Quantum Annealer Orchestration Service for Combinatorial Optimization
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Solution Overview
Problem
Current quantum annealing systems face limitations in solving combinatorial optimization problems due to qubit connectivity, availability, and hardware constraints, making it difficult to select the most suitable system for efficient problem-solving.
Innovation Solution
An orchestration service that uses a classification machine learning model to generate an ordered list of quantum annealing systems based on their compatibility and relevance to solve combinatorial optimization problems, allowing for automatic and informed selection of the most suitable system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum annealing technologies are employed to solve combinatorial optimization problems, then solution quality and computational efficiency are improved, but hardware constraints (qubit availability, connectivity, memory throughput) limit the scope of solvable problems
Solution Approach 1:
The patent creates a universal quantum job orchestration service that can route different types of quantum computing jobs to appropriate quantum annealing systems. The system supports multiple quantum computing platforms (D-Wave, Q-CTRL, Rigetti) and handles various problem types through a unified interface, making the solution adaptable to different hardware constraints while maintaining high computational efficiency for each specific problem type.
Solution Approach 2:
The patent segments the quantum computing ecosystem into distinct components: job submission interfaces, orchestration service, quantum computing systems, and result processing. This segmentation allows each component to be optimized independently - the orchestration service can select the most appropriate quantum system for each specific problem based on its characteristics, thereby overcoming individual hardware limitations.
2Reliability
If multiple quantum annealing systems are evaluated to find the best match for a problem, then solution optimality is improved, but selection time and complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-characterizing quantum computing systems with detailed metadata about their capabilities (qubit count, connectivity graphs, memory throughput, supported problem types). When a new quantum job arrives, the orchestration service quickly matches it to suitable systems using this pre-prepared information, avoiding time-consuming real-time evaluations while still ensuring optimal system selection.
Solution Approach 2:
The orchestration service acts as an intermediary between job submitters and quantum computing systems. It maintains a registry of available quantum systems with their capabilities and automatically performs the matching process. This intermediary layer shields users from the complexity of system selection while ensuring optimal matches, reducing both selection time and user burden.
3Adaptability or versatility
If quantum annealing systems are made more accessible to customers, then adoption rate is improved, but legal and licensing complexities increase
Solution Approach 1:
The patent positions the quantum job orchestration service as an intermediary that handles licensing and access complexity. The service maintains relationships with multiple quantum computing providers and manages the complexities of accessing different quantum systems. Users simply submit their quantum jobs through the orchestration service without needing to navigate licensing agreements or understand the complexities of different quantum platforms, thereby increasing adoption while hiding complexity.
Solution Approach 2:
The orchestration service implements self-service by automatically matching quantum jobs to appropriate systems based on problem characteristics and system capabilities. The system autonomously handles system selection, job routing, and result aggregation without requiring users to manually configure or manage quantum computing resources. This automation reduces the perceived complexity for users while maintaining high accessibility.
Data Source
AI summary
Selecting a quantum annealer for executing a quantum job is disclosed. A classifier is trained using data associated with combinatorial optimization problems that have been solved and quantum annealers used to solve the combinatorial optimization problems. After training, the classifier may receive a new or test problem as input and output an ordered list of labels. Each of the labels corresponds to a quantum annealer. The problem being evaluated can be directed to a most relevant quantum annealer identified in the list. If the problem cannot be solved, the process iterates through the other quantum annealers identified in the list of labels.