Quantum Program Optimization via ML Metadata Parsing
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Solution Overview
Problem
Conventional enterprises lack the infrastructure to deploy quantum programs efficiently and securely on external quantum hardware, leading to high costs due to reliance on Quantum as a Service (QaaS) models that charge based on qubit consumption.
Innovation Solution
A computing platform using machine learning models to optimize quantum programs for minimum qubit consumption by parsing metadata, determining criticality levels, and prioritizing deployments, while leveraging non-fungible tokens (NFTs) for secure and efficient orchestration on external quantum hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If enterprises rely on Quantum as a Service (QaaS) to access quantum computing platforms, then they can use quantum programs without infrastructure, but they incur high costs due to qubit consumption charges
Solution Approach 1:
The system performs preliminary optimization of quantum programs before deployment to quantum hardware. Machine learning models analyze and optimize the quantum program code to minimize qubit consumption before the program is executed on external quantum infrastructure, thereby reducing costs while maintaining access to quantum computing capabilities
Solution Approach 2:
The patent replaces manual quantum program optimization with automated machine learning-based optimization systems. The ML models automatically analyze and optimize quantum program code, substituting human effort with automated intelligent systems that can efficiently reduce qubit consumption without requiring quantum hardware infrastructure
2Productivity
If enterprises deploy quantum programs to external quantum hardware, then they can execute quantum computations, but they lack control over deployment optimization and security
Solution Approach 1:
The patent introduces an intermediary quantum DevOps platform that sits between the enterprise and external quantum hardware. This platform includes machine learning models that optimize quantum programs and a criticality assessment system that evaluates program importance, providing enterprises with control over deployment optimization and security without requiring direct infrastructure ownership
Solution Approach 2:
The system implements feedback mechanisms where machine learning models continuously analyze quantum program performance and provide optimization recommendations. The criticality assessment system also provides feedback on program importance levels, enabling enterprises to make informed decisions about deployment priorities and resource allocation
3Ease of operation
If quantum programs are deployed without optimization, then deployment is simple, but qubit consumption is high leading to increased costs
Solution Approach 1:
The system performs preliminary optimization of quantum programs before deployment using machine learning models. The ML-based optimizer analyzes the quantum program code and applies optimizations to reduce qubit consumption before the program is submitted to external quantum hardware, thereby reducing costs while maintaining deployment simplicity for enterprises
Solution Approach 2:
The quantum optimization system operates autonomously using machine learning models that automatically analyze and optimize quantum programs without requiring manual intervention. The system serves itself by continuously learning from quantum program patterns and applying optimizations, reducing qubit consumption while keeping the deployment process simple for end users
Data Source
AI summary
Arrangements for optimizing quantum bit consumption of quantum programs are provided. In some aspects, a quantum program of a plurality of quantum programs to be executed on target quantum hardware may be received from a digital computing device. The quantum program may be scanned and metadata associated with the quantum program may be parsed using machine learning. In some examples, the quantum program may be optimized by modifying quantum program code for minimum quantum bit consumption using machine learning. A criticality level of the quantum program may be determined based on contextual logic associated with the quantum program. Based on the determined criticality level, a priority sequence may be determined, indicating an order in which the plurality of quantum programs are to be deployed to the target quantum hardware. The quantum program may be transmitted to the target quantum hardware according to the priority sequence.