Quantum Processor Problem Decomposition via Hardware Graph Mapping
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Solution Overview
Problem
Quantum processors face limitations in solving problems with a large number of variables and complex interactions due to their fixed architecture, which restricts the size and complexity of problems that can be addressed, and the direct mapping approach is impractical for many computational problems, especially those with higher-order interactions.
Innovation Solution
The method involves decomposing large problems into sub-problems that fit within the quantum processor's architecture using hardware-specific graph decompositions and local searches, allowing for the recombination of solutions to address the original problem, and employing a sampling approach to program the quantum processor, which is less dependent on the processor's architecture and can handle a broader range of problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If direct mapping approach is used to solve problems on quantum processor, then the solution is straightforward for simple problems, but it becomes impractical for problems with large number of variables and complex interactions due to fixed architecture limitations
Solution Approach 1:
The patent applies segmentation by dividing a large problem into multiple smaller sub-problems that can be solved independently on the quantum processor. The problem decomposition module breaks down the original problem instance into sub-instance(s) that fit within the quantum processor's architectural constraints, allowing each sub-problem to be mapped directly to available qubits and couplers without exceeding connectivity limits.
Solution Approach 2:
The patent transitions from direct mapping in one dimension (single problem instance) to multiple dimensions by creating a hierarchy of problem instances. The system moves between different levels of abstraction: original problem → decomposed sub-problems → quantum processor mapping → solution recombination. This dimensional approach allows handling of larger problems by distributing them across multiple processing iterations.
2Adaptability or versatility
If hardware-specific graph decomposition is used to break down large problems, then problems exceeding processor capabilities can be solved, but the programming process becomes more complex
Solution Approach 1:
The patent applies preliminary action by performing problem decomposition before quantum processing. The classical computer pre-processes the problem instance, breaking it into sub-problems and determining their mapping to quantum processor components. This preliminary structuring eliminates the need for complex real-time decomposition during quantum execution, simplifying the actual quantum programming while maintaining the ability to handle large problems.
Solution Approach 2:
The patent introduces an intermediary problem decomposition module that acts as a bridge between the classical problem formulation and quantum processor execution. This intermediary layer handles the complex graph decomposition and mapping tasks, translating high-level problem descriptions into quantum processor-specific instructions. By isolating complexity in this intermediary layer, the core quantum processing remains relatively simple while still achieving high adaptability.
3Adaptability or versatility
If sampling approach is used instead of direct mapping, then broader range of problems can be handled with less architectural dependence, but the solution requires more sophisticated problem formulation
Solution Approach 1:
The patent applies universality by implementing a sampling-based quantum processor programming approach that can handle multiple types of problems beyond what direct mapping supports. The system formulates problems in a unified sampling framework that works across different problem domains and quantum processor architectures, making the approach universally applicable. The problem formulation module translates diverse problem types into a common sampling-based representation that the quantum processor can handle regardless of specific architectural constraints.
Data Source
AI summary
Systems and methods formulate problems for solving by a quantum processor using hardware graph decomposition. A decomposition of a primal graph may be built in a first stage based on a hardware specific graph, and refined in a second stage by, for example, removing vertices from the decomposition. The hardware specific graph may be a graph that is specific to a piece of hardware, for instance a quantum processor comprising a plurality of qubits and couplers operable to communicatively couple pairs of qubits.