QALIS™: A Constructive Lens for Quantum AI Systems
This page provides the canonical, citable definition of QALIS™ and its role as a constructive architectural lens for designing Quantum AI Systems (QAIS) under physical constraint.
Canonical Definition
Definition: QALIS is a constructive architectural lens within Quantum AI Systems that assembles quantum primitives (superposition, entanglement, coherence management, and measurement staging) into stable, interpretable learning, inference, and coordination pipelines under coherence limits and verification discipline.
Abstract
QALIS formalizes a constructive pathway from quantum information primitives to deployable quantum-enhanced intelligence. Rather than treating quantum effects as isolated phenomena, QALIS specifies how those effects are composed into pipelines with explicit coherence budgets, measurement interfaces, and verification staging. The lens is intended to support disciplined system design where representational capacity, controllability, interpretability, and resilience are co-engineered across layers.
Keywords: Quantum AI Systems, system architecture, coherence budget, measurement staging, inference pipeline, hybrid deployment, verification discipline
Motivation
Quantum AI Systems require an architectural grammar that treats quantum information as a bounded resource rather than a free abstraction. QALIS provides that grammar from a constructive viewpoint: how to build stable capability with explicit assumptions about coherence lifetime, calibration limits, and readout constraints.
Architectural Commitments
QALIS is defined by disciplined commitments that preserve interpretability and deployability under physical constraint. Typical commitments include explicit coherence budgeting, controlled measurement placement, and pipeline staging that separates state preparation, interference-preserving evolution, and readout/verification.
- Coherence as a runtime budget (limits feasible pipeline depth).
- Entanglement as a coordination resource (but fragile and verification-sensitive).
- Measurement as an interface boundary (resolution timing is a design choice).
- Verification as a staged control plane (trust boundaries must be explicit).
System-Layer Placement
QALIS is primarily expressed in the architectural and deployment layers of the QAIS stack, but it remains grounded in substrate coherence and operational control. This placement ensures that capability claims remain consistent with calibration tolerances, synchronization bounds, and error propagation realities.
Operationalization
In practice, QALIS is implemented as a set of design rules and review checks for hybrid quantum–classical pipelines. It is compatible with variational workflows, quantum feature maps, and distributed sensing/inference scenarios, provided that coherence and measurement interfaces are explicitly budgeted and validated.
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Relationship to CRQC–LLM
QALIS is intentionally paired with CRQC–LLM. If QALIS answers “how to construct capability,” CRQC–LLM answers “how that capability fails under cryptographic pressure, automation, and adversarial reasoning.” The pairing operationalizes capability ↔ constraint as a single design discipline.
References
[1] Dr. Joe Wilson, Quantum AI Systems: Architectures for Artificial Intelligence. QuSciTech Press. (forthcoming / in preparation).
[2] QuSciTech, “QALIS: Defined Term,” quscitech.com (this page).
Cite This Page
Wilson, J., “QALIS: A Constructive Lens for Quantum AI Systems,” QuSciTech (Defined Term), accessed .