Cross-Domain Data Fusion and Semantic Operations

Quantum operations data comes from hardware control, calibration systems, cloud providers, compilers, notebooks, workflow engines, cost systems, ticketing, and governance tools. Without a semantic layer, teams debate names instead of operating the platform.

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Cross-Domain Data Fusion and Semantic Operations · Figure 1

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flowchart LR
    Lab[Lab telemetry] --> Fusion[Semantic fusion layer]
    Cloud[Cloud runtime] --> Fusion
    Compiler[Compiler lineage] --> Fusion
    Governance[Governance events] --> Fusion
    Cost[Cost records] --> Fusion
    Fusion --> Ops[Operations views]
    Fusion --> Evidence[Evidence packages]
    Fusion --> Analytics[Analytics]

Canonical entities

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Canonical entities · Figure 2

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classDiagram
    class Workload
    class Execution
    class TargetSnapshot
    class CalibrationEpoch
    class CompilerProfile
    class EvidencePackage
    class Claim
    class PolicyDecision
    Workload --> Execution
    Execution --> TargetSnapshot
    TargetSnapshot --> CalibrationEpoch
    Execution --> CompilerProfile
    Execution --> EvidencePackage
    EvidencePackage --> Claim
    PolicyDecision --> Execution

Each entity needs a stable identifier, owner, lifecycle state, timestamp policy, and provenance record.

Naming discipline

Ambiguous term	Clarify as
target	hardware endpoint, target class, or provider backend
job	broker job, provider job, workflow task, or experiment run
calibration	full calibration, partial update, or metadata snapshot
error	physical, compiler, runtime, policy, or claim error
result	raw counts, mitigated observable, reviewed claim, or product decision

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Naming discipline · Figure 3

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flowchart TB
    RawTerms[Raw terms] --> Ontology[Operational ontology]
    Ontology --> CanonicalIDs[Canonical IDs]
    CanonicalIDs --> Joins[Reliable joins]
    Joins --> Views[Operational views]

Event fabric

CloudEvents provides a common event envelope, while OpenTelemetry provides data models and semantic conventions for telemetry. Quantum platforms can use those patterns while adding quantum-specific fields. [R171] [R175]

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Event fabric · Figure 4

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sequenceDiagram
    participant Broker
    participant Runtime
    participant Evidence
    participant Warehouse
    Broker->>Warehouse: workload.submitted
    Runtime->>Warehouse: execution.started
    Runtime->>Warehouse: execution.completed
    Evidence->>Warehouse: evidence.validated
    Evidence->>Warehouse: claim.reviewed

A quantum event should include workload ID, execution ID, provider, target snapshot, calibration epoch, compiler profile, runtime options, evidence package, and status.

Reliable joins

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Reliable joins · Figure 5

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flowchart LR
    WorkloadID[workload_id] --> Join[Join fabric]
    ProviderJob[provider_job_id] --> Join
    TargetSnapshot[target_snapshot_id] --> Join
    EvidenceID[evidence_package_id] --> Join
    CostID[cost_record_id] --> Join
    Join --> Record[Unified operational record]

Bad joins create false conclusions. A result linked to the wrong calibration epoch can produce the wrong root cause. A cost record detached from the workload contract makes unit economics unusable.

Operational views

View	Users
fleet health	operators and SREs
workload lineage	researchers and platform engineers
cost and capacity	FinOps and executives
audit evidence	legal, security, compliance
reproducibility	scientific reviewers

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Operational views · Figure 6

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flowchart TB
    SemanticLayer[Semantic layer] --> Fleet[Fleet health]
    SemanticLayer --> Lineage[Workload lineage]
    SemanticLayer --> Cost[Cost view]
    SemanticLayer --> Audit[Audit view]
    SemanticLayer --> Repro[Reproducibility view]

OpenAPI can describe stable broker, evidence, scorecard, and governance APIs. Verifiable credentials can represent issuer-backed facts such as training status, benchmark certification, or provider tier. [R174] [R176]

Operating rule

A quantum platform needs a semantic control plane. Without it, every serious analysis becomes custom data archaeology.