Quantum Program Provisioning for Low-Qubit QaaS Deployment

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Solution Overview

Problem

Conventional enterprises lack quantum hardware infrastructure and rely on Quantum as a Service (QaaS), incurring costs based on qubit consumption without optimal program deployment and security.

Innovation Solution

A computing platform using NFT technology for intelligent orchestration of quantum programs, optimizing qubit consumption, assigning hardware, and ensuring program security through smart contracts.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If enterprises rely on Quantum as a Service (QaaS) for quantum computing access, then they can use quantum programs without infrastructure investment, but they incur costs based on qubit consumption without optimal program deployment and security control

Engineering Contradiction:

Improveaccess to quantum computingVSAvoidqubit consumption cost

Core Design Contradiction:

Ease of manufactureVSLoss of energy

Solution Approach 1:

The system performs preliminary optimization of quantum programs before deployment to QaaS hardware. Machine learning models analyze and optimize quantum circuits to minimize qubit consumption and execution time in advance, so that when programs are deployed to external quantum hardware, they consume fewer qubits and reduce operational costs.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary layer between enterprise quantum programs and external QaaS hardware. This intermediary platform acts as a broker that optimizes program deployment, manages resource allocation, and controls access to quantum hardware, thereby reducing unnecessary qubit consumption and improving cost efficiency.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Device complexity

If enterprises use external quantum hardware through QaaS, then they avoid infrastructure requirements, but they lack control over program security and deployment optimization

Engineering Contradiction:

Improveinfrastructure requirementsVSAvoidprogram security

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback mechanisms where quantum program performance and security metrics are continuously monitored during execution on external hardware. This feedback is used to refine optimization strategies, improve security protocols, and enhance future program deployments to external quantum systems.

Inventive Principle:

Principle #23Feedback

3Productivity

If quantum programs are deployed without optimization, then deployment is simple and fast, but qubit consumption is high and costs increase

Engineering Contradiction:

Improvedeployment speedVSAvoidqubit consumption

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates optimized copies of quantum programs through machine learning-based transformation. Instead of directly deploying unoptimized programs, the system generates optimized versions that achieve the same computational results with fewer qubits, thereby reducing costs while maintaining deployment efficiency.

Inventive Principle:

Principle #26Copying

4Quantity of substance

If machine learning models are used to optimize quantum programs, then qubit consumption is minimized, but computational overhead and optimization time increase

Engineering Contradiction:

Improvequbit consumptionVSAvoidoptimization time

Core Design Contradiction:

Quantity of substanceVSLoss of time

Solution Approach 1:

The system applies partial optimization strategies where machine learning models focus on optimizing only the most critical portions of quantum programs that have the greatest impact on qubit consumption. This selective approach reduces overall optimization time while still achieving significant qubit reduction, rather than attempting to optimize every aspect of each program.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12572836B2Intelligent provisioning of quantum programs to quantum hardware

Publication Date: 2026.03.10 BANK OF AMERICA CORP

AI summary

Arrangements for intelligent provisioning of quantum programs to quantum hardware 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 optimized for minimum quantum bit (qubit) consumption using one or more machine learning models. Based on an accuracy requirement of a quantum program output, a criticality level of the quantum program may be identified. In addition, a qubit count associated with the quantum program, indicating a number of qubits used to perform quantum processing of the quantum program, may be identified. Based on the identified criticality level and qubit count, an allocation of qubits together with target quantum hardware may be assigned for deploying the quantum program. The target quantum hardware may ingest the quantum program and perform quantum processing of the quantum program.