Research & White Papers
QuSciTech Press publishes systems-oriented research, white papers, technical analyses,
and architectural studies examining Quantum AI Systems (QAIS), quantum information science,
hybrid quantum–classical integration, verification, governance, resilience, and operational deployment.
This research program extends beyond the books themselves by developing and documenting the
frameworks, system relationships, engineering principles, and evidence models that support the
broader QAIS discipline.
Featured White Paper
QuSciTech White Paper
From Quantum Execution to Operational Advantage
A Systems-Engineering Framework for Quantum AI Systems
Dr. Joe Wilson ·
QuSciTech Press ·
August 2026 ·
Version 1.0 Draft publication
A systems-engineering analysis of how quantum execution becomes measurable, verifiable, end-to-end, and operational advantage across hybrid quantum–classical–AI workflows.
Focus Areas
- Quantum execution, theoretical speedup, experimental advantage, verifiable advantage, end-to-end advantage, and operational advantage
- Hybrid quantum–classical orchestration and measurement interfaces
- Verification and validation across the QAIS lifecycle
- Operational costs that can eliminate apparent quantum advantage
- Cross-layer integration across the seven-layer QAIS Quantum Stack
- Implications for QALIS and CRQC–LLM
The PDF is the authoritative publication file. This page provides discovery, context,
and access to QuSciTech research publications.
QAIS Research Frameworks
Research Positioning
The QAIS Difference
Explains how QAIS differs from conventional quantum-computing, quantum-machine-learning,
distributed-AI, and quantum-networking approaches by treating quantum AI as a full-stack
operational discipline.
Explore The QAIS Difference →
Systems Engineering
QAIS and Systems Engineering
Examines the relationship between QAIS and systems-engineering principles, lifecycle models,
architectural frameworks, verification, resilience, and the seven-layer QAIS Quantum Stack.
Explore Systems Engineering →
Cross-Cutting Framework
QALIS
Presents the QALIS framework for learning, inference, evaluation, orchestration,
and system behavior across integrated Quantum AI System architectures.
Explore QALIS →
Cross-Cutting Framework
CRQC–LLM
Presents the CRQC–LLM framework for examining propagation, coordination, resilience,
interpretive boundaries, and system-level behavior across interacting quantum,
classical, and AI components.
Explore CRQC–LLM →