GPU-Accelerated Minor Embedding for Quantum Annealers
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Solution Overview
Problem
Current methods for embedding quadratic unconstrained binary optimization (QUBO) problems in quantum annealers are time-consuming and limited by poor quality embeddings, restricting the size and complexity of problems that can be solved.
Innovation Solution
The proposed method involves hardware-accelerated systems and methods for finding optimized minor embeddings using GPU thread blocks to efficiently configure quantum annealers, reducing embedding time and improving qubit usage by iteratively updating and evaluating graph embeddings based on evaluation ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If known techniques for minor embedding are used, then the quantum annealer can solve QUBO problems, but the time needed for embedding increases significantly
Solution Approach 1:
The embedding process is divided into multiple independent thread blocks that operate in parallel on different portions of the QUBO problem graph. Each thread block processes a subset of logical qubits and their corresponding physical qubit mappings independently, allowing simultaneous evaluation of multiple embedding configurations without sequential bottlenecks.
Solution Approach 2:
The patent transitions from traditional CPU-based sequential processing to GPU-based parallel processing, utilizing the dimensional advantage of massive parallel thread blocks. This dimensional shift in computational architecture enables thousands of embedding evaluations to occur simultaneously rather than sequentially, dramatically reducing total embedding time.
2Manufacturing precision
If known techniques for minor embedding are used, then embedding can be performed, but the quality of embedding is poor which limits problem size and complexity
Solution Approach 1:
The system implements an iterative feedback mechanism where each thread block continuously evaluates embedding quality metrics (such as chain length, qubit utilization, and graph minor properties) and adjusts its embedding strategy accordingly. Poor quality embeddings are identified and corrected through repeated evaluation cycles, progressively improving overall embedding quality.
Solution Approach 2:
The embedding process is made dynamic and adaptive rather than static. Thread blocks can modify their local embedding configurations in real-time based on global constraints and quality metrics, allowing the system to dynamically optimize embeddings for larger and more complex QUBO problems that were previously intractable.
3Quantity of substance
If more physical qubits are used for embedding, then larger QUBO problems can be solved, but qubit waste increases
Solution Approach 1:
The system dynamically adjusts embedding parameters such as chain length, qubit mapping density, and graph minor transformation strategies to optimize the ratio of useful to wasted qubits. By changing these parameters adaptively based on problem characteristics, the system maximizes the number of logical qubits that can be embedded while minimizing unused physical qubits.
Solution Approach 2:
Different regions of the QUBO problem graph are embedded with locally optimized quality metrics. Thread blocks adjust their local embedding strategies based on the specific density and connectivity requirements of different graph regions, ensuring that physical qubits are allocated efficiently where needed and minimizing waste in less demanding regions.
Data Source
AI summary
Methods for configuring a quantum annealer to solve a QUBO problem comprises receiving data representing an initial graph representing an embedding of a QUBO problem into a qubit architecture of the quantum annealer and causing one or more GPU thread blocks to create and store a best local current graph and update the best local current graph. Updating the best current local graph comprises copying the best local current graph, modifying the best local current graph copy to form a candidate local graph, computing an evaluation rating for the candidate local graph, and, in accordance with a determination that one or more replacement criteria are met, replacing the best local current graph with the candidate local graph. An updated best local current graph may be identified in a local results array as the best global graph. The quantum annealer may be configured based on the best local graph.