Model-Based Systems Engineering for Quantum Platforms

A quantum platform is a system of systems: cryogenics, vacuum, lasers, microwave control, firmware, compilers, schedulers, user APIs, telemetry, evidence stores, and governance workflows. As platforms scale, document-centric engineering breaks down. Model-based systems engineering gives operators a way to connect requirements, interfaces, hazards, telemetry, and tests.

The goal is not bureaucracy. The goal is traceability: when a requirement changes, the team should know which hardware, software, calibration, benchmark, safety, and user-contract artifacts are affected.

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Model-Based Systems Engineering for Quantum Platforms · Figure 1

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flowchart TB
    Requirement[Requirement] --> Architecture[Architecture model]
    Architecture --> Interface[Interface model]
    Interface --> Implementation[Implementation]
    Implementation --> Verification[Verification]
    Verification --> Evidence[Evidence]
    Evidence --> Requirement

What to model

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What to model · Figure 2

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flowchart TB
    Model[Quantum platform model] --> Physical[Physical system]
    Model --> Control[Control system]
    Model --> Software[Software stack]
    Model --> Data[Data and evidence]
    Model --> Operations[Operations processes]
    Model --> Governance[Governance controls]

Model the decisions that must remain consistent, not every bolt.

Useful model objects:

target capability;
qubit topology;
control channel;
timing reference;
calibration dependency;
compiler pass;
runtime contract;
workload class;
safety hazard;
policy gate;
evidence artifact;
acceptance test.

Requirements traceability

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Requirements traceability · Figure 3

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graph LR
    R1[Requirement: dynamic feedback latency] --> I1[Interface: runtime feedback API]
    R1 --> C1[Control: controller firmware]
    R1 --> T1[Test: feedback latency benchmark]
    T1 --> E1[Evidence: timing trace]
    C1 --> H1[Hazard: stale firmware]

A traceability graph prevents silent divergence between architecture promises and operational reality.

Interface control

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Interface control · Figure 4

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flowchart LR
    Producer[Producer] --> Interface[Interface control document]
    Interface --> Consumer[Consumer]
    Interface --> Version[Version policy]
    Interface --> Test[Conformance test]

Quantum platforms have many fragile interfaces:

Interface	Failure mode
compiler to backend target	invalid scheduling assumptions
runtime to controller	unsupported dynamic behavior
controller to measurement pipeline	timing or metadata mismatch
calibration to compiler	stale parameter propagation
evidence store to audit workflow	unverifiable claims

Capability model

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Capability model · Figure 5

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classDiagram
    class TargetCapability {
      modality
      native_gates
      timing_model
      measurement_model
      feedback_supported
      pulse_access_level
      max_shots
      evidence_level
    }
    class WorkloadRequirement {
      circuit_width
      circuit_depth
      dynamic_control
      timing_sensitivity
      evidence_required
      cost_limit
    }
    WorkloadRequirement --> TargetCapability : matched_against

A capability model should be machine-readable enough to drive routing and review.

Hazard model

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Hazard model · Figure 6

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flowchart TB
    Hazard[Hazard] --> Cause[Cause]
    Hazard --> Control[Control]
    Hazard --> Test[Test]
    Hazard --> Evidence[Evidence]
    Hazard --> Owner[Owner]

Examples:

Hazard	Control
stale calibration used for production run	calibration freshness gate
unsupported pulse feature used by tenant	reject against the target capability profile; approval applies only to supported, authorized controls
misleading benchmark claim	claim review board
raw sensitive data exported	data-classification gate
unsafe lab state during maintenance	safety interlock and checklist

Digital thread

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Digital thread · Figure 7

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flowchart LR
    Design[Design] --> Build[Build]
    Build --> Calibrate[Calibrate]
    Calibrate --> Execute[Execute]
    Execute --> Analyze[Analyze]
    Analyze --> Claim[Claim]
    Claim --> Learn[Learn]
    Learn --> Design

The digital thread connects the book's core operating loop to engineering governance.

Change impact analysis

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Change impact analysis · Figure 8

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sequenceDiagram
    participant Engineer
    participant Model
    participant CI
    participant Review
    participant Runtime

    Engineer->>Model: propose interface change
    Model-->>Engineer: impacted requirements and tests
    Engineer->>CI: run conformance suite
    CI-->>Review: evidence report
    Review->>Runtime: approve rollout

Minimal model repository

Illustrative listing · yaml

model_repository:
  objects:
    - requirements
    - target_capabilities
    - interfaces
    - hazards
    - tests
    - evidence_artifacts
  rules:
    - every production requirement has an acceptance test
    - every safety hazard has an owner
    - every runtime interface has a version policy
    - every claim maps to evidence

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Minimal model repository · Figure 9

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flowchart TB
    Repo[Model repository] --> Req[Requirements]
    Repo --> Cap[Capabilities]
    Repo --> ICD[Interfaces]
    Repo --> Haz[Hazards]
    Repo --> Tests[Tests]
    Repo --> Evidence[Evidence]

Operating rule

Do not let the architecture live only in diagrams. The useful model is the one that can answer: what breaks if this assumption changes?

Additional technical sources: [R25].