Embedding Cache for Quantum Graph Mapping Bottlenecks
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Solution Overview
Problem
The embedding process for quantum computations is computationally intensive and time-consuming due to the limitations of current quantum hardware, which lacks full connectivity between qubits, making it difficult to map logical variables onto qubits efficiently, especially when dealing with problems that require heuristic methods and potential multiple qubit chaining.
Innovation Solution
Implementing a caching system that stores problem embeddings and uses hash-based or similarity-based queries to retrieve or generate embeddings for new problems with similar graph structures, reducing the computational expense by leveraging existing embeddings for structurally equivalent or similar problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If heuristic methods are used to handle the computationally intensive embedding process, then the embedding can be generated, but the time required increases significantly
Solution Approach 1:
The system pre-computes and stores embeddings for common graph structures in a cache database. When a new problem arrives, the system first checks if an embedding exists for the corresponding graph structure before performing the computationally intensive embedding computation, thereby avoiding redundant calculations and reducing overall processing time.
Solution Approach 2:
The patent creates a cache database that stores pre-computed embeddings as reusable copies. When a new problem with a known graph structure is encountered, the system retrieves the existing embedding copy from the database rather than regenerating it, significantly reducing the time and computational resources required.
2Ease of operation
If full connectivity between all qubits is achieved, then mapping logical variables onto qubits becomes easier, but the quantum hardware complexity increases
Solution Approach 1:
The patent introduces a classical computer system as an intermediary that performs the embedding computation before the problem is sent to the quantum hardware. This intermediary handles the complexity of mapping logical variables to physical qubits by using pre-computed embeddings for common graph structures, allowing the quantum hardware to focus solely on execution without bearing the burden of embedding computation.
3Adaptability or versatility
If multiple qubit chaining is implemented to handle limited connectivity, then embeddings can be found, but the computational difficulty increases
Solution Approach 1:
The system pre-computes embeddings for common graph structures including those requiring qubit chaining, and stores them in the cache database. When a new problem with similar structure arrives, the system retrieves the pre-computed embedding that already accounts for the connectivity constraints, avoiding the need to perform complex chaining computations in real-time.
Data Source
AI summary
In an embedding caching system, embeddings generated from previous problems are re-used to improve performance on future problems. A data structure stores problems and their corresponding embeddings. When computing future embeddings, this data structure can be queried to determine whether an embedding has already been computed for a problem with the same structure. If it has, the embedding can be retrieved from the data structure, saving the time and computational expense of generating a new embedding. In one variation, the query is not based on exact matches. If a new problem is similar in structure to previous problems, those embeddings may be used to accelerate the generating of an embedding for the new problem, even if they cannot be used directly to embed the new problem.