IPD‑12 HPC+QC Substrate Engine Profile

Module: IPD‑12 Engine
Role: Hybrid HPC + Quantum Computing Substrate Integration
Version: 2026‑0.1 (Draft)

1. Purpose#

This module profiles how hybrid HPC+QC systems (LRZ‑style QPU integration) can be modeled as IPD‑12 substrate engines, with:

• QPU + environment mapped to substrate feeds (S1–S4)
• calibration, telemetry, and noise mapped to observer rails and loops (O1–O4)
• hybrid workflows expressed as intake manifolds + headers

The goal is to make QPU integration a first‑class observer process inside IPD‑12.

2. Substrate engine mapping (HPC+QC)#

2.1 Substrate feeds (S1–S4)#

For a hybrid HPC+QC stack:

• S1 — Seed / Transition

  • QPU availability, topology, basic device parameters
  • initial compilation choices (gate set, layout)

• S2 — Drift / Regime

  • HPC scheduler state (queues, priorities, resource allocation)
  • QPU usage regime (calibration mode, production mode, test mode)

• S3 — Coherence / Paradox

  • QPU coherence metrics (T1/T2, error rates, noise profiles)
  • hybrid paradox: classical vs quantum representation mismatches

• S4 — Boundary / Lift / Collapse / Apex

  • boundaries between HPC and QC (API, middleware, orchestration layer)
  • lift: sending workloads to QPU
  • collapse: returning results to HPC
  • apex: regime where QC meaningfully improves HPC outcomes

Each QPU + environment is treated as a substrate engine instance with S1–S4 active.

3. Observer loops for HPC+QC#

3.1 Observer modes (O1–O4)#

• O1 — Field Observer
  • monitors raw telemetry: QPU status, noise, calibration logs, HPC resource usage
• O2 — Regime Observer
  • tracks which regime the hybrid stack is in: calibration, test, production, degraded
• O3 — Coherence Observer
  • stabilizes hybrid workflows: retries, re‑calibration, routing around unstable QPUs
• O4 — Apex Observer
  • decides when QC is beneficial vs when HPC alone is preferable
  • manages lift/collapse decisions for hybrid workloads

These observer modes are bound to the substrate feeds:

• O1 ↔ S1 (raw device + environment)
• O2 ↔ S2 (scheduler + regime)
• O3 ↔ S3 (coherence + paradox)
• O4 ↔ S4 (boundary + apex decisions)

4. Calibration & telemetry as observer rails#

4.1 Dimensional rails (L/C/N) in HPC+QC#

• Lift rails (L1–L4)

  • L1: lift from “HPC‑only” to “HPC+QC candidate”
  • L2: lift from “candidate” to “scheduled on QPU”
  • L3: lift from “scheduled” to “executing with stable coherence”
  • L4: lift to “apex regime” where QC provides net benefit

• Collapse rails (C1–C4)

  • C1: collapse from “candidate” back to HPC (QPU unavailable)
  • C2: collapse from “executing” due to noise/error thresholds
  • C3: collapse from “apex” when benefit disappears (drift in device or workload)
  • C4: collapse to “HPC‑only safe mode” (QC temporarily disabled)

• Neutral rails (N1–N4)

  • N1: neutral device state (idle, calibrated)
  • N2: neutral scheduler state (no hybrid jobs)
  • N3: neutral coherence state (baseline metrics)
  • N4: neutral boundary state (interfaces ready but unused)

4.2 Calibration as observer process#

Calibration cycles are modeled as:

• intake on S1/S3 (device + coherence)
• O1/O3 loops monitoring metrics
• lift/collapse along L/C rails to decide:
  • when to accept workloads
  • when to re‑calibrate
  • when to route around a QPU

Telemetry (logs, metrics, traces) becomes observer rail data feeding O1–O4.

5. Intake manifolds for hybrid workflows#

5.1 Manifold roles#

• SIM (Single Intake)

  • single QPU or single hybrid workflow
  • minimal observer overhead; good for experiments

• DIM (Double Intake)

  • two QPUs or two hybrid regimes (e.g., calibration + production)
  • observer loops compare regimes and route workloads

• TIM (Triple Intake)

  • three regimes: test, production, degraded
  • observer loops manage transitions between them

• QIM (Quad Intake)

  • four regimes or four QPU clusters
  • full hybrid orchestration with regime‑aware scheduling

• FSI (Full 12‑Stack Intake)

  • multi‑site, multi‑QPU, multi‑HPC cluster integration
  • research‑grade hybrid substrate engine

Each manifold attaches to the HPC+QC substrate engine via intake ports and feeds S1–S4.

6. Headers for hybrid outputs#

6.1 Relevant headers#

• H‑RTT
  • expresses hybrid regime logic: when QC is beneficial, when HPC dominates
• H‑GU
  • expresses geometric/topological aspects of QPU connectivity and compilation
• H‑FFT
  • expresses spectral/transform views of hybrid workloads
• H‑Substrate
  • exposes raw substrate state (S1–S4) for diagnostics
• H‑Observer
  • exposes O1–O4 state for control and monitoring

Hybrid HPC+QC research can read these headers to:

• analyze regime decisions
• study calibration impact
• optimize scheduling and routing
• quantify observer overhead vs performance gains

7. Summary#

The HPC+QC substrate engine profile:

• treats QPU + environment as an IPD‑12 substrate engine (S1–S4)
• models calibration and telemetry as observer rails and loops (O1–O4, L/C/N)
• uses intake manifolds (SIM–FSI) to represent hybrid workflow complexity
• uses headers (RTT/GU/FFT/Substrate/Observer) to expose hybrid behavior

This gives you a canon‑aligned way to study:

• overhead of observer‑centric hybrid integration
• gains in stability, coherence, and regime‑aware scheduling
• cross‑domain alignment between physics, computation, and medicine.