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Adaptive FIM QPU Protocol Boundary

Date: 2026-05-06

This document defines the approval boundary for any future adaptive lambda_fim hardware campaign. It is a preregistration and readiness artefact, not an IBM submission record and not evidence that adaptive FIM feedback has been validated on hardware.

Current evidence boundary

The repeated SCPN/FIM IBM follow-up on ibm_kingston falsified the simple claim that the tested digital Trotter implementation with lambda_fim = 4 improves hardware coherence. The adaptive protocol therefore starts from the opposite operational assumption: if leakage rises or exact-state retention falls, the controller should reduce lambda_fim, not reward larger feedback.

Safe framing:

  • The controller is a deterministic batch-level rule for selecting the next static lambda_fim value.
  • The controller consumes committed leakage and retention witnesses.
  • The controller does not run mid-circuit feedback.
  • The controller does not submit QPU jobs.
  • Any adaptive hardware claim requires a separately approved run, raw counts, integrity hashes, and analysis artefacts.

Blocked framing:

  • Do not claim real-time adaptive feedback.
  • Do not claim FIM coherence protection.
  • Do not claim platform-general behaviour from a single backend or calibration window.

Implemented software boundary

The implemented controller lives in src/scpn_quantum_control/analysis/adaptive_fim_feedback.py.

Inputs:

  • current lambda_fim,
  • leakage probability in [0, 1],
  • exact-state retention probability in [0, 1],
  • optional depth,
  • optional shot count,
  • AdaptiveFIMConfig.

Modes:

  • leakage_suppression: reduce lambda_fim when leakage exceeds target.
  • retention_recovery: reduce lambda_fim when retention falls below target.

The rule is clipped to [lambda_min, lambda_max] and supports a deadband. This keeps the hardware follow-up conservative after the negative FIM result.

Candidate adaptive campaign

This campaign is not authorised by this document. It is the minimum design that would make an adaptive FIM follow-up interpretable if approved later.

Field Candidate value
Backend class IBM Heron r2 or equivalent calibrated gate-model backend
Initial backend ibm_kingston only if calibration and queue state are acceptable
Qubits n=4
States same representative magnetisation-sector set as the repeated FIM follow-up
Depths {2, 4, 6} unless live transpilation rejects a depth
Lambda grid batch 0 uses {0, 1, 4} from the repeated protocol
Adaptive batches at most 2 follow-up batches after batch 0
Shots 4096 per measured circuit unless budget gate lowers this
Readout full 16-state basis calibration required in each calibration window
Primary witness magnetisation-sector leakage
Secondary witness exact-state retention
Controller mode leakage_suppression unless preregistered otherwise
Maximum lambda 8.0
Minimum lambda 0.0

QPU budget gate

Before submission, the prepared manifest must include:

  • circuit count,
  • shot count,
  • estimated QPU seconds,
  • expected queue class,
  • backend name,
  • backend calibration timestamp,
  • max transpiled depth,
  • max two-qubit gate count,
  • readout calibration circuit count,
  • abort criteria.

Hard budget rule:

  • The campaign must have a written QPU-time estimate before submission.
  • The estimate must fit within the remaining approved QPU budget.
  • If the estimate exceeds the approved budget, reduce optional adaptive batches before reducing calibration integrity.

Live transpilation gate

Each candidate circuit must pass live backend transpilation before submission.

Required metadata:

  • physical qubit layout,
  • transpiled depth,
  • two-qubit gate count,
  • measurement mapping,
  • backend basis gates,
  • optimisation level,
  • pass-manager or transpiler version.

Abort criteria:

  • any lambda_fim > 0 arm exceeds twice the repeated-follow-up maximum depth,
  • any arm loses the intended measurement register,
  • any arm fails backend transpilation,
  • any readout calibration circuit cannot be mapped to the same measured qubits,
  • backend queue or calibration state makes the run likely to exceed the budget.

Falsification and promotion rules

The adaptive campaign is considered successful only if it produces an interpretable bounded result, not only if the effect is positive.

Promotion requires:

  • raw count dictionaries,
  • job IDs,
  • SHA256 hashes,
  • exact circuit manifest,
  • readout calibration manifest,
  • controller input witnesses,
  • generated adaptive schedule,
  • per-batch leakage and retention tables,
  • claim-boundary update.

Positive adaptive claim requires:

  • leakage or retention improves relative to the preregistered non-adaptive baseline after readout mitigation,
  • improvement is not driven by reducing circuit depth or changing layout,
  • the controller's chosen lambda_fim values are reproducible from the committed witness artefact.

Negative or null result claim:

  • if the adaptive rule does not improve leakage or retention, report it as a bounded negative result for the tested backend, depths, states, and controller configuration.

Blocked claims after any single adaptive run:

  • no quantum advantage,
  • no general FIM protection,
  • no backend-general adaptive feedback,
  • no real-time control unless mid-circuit feedback is actually implemented and validated.

Required artefact names

Future approved runs should use these names or document a replacement:

  • data/scpn_fim_hamiltonian/adaptive_fim_candidate_manifest_YYYY-MM-DD.json
  • data/scpn_fim_hamiltonian/adaptive_fim_live_readiness_YYYY-MM-DD.json
  • data/scpn_fim_hamiltonian/adaptive_fim_raw_counts_YYYY-MM-DD_JOBID.json
  • data/scpn_fim_hamiltonian/adaptive_fim_analysis_YYYY-MM-DD_JOBID.json
  • docs/campaigns/adaptive_fim_claim_boundary_YYYY-MM-DD.md

Current status

Status: protocol designed, not authorised for QPU submission.

Next required action before any hardware use: generate a non-submitting candidate manifest with circuit count, shot count, live transpilation metadata, readout calibration plan, and QPU-time estimate.