clique graph size and topology type. Embeddings are stored with indexed parameters that allow efficient retrieval based on problem characteristics. This parameter-based organization enables the system to quickly locate and retrieve only the specific embeddings needed for particular problem sizes, improving computational speed while optimizing storage space by avoiding unnecessary embeddings.
3Adaptability or versatility
If a comprehensive library of embeddings for all possible clique sizes is precomputed, then adaptability to different problem sizes is improved, but the complexity of library management increases
Solution Approach 1:
The patent segments the embedding library into distinct sections or categories based on clique graph parameters such as size, topology, and other characteristics. Each segment contains embeddings for specific problem types and sizes. This segmentation improves adaptability by making it easy to select the appropriate segment for a given problem while reducing library management complexity through organized, modular structure that simplifies maintenance and updates.
Data Source
Figure 1
Figure 2A~2B
Figure 3A~3B
AI summary
Computation of optimal embeddings for an optimization problem to be solved using a quantum annealer can be accelerated by pre-computing optimal embeddings in a target graph corresponding to a quantum processor architecture of clique graphs (or fully-connected network graphs) having various numbers of nodes. Pre-computed embeddings can be stored in a library. To solve an optimization problem, a clique-graph embedding of appropriate size can be retrieved from the library and modified to match the problem to be solved. The modified embedding can be executed using an appropriate quantum annealer.