Q-READY: Predictive Feasibility Assessment for Hybrid Quantum-Classical Applications

2026-06-07 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

Q-READY is a Model-Based Systems Engineering (MBSE) framework designed to systematically assess the feasibility of hybrid quantum-classical applications before costly implementation. It integrates Model-Driven Engineering (MDE) principles into a structured pipeline covering quantum-aware requirements modeling, guided problem structuring, strategy configuration, hybrid executable workflow modeling, and predictive feasibility assessment. The framework enables simulation-based evaluation and comparison of candidate solutions under realistic hardware constraints, such as qubit scarcity and noise. Illustrated with a bank credit-portfolio capital-assessment example involving a 10,000-loan, \$1 billion portfolio, Q-READY evaluates if a quantum probability-estimation service can meet specific accuracy (e.g., 0.8% expected-loss error, 1.5% VaR99.9 error), runtime (e.g., 3.5 hours), and resource constraints (e.g., 64 qubits, depth 3200, 50000 shots). The example concludes with a "shadow mode" decision, indicating technical feasibility but with a robustness warning and pending governance approval.

Key takeaway

For AI Architects evaluating hybrid quantum-classical solutions, Q-READY provides a structured methodology to assess feasibility early. You should adopt model-based approaches like Q-READY to systematically link requirements to hardware constraints and predict performance before committing to costly development. This enables evidence-based decisions, allowing you to identify viable quantum components for integration into existing systems, or to flag solutions for "shadow mode" testing due to marginal robustness or pending governance.

Key insights

The Q-READY framework systematically assesses hybrid quantum-classical application feasibility using MBSE and MDE before implementation.

Principles

• Hybrid quantum-classical systems require systematic, hardware-aware engineering.
• Early feasibility assessment prevents costly, ad hoc implementation.
• Model-based traceability links requirements to execution evidence.

Method

Q-READY follows a pipeline: quantum-aware requirements modeling, guided problem structuring, strategy configuration, hybrid executable workflow modeling, and predictive feasibility assessment.

In practice

• Use SysML v2 for structured requirements and models.
• Employ simulation to evaluate solutions pre-deployment.
• Formalize compatibility and dependency constraints.

Topics

• Hybrid Quantum-Classical Applications
• Feasibility Assessment
• Model-Based Systems Engineering
• Quantum Software Engineering
• SysML v2
• Quantum Amplitude Estimation

Best for: Research Scientist, AI Scientist, AI Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.