Quantum Queue Wait Time Prediction via ML Models
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Solution Overview
Problem
The non-deterministic nature of quantum computing and evolving factors like speed, quality, and performance of quantum resources make it difficult to reliably predict wait times for quantum processing units (QPUs).
Innovation Solution
A quantum computing service is configured to train and implement machine learning models to accurately predict wait times in QPU-specific queues. These models are trained using labeled datasets generated from high-throughput customer requests and QPU-specific performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional queue management is used for QPUs, then system simplicity is maintained, but wait time prediction reliability deteriorates due to the non-deterministic nature of quantum computing
Solution Approach 1:
The patent introduces machine learning models as an intermediary layer between the quantum processing units and the queue management system. These models predict wait times by analyzing historical queue data and QPU performance metrics, thereby resolving the contradiction by providing reliable predictions without requiring fundamental changes to the quantum computing hardware or basic queue structure.
Solution Approach 2:
The system implements a feedback mechanism where actual wait times from executed quantum jobs are collected and used to retrain and refine the machine learning models. This continuous feedback loop improves prediction reliability over time while maintaining a relatively simple queue management structure that builds upon traditional FIFO queues enhanced with predictive capabilities.
2Measurement precision
If machine learning models are implemented to predict wait times, then prediction accuracy improves, but computational overhead and system complexity increase
Solution Approach 1:
The patent applies partial action by implementing machine learning models only for wait time prediction rather than using them for all queue management decisions. The models provide predictive insights that supplement traditional queue management, achieving improved accuracy without the excessive complexity of fully automated AI-driven queue management. The system selectively uses ML predictions where they add value while maintaining simple rule-based fallbacks.
3Adaptability or versatility
If multiple QPUs with different performance characteristics are used, then quantum computing versatility improves, but queue management difficulty increases due to varying speeds and quality
Solution Approach 1:
The patent applies local quality by training separate machine learning models for each QPU based on its specific performance characteristics, error rates, and speed. Each model learns from historical data specific to its QPU, allowing the system to handle diverse quantum processors with different qualities. This approach maintains versatility across multiple QPU types while managing complexity through localized, QPU-specific predictions rather than a single monolithic queue management system.
Data Source
AI summary
Techniques for tracking and maintaining queues used for executing pending quantum objects using respective quantum processing units (QPUs) are disclosed. An amount of time to execute a given quantum object depends on many factors, and a non-deterministic nature of quantum computing resources is such that, while knowing an expected wait time in a queue for access to a given QPU is useful, it is difficult to reliably determine. A quantum computing service that manages submission and execution of quantum objects to respective QPUs may apply QPU-specific machine learning models in order to predict expected wait times and provide that information to customers. By generating labeled datasets using ground truth wait times pertaining to already-executed quantum objects, respective machine learning models may be trained using a supervised learning technique, which may be a self-contained and re-occurring process.