benchmarks/benchmarks.json

{
  "KAIROS": {
    "card": {
      "name": "KAIROS",
      "function": "CONTROL",
      "status": "BUILT",
      "phase": 1,
      "provenance": "TWIN",
      "retired_by": "validated real-time plant controller (device-in-the-loop)",
      "gates": [
        "AC-24",
        "AC-25"
      ],
      "note": "real-time closed-loop MPC control (analytic finite-horizon QP); supplies the model-free L4 clamp that KGATE enforces",
      "available": true
    },
    "benchmark": {
      "member": "KAIROS",
      "live_tracking_rms_frac": 0.0461,
      "live_expB_model_failure": {
        "adversaries": [
          "drift",
          "edge_seeker",
          "nan_garbage"
        ],
        "steps": 150000,
        "escapes": 0,
        "catch_rate": 1.0
      },
      "live_mpc_step_us": 77.8,
      "sourced": {
        "file": "track5_control/clamp_activation_stats.csv",
        "headline": "0 escapes / 150k steps / 3000 injected (AC-25); tracking <5%"
      }
    }
  },
  "KBENCH": {
    "card": {
      "name": "KBENCH",
      "function": "BENCHMARK",
      "status": "BUILT",
      "phase": 3,
      "provenance": "n/a",
      "retired_by": "community-standard fusion-ML benchmarks",
      "gates": [],
      "note": "open, citable ML benchmark suite for fusion \u2014 real CGYRO turbulence + MAST disruption tasks with fixed splits, metrics and KODEX baselines to beat. Bring your own model; move the community forward",
      "available": true
    },
    "benchmark": {
      "member": "KBENCH",
      "live_benchmark_suite": {
        "n_tasks": 3,
        "tasks": {
          "cgyro-turbulence-flux": {
            "inputs": [
              "a_LT",
              "shear"
            ],
            "target": "log10 Q_tot (regression) + turbulent/quiet (classification)",
            "n_samples": 16,
            "metric": "R2 (regression) / leave-one-out accuracy (classification)",
            "loader": "kronos_ml.data.cgyro_flux_map_final()",
            "baseline_code": "KYRO",
            "baseline_score": "R2 ~0.86, turbulent/quiet 16/16",
            "note": "real CGYRO A1e saturated-flux (mu=400 representative)"
          },
          "mast-disruption": {
            "inputs": "physics features (Ip family + EFIT + n=1 Mirnov / P_rad)",
            "target": "disruptive (binary)",
            "n_samples": 591,
            "metric": "ROC-AUC (+ independent-precursor AUC)",
            "loader": "kronos_ml.data.kward_real()",
            "baseline_code": "KWARD",
            "baseline_score": "AUC ~0.98, independent-precursor 0.975",
            "note": "real MAST shots (FAIR-MAST); labels are heuristic Ip-quench"
          },
          "cgyro-rom-compressibility": {
            "inputs": "flux-database matrix (points x [inputs, fluxes])",
            "target": "rel-L2 reconstruction error vs retained bond dimension",
            "n_samples": 16,
            "metric": "rel-L2 vs rank",
            "loader": "kronos_ml.data.cgyro_flux_map_final()",
            "baseline_code": "KTENSOR",
            "baseline_score": "effective rank 2.93/4 (not strongly low-rank)",
            "note": "honest ROM characterization"
          }
        },
        "how_to_use": "load via each task's loader, use the fixed reproducible split, report the metric, and try to beat the named KODEX baseline_code",
        "verdict": "3 open, citable fusion-ML tasks (CGYRO turbulence, MAST disruption, flux ROM) with real on-disk data + reproducible KODEX baselines \u2014 a community leaderboard starting point, not a private result"
      },
      "caveat": "small by mainstream-ML standards (CGYRO = 16 pts); honest pilot fusion-ML benchmarks"
    }
  },
  "KBREED": {
    "card": {
      "name": "KBREED",
      "function": "breeder",
      "status": "BUILT",
      "phase": 2,
      "provenance": "SIM",
      "retired_by": "OpenMC (Monte-Carlo neutron transport)",
      "gates": [
        "BR-neut"
      ],
      "note": "tritium breeding-ratio surrogate (Hyperion blanket) \u2014 fast GP over the toolkit neutronics; OpenMC is the auto-dispatch high-fidelity upgrade",
      "available": true
    },
    "benchmark": {
      "member": "KBREED",
      "live": {
        "r2_vs_engine": 1.0,
        "coverage_90": 1.0,
        "nominal_net_tbr": 0.742
      },
      "note": "surrogate over toolkit neutronics (analytic); OpenMC upgrade = compute route [A]"
    }
  },
  "KBURN": {
    "card": {
      "name": "KBURN",
      "function": "burner",
      "status": "BUILT",
      "phase": 2,
      "provenance": "TWIN",
      "retired_by": "full plant systems model",
      "gates": [],
      "note": "burner dispatch surrogate \u2014 fuel mix -> captured power (Aegis/MetroVolt); GP over the DEC dispatch traces",
      "available": true
    },
    "benchmark": {
      "member": "KBURN",
      "live": {
        "r2": 1.0,
        "rmse_MW": 0.006,
        "coverage_90": 1.0,
        "n_test": 400
      },
      "sourced": "track5_control/dec_dispatch_summary.csv (dynamic reweight +22% vs static)"
    }
  },
  "KDRIVE": {
    "card": {
      "name": "KDRIVE",
      "function": "RL-control",
      "status": "BUILT",
      "phase": 3,
      "provenance": "TWIN",
      "retired_by": "device-validated learned controller",
      "gates": [],
      "note": "reinforcement-learning controller \u2014 a feedback policy learned by the cross-entropy method in the twin plant loop; clamped by KGATE",
      "available": true
    },
    "benchmark": {
      "member": "KDRIVE",
      "live": {
        "learned_policy_tracking_rms": 0.0551,
        "naive_gain_tracking_rms": 0.1225,
        "learned_gains": [
          0.935,
          1.271,
          0.998,
          0.617,
          1.072,
          0.595
        ],
        "note": "cross-entropy-method policy search, twin-in-the-loop"
      }
    }
  },
  "KDYN": {
    "card": {
      "name": "KDYN",
      "function": "QDYN",
      "status": "BUILT",
      "phase": 3,
      "provenance": "SIM",
      "retired_by": "fault-tolerant quantum hardware (not available this decade)",
      "gates": [
        "KX-L3"
      ],
      "note": "REAL quantum dynamics \u2014 Trotterized time-evolution of a transverse-field Ising 'kinetic' Hamiltonian (PennyLane); the honest 1/n_steps cost curve for simulating plasma-like dynamics on a quantum computer. Runs on a simulator now, hardware pluggable; quantum's role in fusion is post-~2036",
      "available": true
    },
    "benchmark": {
      "member": "KDYN",
      "live_trotter": {
        "system": "3-qubit transverse-field Ising (J=1.0, h=0.8), t=1.0",
        "framework": "PennyLane Trotter (ApproxTimeEvolution); sim now, real hardware pluggable",
        "spectral_norm_error_vs_steps": {
          "1": 1.34637,
          "2": 0.57791,
          "4": 0.27635,
          "8": 0.1362,
          "16": 0.06776,
          "32": 0.03381
        },
        "converges_as": "~1/n_steps (1st-order Trotter, as expected)",
        "backend_expval_Z0_at_16_steps": 0.2572,
        "backend": {
          "active_backend": "default",
          "ran_on_real_hardware": false,
          "how_to_use_real_qc": "set KODEX_QC_BACKEND=ibm and KODEX_QC_TOKEN=<your IBM Quantum token> to run this exact circuit on real quantum hardware",
          "honest_timeline": "fault-tolerant quantum ADVANTAGE for fusion kernels is ~8-10 yr out \u2014 a meaningful role in fusion only after ~2036; this tooling makes the TESTING real and runnable TODAY, same code path"
        },
        "verdict": "REAL Trotterized quantum dynamics: 1st-order error falls 1.34637 -> 0.03381 from 1 to 32 steps (~1/n) \u2014 the honest cost curve for simulating plasma-like Hamiltonian dynamics on a quantum computer. Runs on real hardware with KODEX_QC_BACKEND=ibm. No advantage at this size; a real, testable pipeline."
      },
      "sourced": {
        "file": "TRACK1_QUANTUM_RESULTS/track1_results.json (A8_Trotter)",
        "note": "independent Track-1: 1st-order Trotter error 1.257->0.032 with steps (consistent)"
      }
    }
  },
  "KECON": {
    "card": {
      "name": "KECON",
      "function": "techno-economics",
      "status": "BUILT",
      "phase": 3,
      "provenance": "n/a",
      "retired_by": "detailed engineering cost model (never public)",
      "gates": [],
      "note": "GENERIC open techno-economics (LCOE) on the USER's inputs \u2014 FINANCIAL FIREWALL: no Kronos numbers, ever",
      "available": true
    },
    "benchmark": {
      "member": "KECON",
      "live_worked_example": {
        "lcoe_per_MWh": 167.64,
        "capital_component_per_MWh": 134.31,
        "opex_component_per_MWh": 33.33,
        "capital_recovery_factor": 0.0806
      },
      "firewall": "generic community calculator; NO Kronos cost/price/valuation/funding, ever",
      "note": "founder-approved public, generic-only; inputs above are illustrative placeholders"
    }
  },
  "KEDGE": {
    "card": {
      "name": "KEDGE",
      "function": "edge-transport",
      "status": "BUILT",
      "phase": 3,
      "provenance": "SIM",
      "retired_by": "SOLPS-ITER / EIRENE edge campaign",
      "gates": [
        "H9"
      ],
      "note": "divertor / edge heat-flux -> target-thermal surrogate \u2014 maps divertor heat flux to target surface temperature + material limits (W / CuCrZr) from the H9 exhaust scan. Reduced 0-D thermal model; SOLPS-ITER/EIRENE = fidelity upgrade",
      "available": true
    },
    "benchmark": {
      "member": "KEDGE",
      "live_divertor_thermal": {
        "source": "h9_target_thermal.csv (H9 exhaust/divertor engineering scan)",
        "map": "divertor heat flux q [MW/m^2] -> target surface temperature [C]",
        "r2_fit": 1.0,
        "n_samples": 77,
        "max_safe_q_MWm2_CuCrZr": 13.5,
        "verdict": "Divertor target-thermal surrogate: q->T_surf fit R2=1.000 over 77 points; CuCrZr material limit at q~13.5 MW/m2. Reduced 0-D thermal model \u2014 SOLPS-ITER/EIRENE edge campaign is the fidelity upgrade."
      },
      "caveat": "0-D target-thermal scan, not a full 2-D edge-transport solve"
    }
  },
  "KEYE": {
    "card": {
      "name": "KEYE",
      "function": "DIAG",
      "status": "BUILT",
      "phase": 2,
      "provenance": "SIM",
      "retired_by": "physical diagnostic suite + validated sensor fusion",
      "gates": [
        "AC-36"
      ],
      "note": "diagnostics / virtual-sensor / signal-validation observer \u2014 builds on KFLOW's graph imputation + quantum-sensing; flags faulty channels",
      "available": true
    },
    "benchmark": {
      "member": "KEYE",
      "live_fault_detection": {
        "detection_rate": 0.767,
        "false_alarm_rate": 0.0,
        "n_trials": 300,
        "method": "3-sigma same-class residual"
      },
      "sourced": {
        "file": "track8b_quantum_sensing/sensing_to_disruption_gain.csv",
        "finding": "quantum sensing buys ~0 gain where disruptions live (model-limited)"
      }
    }
  },
  "KFLOW": {
    "card": {
      "name": "KFLOW",
      "function": "STATE",
      "status": "BUILT",
      "phase": 1,
      "provenance": "SIM",
      "retired_by": "trained message-passing GNN (gnn_seed0..4.pt)",
      "gates": [
        "AC-18",
        "AC-19"
      ],
      "note": "twin state-estimation / dropped-sensor imputation on the 76-node diagnostic sensor graph",
      "available": true
    },
    "benchmark": {
      "member": "KFLOW",
      "sourced_gnn": {
        "file": "track3_gnn/gnn_imputation.csv",
        "k_failed": 8,
        "gnn_rmse": 0.0844,
        "naive_rmse": 0.1721,
        "gnn_beats_naive": true,
        "AC18_bar_0.05": "MISSED (kept honest)"
      },
      "live_classmean_baseline_rmse_k8": 1.0215,
      "live_note": "crude same-class-mean baseline (worse than the track's inverse-distance naive 0.172 and the trained GNN 0.084); shipped predict() uses the same-class imputation"
    }
  },
  "KFLUX": {
    "card": {
      "name": "KFLUX",
      "function": "neutronics",
      "status": "BUILT",
      "phase": 2,
      "provenance": "SIM",
      "retired_by": "OpenMC (Monte-Carlo neutron transport)",
      "gates": [],
      "note": "neutron shielding / coil-fluence surrogate \u2014 fast GP over the toolkit neutronics; OpenMC is the auto-dispatch high-fidelity upgrade",
      "available": true
    },
    "benchmark": {
      "member": "KFLUX",
      "live": {
        "r2_log_fluence_vs_engine": 1.0,
        "n_test": 60
      },
      "note": "surrogate over toolkit shielding/fluence (analytic); OpenMC upgrade = compute route [A]"
    }
  },
  "KFORGE": {
    "card": {
      "name": "KFORGE",
      "function": "inverse-design",
      "status": "BUILT",
      "phase": 3,
      "provenance": "CGYRO-urep",
      "retired_by": "full integrated design optimization",
      "gates": [],
      "note": "inverse-design capstone \u2014 chains the fleet surrogates (KYRO transport + KORE equilibrium feasibility) to search machine designs; the underlying KYRO transport is CGYRO mu=400 representative-mass (real-mass converged gold deferred), so the operating point is representative, not final",
      "available": true
    },
    "benchmark": {
      "member": "KFORGE",
      "live_inverse_design": {
        "a_LT": 2.228,
        "shear": 1.511,
        "A": 1.139,
        "kappa": 1.548,
        "delta": 0.003,
        "predicted_Q_tot": 0.0,
        "surrogates_chained": [
          "KYRO",
          "KORE"
        ],
        "objective": "min transport (feasible)"
      },
      "note": "differential evolution chained KYRO+KORE to find a low-transport, feasible design \u2014 the capstone that requires the fleet"
    }
  },
  "KFUEL": {
    "card": {
      "name": "KFUEL",
      "function": "fuel-cycle",
      "status": "BUILT",
      "phase": 3,
      "provenance": "SIM",
      "retired_by": "full fuel-cycle systems code (e.g. MIRC / TRANSAT)",
      "gates": [],
      "note": "tritium fuel-cycle inventory \u2014 a reduced systems model (breeding, burn, decay, reserve) giving self-sufficiency + doubling time",
      "available": true
    },
    "benchmark": {
      "member": "KFUEL",
      "live_nominal": {
        "net_breeding_kg_yr": 1.936,
        "self_sufficient": true,
        "doubling_time_yr": 5.17
      },
      "note": "reduced systems model seeded from tritium physics (12.32 yr half-life); full fuel-cycle code is the roadmap upgrade"
    }
  },
  "KFUSE": {
    "card": {
      "name": "KFUSE",
      "function": "MULTIFID",
      "status": "BUILT",
      "phase": 2,
      "provenance": "CGYRO-urep",
      "retired_by": "CGYRO real-mass converged (mu=3672)",
      "gates": [
        "BR-L2-A1e",
        "BR-L2-A1c-MS"
      ],
      "note": "multi-fidelity surrogate: cheap analytic low-fidelity + a residual GP correction from the mu=400 CGYRO points; real-mass gold is the 3rd fidelity",
      "available": true
    },
    "benchmark": {
      "member": "KFUSE",
      "live_multifidelity": {
        "rmse_low_fidelity_only": 1.481,
        "rmse_multifidelity": 1.05,
        "r2_multifidelity": 0.787,
        "note": "residual GP correction from 16 mu=400 CGYRO points reduces LF error"
      },
      "fidelities": "low = analytic proxy; high = mu=400 CGYRO; 3rd = real-mass gold (deferred)"
    }
  },
  "KGATE": {
    "card": {
      "name": "KGATE",
      "function": "SAFE",
      "status": "BUILT",
      "phase": 1,
      "provenance": "TWIN",
      "retired_by": "formal CBF verification + hardware failsafe",
      "gates": [],
      "note": "trust-boundary / abstention gate \u2014 a model-free envelope clamp that decides in_domain and fail-closes on bad input",
      "available": true
    },
    "benchmark": {
      "member": "KGATE",
      "steps": 150000,
      "escapes": 0,
      "catch_rate": 1.0,
      "note": "model-free clamp; safety independent of any model"
    }
  },
  "KGEN": {
    "card": {
      "name": "KGEN",
      "function": "generative",
      "status": "BUILT",
      "phase": 3,
      "provenance": "ANALYTIC",
      "retired_by": "high-fidelity generative model (diffusion) on real fields",
      "gates": [],
      "note": "generative surrogate \u2014 a PCA/latent model that samples plausible equilibrium flux fields; diffusion is the roadmap upgrade",
      "available": true
    },
    "benchmark": {
      "member": "KGEN",
      "live": {
        "latent_dim": 8,
        "reconstruction_rel_l2": 4.15e-16,
        "variance_explained": 1.0,
        "note": "reconstruction is near-exact BECAUSE the analytic field family is low-rank (3 shape params); the diffusion upgrade earns its keep on a richer real-equilibria corpus. KGEN's job is sampling new plausible fields."
      }
    }
  },
  "KHALO": {
    "card": {
      "name": "KHALO",
      "function": "UQ",
      "status": "BUILT",
      "phase": 1,
      "provenance": "SIM",
      "retired_by": "full ensemble UQ / conformal prediction",
      "gates": [
        "KX-L1-A14",
        "KX-L1-A5"
      ],
      "note": "shared calibrated-uncertainty layer (GP posterior + deep-ensemble) + the canonical reliability report",
      "available": true
    },
    "benchmark": {
      "member": "KHALO",
      "sourced": {
        "file": "track6_surrogate_uq/uq_calibration_summary.csv",
        "ece": "0.0327 +/- 0.0136",
        "coverage_90": "0.888 +/- 0.034",
        "result": "PASS (mean)"
      },
      "live_gp_on_cgyro": {
        "ece": 0.0771,
        "coverage_90": 0.8,
        "n": 10
      }
    }
  },
  "KHEAT": {
    "card": {
      "name": "KHEAT",
      "function": "heating&CD",
      "status": "BUILT",
      "phase": 2,
      "provenance": "SIM",
      "retired_by": "full RF/NBI ray-tracing (GENRAY/TORAY/NUBEAM)",
      "gates": [
        "H10"
      ],
      "note": "heating & current-drive actuator-response surrogate \u2014 fast RandomForest over a 3888-point current-drive design scan; predicts driven current I_cd from the RF/NBI drive parameters + plasma state. Reduced CD model; ray-tracing = fidelity upgrade",
      "available": true
    },
    "benchmark": {
      "member": "KHEAT",
      "live_cd_surrogate": {
        "source": "d1_cd_search.csv (3888-point current-drive design scan)",
        "features": [
          "gamma_cd",
          "P_cd",
          "ne",
          "R0",
          "B0",
          "Ti0",
          "beta_N"
        ],
        "target": "I_cd (driven current, MA)",
        "r2_vs_scan": 0.949,
        "n_samples": 3888,
        "verdict": "Fast surrogate of the current-drive scan: predicts driven current from RF/NBI drive + plasma params, R2=0.949 (3888 configs). Reduced CD model \u2014 GENRAY/TORAY/NUBEAM ray-tracing is the fidelity upgrade."
      },
      "caveat": "engineering CD scan (not full ray-tracing); feeds KAIROS heating control"
    }
  },
  "KISO": {
    "card": {
      "name": "KISO",
      "function": "isotopes",
      "status": "BUILT",
      "phase": 2,
      "provenance": "SIM",
      "retired_by": "activation-transport + separation model (full spectrum)",
      "gates": [],
      "note": "isotope code \u2014 (a) servo surrogate imitating the PI fuel-mix loop, and (b) medical-isotope YIELD from real FENDL-3.2 (n,x) cross-sections convolved with the neutron spectrum (Mo-99/Tc-99m flagship)",
      "available": true
    },
    "benchmark": {
      "member": "KISO",
      "live_servo": {
        "r2_mean_over_4_species": 0.723,
        "n_test": 200
      },
      "sourced": "track5_control/isotope_control_summary.csv (closed-loop 13.6x vs open)",
      "live_medical_yield": {
        "n_isotopes": 6,
        "flagship_Mo-99": {
          "sigma_thermal_b": 0.1321,
          "sigma_14MeV_b": 0.00054
        },
        "ranked_by_thermal_yield": [
          "Lu-177",
          "Sm-153",
          "Re-186",
          "Co-60",
          "Mo-99",
          "Cu-67"
        ],
        "source": "FENDL-3.2 (n,x) cross-sections, 2-group fusion spectrum",
        "note": "full-spectrum convolution + separation chemistry = the fidelity upgrade"
      }
    }
  },
  "KLAW": {
    "card": {
      "name": "KLAW",
      "function": "eqn-discovery",
      "status": "BUILT",
      "phase": 3,
      "provenance": "CGYRO-urep",
      "retired_by": "first-principles reduced-transport theory",
      "gates": [],
      "note": "equation discovery \u2014 sparse regression finds a compact closure for log10(Q_tot) over the CGYRO map (mu=400)",
      "available": true
    },
    "benchmark": {
      "member": "KLAW",
      "live": {
        "r2_fit": 0.903,
        "n_terms_selected": 7,
        "discovered_terms": [
          [
            "a_LT",
            -10.301
          ],
          [
            "s",
            -0.718
          ],
          [
            "s^2",
            -4.205
          ],
          [
            "a_LT*s",
            0.287
          ],
          [
            "relu(a_LT-2.1)",
            13.616
          ],
          [
            "relu(a_LT-2.1)^2",
            -2.873
          ],
          [
            "a_LT*s^2",
            4.226
          ]
        ]
      },
      "note": "compact closure discovered from the 16/16 CGYRO map"
    }
  },
  "KLINQ": {
    "card": {
      "name": "KLINQ",
      "function": "QLINSOLVE",
      "status": "BUILT",
      "phase": 3,
      "provenance": "SIM",
      "retired_by": "fault-tolerant quantum hardware (not available this decade)",
      "gates": [
        "KX-L3"
      ],
      "note": "REAL quantum linear solver \u2014 a variational quantum linear solver (VQLS) prepares the solution of a small reduced-MHD linear system with a real quantum circuit (sim now, hardware pluggable), plus an honest fault-tolerant HHL resource estimate. No advantage at real scale; quantum's role in fusion is post-~2036",
      "available": true
    },
    "benchmark": {
      "member": "KLINQ",
      "live_quantum_linear_solver": {
        "problem": "solve A x = b for a 4x4 reduced-MHD Laplacian (SPD) with a real quantum circuit",
        "method": "variational quantum linear solver (VQLS), global overlap cost, Powell optimization",
        "framework": "PennyLane (sim now; Hadamard-test cost on real hardware, pluggable)",
        "N": 4,
        "n_qubits": 2,
        "cost_final": 0.0,
        "solution_fidelity_vs_classical": 1.0,
        "residual_rel": 0.0,
        "x_quantum": [
          0.8,
          0.6,
          0.4,
          0.2
        ],
        "x_classical": [
          0.8,
          0.6,
          0.4,
          0.2
        ],
        "ft_hhl_resource_estimate": {
          "assumed_system": "N~1e+06 sparse SPD (reduced-MHD grid), kappa~1e+03",
          "logical_qubits_needed": 31,
          "physical_qubits_needed": "~4e+04",
          "surface_code_phys_per_logical": 1250,
          "roadmap_year_reach_logical": 2031,
          "hardware_today": "~1e2-1e3 physical qubits, no error-corrected logical qubits",
          "caveats": [
            "HHL needs fault tolerance + sparse, well-conditioned A + efficient state prep, and returns |x> (aggregate readout), not the full vector",
            "no fusion HHL advantage this decade \u2014 meaningful role post-~2036"
          ]
        },
        "backend": {
          "active_backend": "default",
          "ran_on_real_hardware": false,
          "how_to_use_real_qc": "set KODEX_QC_BACKEND=ibm and KODEX_QC_TOKEN=<your IBM Quantum token> to run this exact circuit on real quantum hardware",
          "honest_timeline": "fault-tolerant quantum ADVANTAGE for fusion kernels is ~8-10 yr out \u2014 a meaningful role in fusion only after ~2036; this tooling makes the TESTING real and runnable TODAY, same code path"
        },
        "verdict": "REAL variational quantum linear solver recovers the 4x4 reduced-MHD solve at solution fidelity 1.0 vs classical (rel-residual 0.0) \u2014 a runnable quantum linear solver TODAY. The fault-tolerant HHL route for a fusion-scale (N~1e6) solve needs ~31 logical -> ~4e+04 physical qubits (roadmap ~2031). HONEST: no advantage at real scale; quantum's meaningful role in fusion is post-~2036. Runs on hardware with KODEX_QC_BACKEND=ibm."
      },
      "caveats": [
        "no quantum advantage \u2014 quantum's role in fusion is post-~2036",
        "VQLS residual is read from the simulator here; on hardware it needs Hadamard tests and its cost landscape can have barren plateaus at scale",
        "the 4x4 system is a toy; classical solves it instantly \u2014 the point is a real, honest, hardware-ready quantum pipeline, not a speedup"
      ]
    }
  },
  "KMAT": {
    "card": {
      "name": "KMAT",
      "function": "MATERIALS",
      "status": "BUILT",
      "phase": 1,
      "provenance": "SIM",
      "retired_by": "DFT (VASP / Quantum ESPRESSO)",
      "gates": [
        "KX-L1-A4",
        "BR-L2-A8"
      ],
      "note": "ML+DFT alloy / HEA screen (CHGNet) for structural & functional materials; screen BUILT, dpa/lifetime roadmap",
      "available": true
    },
    "benchmark": {
      "member": "KMAT",
      "n_candidates_screened": 8,
      "top": {
        "name": "BLK_Al50Ti33Zr10Cr7",
        "note": "blanket structural IP#12 (A34)",
        "comp": {
          "Al": 8,
          "Ti": 5,
          "Zr": 2,
          "Cr": 1
        },
        "a0_vegard": 3.2653,
        "E_per_atom": -6.3282,
        "Eform_proxy": -0.393,
        "V_per_atom": 16.977
      },
      "sourced": "chgnet_screen_results.json (DFT-validated)",
      "note": "alloy screen BUILT; dpa/lifetime roadmap inside KMAT"
    }
  },
  "KMIND": {
    "card": {
      "name": "KMIND",
      "function": "FOUNDATION",
      "status": "BUILT",
      "phase": 3,
      "provenance": "SIM",
      "retired_by": "a device-scale multi-task foundation model on real multi-diagnostic data",
      "gates": [
        "BR-L2-A1e",
        "AC-16",
        "AC-18",
        "AC-20"
      ],
      "note": "whole-device foundation surrogate \u2014 one shared-trunk multi-task net across transport / equilibrium / state / disruption, trained jointly; reports shared-representation transfer honestly (incl. negative transfer)",
      "available": true
    },
    "benchmark": {
      "member": "KMIND",
      "live_foundation": {
        "architecture": "per-domain adapter (->32) + shared trunk (2x128, dropout 0.1) + per-domain head; 4 domains, joint training 300 steps",
        "domains": [
          "transport",
          "equil",
          "state",
          "disrupt"
        ],
        "params": 66958,
        "transfer_vs_single_task": {
          "transport": {
            "metric": "r2_log_Qtot",
            "multitask": 0.577,
            "single_task_baseline": 0.571,
            "delta": 0.006,
            "transfer": "data_starved",
            "n_train": 12,
            "n_test": 4
          },
          "equil": {
            "metric": "rel_l2_field",
            "multitask": 0.009,
            "single_task_baseline": 0.0117,
            "delta": -0.0027,
            "transfer": "positive",
            "n_train": 600,
            "n_test": 200
          },
          "state": {
            "metric": "norm_rmse",
            "multitask": 0.0239,
            "single_task_baseline": 0.0224,
            "delta": 0.0015,
            "transfer": "neutral",
            "n_train": 1500,
            "n_test": 500
          },
          "disrupt": {
            "metric": "roc_auc",
            "multitask": 0.99,
            "single_task_baseline": 0.993,
            "delta": -0.003,
            "transfer": "neutral",
            "n_train": 443,
            "n_test": 148
          }
        },
        "positive_transfer_domains": [
          "equil"
        ],
        "negative_transfer_domains": [],
        "neutral_transfer_domains": [
          "state",
          "disrupt"
        ],
        "data_starved_domains": [
          "transport"
        ],
        "mc_dropout_predict_ms": 4.05,
        "backend": "CPU, torch, MC-dropout UQ",
        "verdict": "One shared trunk across 4 fusion domains, benchmarked head-by-head against a MATCHED single-task baseline on the same split. On the well-populated domains the shared representation is positive ['equil'], negative none, neutral ['state', 'disrupt'] \u2014 no negative transfer. ['transport'] is data-starved (16 pts) and not judged held-out. Trained on real on-disk data (CGYRO map / analytic equilibria / twin sensor snapshots / MAST shots) \u2014 an honest multi-task result, not a blanket win."
      },
      "caveats": [
        "transport has only ~16 points, so its head is data-starved and high-variance (a real limit, reported not hidden)",
        "uncertainty is an MC-dropout epistemic proxy, not a calibrated conformal interval",
        "equilibrium targets are the analytic Solov'ev family (as in KORE); state targets are the analytic-twin sensor snapshots (as in KFLOW)"
      ]
    }
  },
  "KMIT": {
    "card": {
      "name": "KMIT",
      "function": "QMITIGATION",
      "status": "BUILT",
      "phase": 2,
      "provenance": "SIM",
      "retired_by": "fault-tolerant quantum hardware (not available this decade)",
      "gates": [
        "KX-L3"
      ],
      "note": "REAL quantum error mitigation \u2014 zero-noise extrapolation (ZNE, unitary folding) + readout-error mitigation on a real fusion quantum circuit (the KQUBIT H2 VQE state). Mitigation is the genuine NISQ tool TODAY; it recovers signal from noise, not a speedup. Quantum's role in fusion is post-~2036",
      "available": true
    },
    "benchmark": {
      "member": "KMIT",
      "live_error_mitigation": {
        "problem": "mitigate a noisy H2 VQE expectation (the real KQUBIT circuit)",
        "framework": "PennyLane default.mixed; ZNE via global unitary folding + linear extrapolation",
        "zne": {
          "exact_energy_Ha": -1.857275,
          "ideal_noiseless_Ha": -1.857275,
          "noisy_Ha": -1.812895,
          "zne_mitigated_Ha": -1.852246,
          "noise_scales_lambda": [
            1.0,
            3.0,
            5.0
          ],
          "energies_vs_lambda": [
            -1.812895,
            -1.725694,
            -1.648692
          ],
          "error_noisy_mHa": 44.38,
          "error_zne_mHa": 5.029,
          "error_reduction_frac": 0.887,
          "depolarizing_p": 0.04,
          "backend": {
            "active_backend": "default",
            "ran_on_real_hardware": false,
            "how_to_use_real_qc": "set KODEX_QC_BACKEND=ibm and KODEX_QC_TOKEN=<your IBM Quantum token> to run this exact circuit on real quantum hardware",
            "honest_timeline": "fault-tolerant quantum ADVANTAGE for fusion kernels is ~8-10 yr out \u2014 a meaningful role in fusion only after ~2036; this tooling makes the TESTING real and runnable TODAY, same code path"
          }
        },
        "readout_mitigation": {
          "readout_bitflip_p": 0.08,
          "tv_distance_noisy": 0.1472,
          "tv_distance_mitigated": 0.0,
          "tv_reduction_frac": 1.0,
          "method": "inverse confusion-matrix (linear) readout correction"
        },
        "backend": {
          "active_backend": "default",
          "ran_on_real_hardware": false,
          "how_to_use_real_qc": "set KODEX_QC_BACKEND=ibm and KODEX_QC_TOKEN=<your IBM Quantum token> to run this exact circuit on real quantum hardware",
          "honest_timeline": "fault-tolerant quantum ADVANTAGE for fusion kernels is ~8-10 yr out \u2014 a meaningful role in fusion only after ~2036; this tooling makes the TESTING real and runnable TODAY, same code path"
        },
        "verdict": "REAL error mitigation on a fusion quantum circuit: ZNE cuts the noisy-energy error 44.38 -> 5.029 mHa (89% of the error removed) and inverse-confusion readout correction cuts the measurement TV distance 0.1472 -> 0.0 (100%). Mitigation is the REAL NISQ tool today \u2014 it recovers signal from noise. HONEST: it is NOT a speedup; quantum's meaningful role in fusion is post-~2036, and mitigation cost grows with noise. Runs on real hardware with KODEX_QC_BACKEND=ibm."
      },
      "caveats": [
        "no quantum advantage \u2014 quantum's role in fusion is post-~2036",
        "ZNE assumes a smooth noise->observable relation; it extrapolates, it does not eliminate noise, and its overhead grows as noise grows",
        "readout mitigation uses a known confusion matrix; on hardware you must calibrate it"
      ]
    }
  },
  "KOIL": {
    "card": {
      "name": "KOIL",
      "function": "MAGNET",
      "status": "BUILT",
      "phase": 1,
      "provenance": "SIM",
      "retired_by": "full FE electromechanical solve",
      "gates": [
        "BR-MT1",
        "BR-MT3"
      ],
      "note": "magnet-twin stress-field surrogate + quench-precursor early-warning; REBCO per-turn Wilson-form hoop stress",
      "available": true
    },
    "benchmark": {
      "member": "KOIL",
      "live_strain_surrogate": {
        "rel_l2": 0.0038,
        "r2": 1.0,
        "n_turns": 82
      },
      "sourced_mt1": {
        "file": "track_MT1_magnet_twin/ac_mt1_verdict.csv",
        "headline": "surrogate 0.24% rel-L2 @ 0.15 ms p99 (MET)"
      },
      "sourced_mt3_quench": {
        "file": "track_MT3_quench/quench_precursor_metrics.csv",
        "headline": "quench precursor 47.2 ms lead, AUC 0.9998, FAR 2.4%"
      }
    }
  },
  "KORE": {
    "card": {
      "name": "KORE",
      "function": "EQUIL",
      "status": "BUILT",
      "phase": 1,
      "provenance": "ANALYTIC",
      "retired_by": "classical Grad-Shafranov solver",
      "gates": [
        "AC-16",
        "AC-L1"
      ],
      "note": "fast learned MHD-equilibrium accelerator (neural surrogate, 3-seed ensemble); Grad-Shafranov, not turbulence",
      "available": true
    },
    "benchmark": {
      "member": "KORE",
      "live_learned_surrogate": {
        "rel_l2_vs_analytic": 0.0035,
        "ensemble_cov90": 0.778,
        "infer_ms": 0.318,
        "grid": "16x16",
        "n_heldout": 120
      },
      "sourced": {
        "file": "track_L1_neural_operator/operator_vs_pinn.csv",
        "headline": "track FNO best 2.5% rel-L2 vs 1% bar (MISSED); ~1 s classical solver -> sub-ms surrogate"
      }
    }
  },
  "KPATH": {
    "card": {
      "name": "KPATH",
      "function": "operational",
      "status": "BUILT",
      "phase": 2,
      "provenance": "TWIN",
      "retired_by": "operational scenario optimizer (device-validated)",
      "gates": [],
      "note": "operational controller surrogate \u2014 (state, setpoint) -> command; imitation of the MPC over the closed-loop control traces",
      "available": true
    },
    "benchmark": {
      "member": "KPATH",
      "live": {
        "r2_mean_over_6_axes": 0.879,
        "n_test": 1960,
        "note": "imitation of the MPC command law"
      },
      "sourced": "track5_control/tracking_error.csv (MPC RMS <5% on all 6)"
    }
  },
  "KPILOT": {
    "card": {
      "name": "KPILOT",
      "function": "agentic-AI",
      "status": "BUILT",
      "phase": 3,
      "provenance": "n/a",
      "retired_by": "full LLM agent runtime over the fleet",
      "gates": [],
      "note": "fleet orchestration layer \u2014 routes a plain-language query to the right KODEX code and runs it; the LLM reasoning layer is the roadmap upgrade",
      "available": true
    },
    "benchmark": {
      "member": "KPILOT",
      "live_routing": {
        "what's the heat flux at gradient 3.5 shear 1.6?": "KYRO",
        "is this command inside the safe envelope?": "KGATE",
        "estimate the cost of electricity": "KECON",
        "optimize a machine design": "KFORGE",
        "which alloy should we use?": "KMAT"
      },
      "note": "keyword tool-router over the fleet; a full LLM agent is the roadmap upgrade"
    }
  },
  "KQERN": {
    "card": {
      "name": "KQERN",
      "function": "QKERNEL",
      "status": "BUILT",
      "phase": 2,
      "provenance": "SIM",
      "retired_by": "fault-tolerant quantum hardware (not available this decade)",
      "gates": [
        "KX-L3"
      ],
      "note": "REAL quantum-kernel classifier \u2014 embeds real MAST disruption features into a quantum feature map and classifies on the fidelity kernel (PennyLane); runs on a simulator now, real hardware pluggable. Honest: ties the classical kernel \u2014 a validated no-advantage result, but a real, runnable quantum-ML pipeline; role in fusion post-~2036",
      "available": true
    },
    "benchmark": {
      "member": "KQERN",
      "live_quantum_kernel": {
        "problem": "MAST disruption classification on a real quantum fidelity kernel",
        "framework": "PennyLane AngleEmbedding kernel + precomputed-kernel SVM",
        "quantum_kernel_auc": 0.919,
        "classical_rbf_auc": 0.929,
        "n_train": 44,
        "n_test": 20,
        "n_qubits": 4,
        "features": [
          "ip_mean_MA",
          "beta_n",
          "li",
          "q95"
        ],
        "quantum_minus_classical_auc": -0.01,
        "backend": {
          "active_backend": "default",
          "ran_on_real_hardware": false,
          "how_to_use_real_qc": "set KODEX_QC_BACKEND=ibm and KODEX_QC_TOKEN=<your IBM Quantum token> to run this exact circuit on real quantum hardware",
          "honest_timeline": "fault-tolerant quantum ADVANTAGE for fusion kernels is ~8-10 yr out \u2014 a meaningful role in fusion only after ~2036; this tooling makes the TESTING real and runnable TODAY, same code path"
        },
        "verdict": "REAL quantum kernel classifies MAST disruptions at AUC 0.919 vs classical RBF 0.929 (Delta -0.010) \u2014 no advantage: a validated null, but a REAL runnable quantum-ML pipeline on real fusion data. Runs on hardware with KODEX_QC_BACKEND=ibm.",
        "caveats": [
          "labels are the heuristic Ip-quench disruption labels (from KWARD)",
          "quantum kernel ties classical here \u2014 no advantage; the value is a real, hardware-ready quantum-ML tool, honest that advantage is ~8-10 yr out"
        ]
      },
      "sourced": {
        "file": "TRACK1_QUANTUM_RESULTS/track1_results.json (A6_quantum_kernel_MAST)",
        "note": "independent Track-1 run also found quantum-kernel AUC ~= classical (null)"
      }
    }
  },
  "KQOPT": {
    "card": {
      "name": "KQOPT",
      "function": "QOPT",
      "status": "BUILT",
      "phase": 3,
      "provenance": "SIM",
      "retired_by": "fault-tolerant quantum hardware (not available this decade)",
      "gates": [
        "KX-L3"
      ],
      "note": "REAL QAOA quantum optimizer \u2014 solves a combinatorial design QUBO on an actual quantum circuit (PennyLane); runs on a simulator now, real hardware pluggable, and you can plug in your own QUBO. Honest: standard ~0.7-1.0 approx ratio, no speedup at this size (advantage post-~2036)",
      "available": true
    },
    "benchmark": {
      "member": "KQOPT",
      "live_qaoa": {
        "problem": "6-node design QUBO (MaxCut-style; plug in your own Q matrix)",
        "framework": "PennyLane QAOA (p=2); runs on simulator now, real hardware pluggable",
        "qaoa_cut": 6,
        "optimal_cut": 6,
        "approx_ratio": 1.0,
        "p": 2,
        "n_qubits": 6,
        "solution_bitstring": [
          0,
          1,
          0,
          1,
          0,
          1
        ],
        "backend": {
          "active_backend": "default",
          "ran_on_real_hardware": false,
          "how_to_use_real_qc": "set KODEX_QC_BACKEND=ibm and KODEX_QC_TOKEN=<your IBM Quantum token> to run this exact circuit on real quantum hardware",
          "honest_timeline": "fault-tolerant quantum ADVANTAGE for fusion kernels is ~8-10 yr out \u2014 a meaningful role in fusion only after ~2036; this tooling makes the TESTING real and runnable TODAY, same code path"
        },
        "verdict": "REAL QAOA reaches 100% of the brute-force optimum (cut 6/6, p=2, 6 qubits) on the default backend \u2014 a working quantum optimizer, runnable on hardware with KODEX_QC_BACKEND=ibm. HONEST: no speedup vs classical at this size; advantage is ~8-10 yr out."
      },
      "sourced": {
        "file": "TRACK1_QUANTUM_RESULTS/track1_results.json (A4_QAOA)",
        "note": "independent Track-1 p=1 QAOA reached ~66% on an 8-node QUBO (consistent)"
      }
    }
  },
  "KQROSS": {
    "card": {
      "name": "KQROSS",
      "function": "QRE",
      "status": "BUILT",
      "phase": 2,
      "provenance": "SIM",
      "retired_by": "fault-tolerant quantum hardware (not available this decade)",
      "gates": [
        "AC-43",
        "BR-SX-08"
      ],
      "note": "REAL fault-tolerant resource estimator \u2014 computes the classical<->quantum crossover N, the surface-code physical-qubit overhead, and the logical-qubit roadmap year for a fusion electronic-structure kernel (order-of-magnitude, literature-scaled). Verdict: no FT advantage this decade \u2014 the machine does not exist yet; meaningful role post-~2036",
      "available": true
    },
    "benchmark": {
      "member": "KQROSS",
      "live_resource_estimate": {
        "method": "surface-code overhead (d=25) + qubitization T-counts + classical exact-CI, order-of-magnitude literature-scaled",
        "crossover_N_spin_orbitals": 50,
        "logical_qubits_needed": 50,
        "physical_qubits_needed": "~6e+04",
        "surface_code_phys_per_logical": 1250,
        "quantum_runtime_hours_at_crossover": 0.03,
        "roadmap_year_reach_logical": 2032,
        "hardware_today": "~1e2-1e3 physical qubits, no error-corrected logical qubits",
        "verdict": "REAL FT resource estimate: classical<->quantum crossover at N~50 spin-orbitals needs 50 logical -> ~6e+04 physical qubits and ~0.03 h/run. Today's hardware = ~1e2-1e3 physical qubits, no error-corrected logical qubits; the roadmap reaches that logical count ~2032. NO fault-tolerant quantum advantage for fusion this decade \u2014 a validated negative result."
      },
      "caveat": "MANDATORY: no quantum advantage this decade (order-of-magnitude estimate)",
      "sourced": {
        "file": "TRACK1_QUANTUM_RESULTS/track1_results.json (A1/A3/A9)",
        "note": "independent Track-1: crossover N~40, ~1e5-1e6 phys qubits, mid/late-2030s"
      }
    }
  },
  "KQUBIT": {
    "card": {
      "name": "KQUBIT",
      "function": "QML",
      "status": "BUILT",
      "phase": 1,
      "provenance": "SIM",
      "retired_by": "fault-tolerant quantum hardware (not available this decade)",
      "gates": [
        "AC-44",
        "KX-L3-A4"
      ],
      "note": "REAL variational quantum eigensolver (VQE) for a molecular Hamiltonian \u2014 a runnable quantum program (PennyLane): executes on a simulator now and on real quantum hardware when you plug in a backend. Honest: no quantum advantage yet (~8-10 yr out); the tooling + testing are real TODAY \u2014 quantum's role in fusion is post-~2036",
      "available": true
    },
    "benchmark": {
      "member": "KQUBIT",
      "live_vqe": {
        "problem": "H2 molecular Hamiltonian (2-qubit parity-reduced; standard coeffs)",
        "framework": "PennyLane (runnable on simulator now; real hardware pluggable)",
        "vqe_energy_Ha": -1.857275,
        "exact_energy_Ha": -1.857275,
        "recovery_mHa": 0.0002,
        "n_qubits": 2,
        "steps": 120,
        "reaches_chemical_accuracy": true,
        "nisq_noise_sweep_mHa_error": {
          "0.0": 0.0,
          "0.001": 1.112,
          "0.005": 5.557,
          "0.01": 11.11,
          "0.02": 22.211,
          "0.05": 55.449
        },
        "noise_note": "depolarizing-noise sweep (mHa error vs per-qubit p): NISQ noise breaks the 1.6 mHa chemical accuracy fast \u2014 this is WHY there is no advantage yet",
        "backend": {
          "active_backend": "default",
          "ran_on_real_hardware": false,
          "how_to_use_real_qc": "set KODEX_QC_BACKEND=ibm and KODEX_QC_TOKEN=<your IBM Quantum token> to run this exact circuit on real quantum hardware",
          "honest_timeline": "fault-tolerant quantum ADVANTAGE for fusion kernels is ~8-10 yr out \u2014 a meaningful role in fusion only after ~2036; this tooling makes the TESTING real and runnable TODAY, same code path"
        },
        "verdict": "REAL VQE recovers the H2 ground state to 0.0002 mHa on the default backend (within 1.6 mHa chemical accuracy). Runs on real quantum hardware when you set KODEX_QC_BACKEND=ibm. HONEST: no quantum advantage this decade \u2014 the value now is a real, testable quantum pipeline, not a speedup."
      },
      "sourced": {
        "file": "track8_vqe_poc/vqe_convergence.csv",
        "note": "prior noisy-VQE sweep: chemical accuracy breaks under NISQ noise (quantifies the no-advantage-this-decade caveat)"
      },
      "caveat": "MANDATORY: no quantum advantage this decade; hardware value is ~8-10 yr out"
    }
  },
  "KRAD": {
    "card": {
      "name": "KRAD",
      "function": "radiation-control",
      "status": "BUILT",
      "phase": 3,
      "provenance": "SIM",
      "retired_by": "impurity transport (SOLPS + impurity) + radiation control",
      "gates": [
        "H9"
      ],
      "note": "impurity-seeding radiation-control evaluator \u2014 maps a seeded impurity + radiated-power fraction to core Zeff penalty and P_rad from the H9 seeding scan. Small scan; full impurity-transport (SOLPS + impurity) = fidelity upgrade",
      "available": true
    },
    "benchmark": {
      "member": "KRAD",
      "live_impurity_seeding": {
        "source": "h9_seeding.csv (H9 impurity-seeding scan)",
        "n_impurities": 3,
        "impurities": [
          "Ar",
          "N",
          "Ne"
        ],
        "radiated_fraction": 0.58,
        "least_core_dilution_impurity": "Ar",
        "verdict": "Impurity-seeding radiation evaluator over 3 impurities at f_rad=0.58: 'Ar' gives the least core Zeff penalty (dZeff_hi=0.1102). Small scan \u2014 full impurity-transport (SOLPS) is the fidelity upgrade."
      },
      "caveat": "small seeding scan (few impurities); reduced 0-D radiation model"
    }
  },
  "KSCOUT": {
    "card": {
      "name": "KSCOUT",
      "function": "MF-CAMPAIGN",
      "status": "BUILT",
      "phase": 3,
      "provenance": "SIM",
      "retired_by": "the full multi-fidelity CGYRO campaign (twin + mu=400 + real-mass gold)",
      "gates": [
        "BR-L2-A1e"
      ],
      "note": "multi-fidelity Bayesian-optimization campaign manager \u2014 extends KSEEK: chooses which operating point AND which fidelity (reduced twin / mu=400 CGYRO / real-mass gold) to run next, greedily maximizing truth-weighted information per GPU-hour under a budget",
      "available": true
    },
    "benchmark": {
      "member": "KSCOUT",
      "live_mf_campaign": {
        "source": "KYRO GP over the completed 16-pt CGYRO A1e map",
        "budget_gpu_h": 15.0,
        "target_hotspots": 16,
        "policy": "twin-triage every hotspot, escalate most-uncertain to mu=400, reserve gold",
        "fidelities": {
          "twin": {
            "gpu_h": 0.0,
            "rho": 0.55,
            "desc": "reduced analytic twin (near-free triage)"
          },
          "cgyro_mu400": {
            "gpu_h": 2.89,
            "rho": 0.9,
            "desc": "CGYRO representative mu=400 (measured GPU-h)"
          },
          "gold_mu3672": {
            "gpu_h": 28.9,
            "rho": 1.0,
            "desc": "CGYRO real-mass gold (est. ~10x mu=400; not yet run)"
          }
        },
        "multi_fidelity_plan": [
          {
            "a_LT": 3.25,
            "shear": 1.4,
            "fidelity": "cgyro_mu400",
            "gpu_h": 2.99,
            "gp_std": 0.5041,
            "truth_confidence_rho": 0.9
          },
          {
            "a_LT": 2.25,
            "shear": 1.4,
            "fidelity": "cgyro_mu400",
            "gpu_h": 2.99,
            "gp_std": 0.5041,
            "truth_confidence_rho": 0.9
          },
          {
            "a_LT": 3.25,
            "shear": 0.6,
            "fidelity": "cgyro_mu400",
            "gpu_h": 2.99,
            "gp_std": 0.5041,
            "truth_confidence_rho": 0.9
          },
          {
            "a_LT": 2.25,
            "shear": 0.6,
            "fidelity": "cgyro_mu400",
            "gpu_h": 2.99,
            "gp_std": 0.5041,
            "truth_confidence_rho": 0.9
          },
          {
            "a_LT": 3.25,
            "shear": 1.0,
            "fidelity": "twin",
            "gpu_h": 0.1,
            "gp_std": 0.4831,
            "truth_confidence_rho": 0.55
          },
          {
            "a_LT": 2.25,
            "shear": 1.0,
            "fidelity": "twin",
            "gpu_h": 0.1,
            "gp_std": 0.4831,
            "truth_confidence_rho": 0.55
          },
          {
            "a_LT": 2.75,
            "shear": 0.6,
            "fidelity": "twin",
            "gpu_h": 0.1,
            "gp_std": 0.4831,
            "truth_confidence_rho": 0.55
          },
          {
            "a_LT": 2.75,
            "shear": 1.4,
            "fidelity": "twin",
            "gpu_h": 0.1,
            "gp_std": 0.4831,
            "truth_confidence_rho": 0.55
          },
          {
            "a_LT": 2.75,
            "shear": 1.0,
            "fidelity": "twin",
            "gpu_h": 0.1,
            "gp_std": 0.4623,
            "truth_confidence_rho": 0.55
          },
          {
            "a_LT": 2.25,
            "shear": 0.4,
            "fidelity": "twin",
            "gpu_h": 0.1,
            "gp_std": 0.3838,
            "truth_confidence_rho": 0.55
          },
          {
            "a_LT": 2.25,
            "shear": 1.6,
            "fidelity": "twin",
            "gpu_h": 0.1,
            "gp_std": 0.3838,
            "truth_confidence_rho": 0.55
          },
          {
            "a_LT": 3.25,
            "shear": 1.6,
            "fidelity": "twin",
            "gpu_h": 0.1,
            "gp_std": 0.3838,
            "truth_confidence_rho": 0.55
          },
          {
            "a_LT": 3.25,
            "shear": 0.4,
            "fidelity": "twin",
            "gpu_h": 0.1,
            "gp_std": 0.3838,
            "truth_confidence_rho": 0.55
          },
          {
            "a_LT": 2.0,
            "shear": 1.4,
            "fidelity": "twin",
            "gpu_h": 0.1,
            "gp_std": 0.3838,
            "truth_confidence_rho": 0.55
          },
          {
            "a_LT": 3.5,
            "shear": 1.4,
            "fidelity": "twin",
            "gpu_h": 0.1,
            "gp_std": 0.3838,
            "truth_confidence_rho": 0.55
          },
          {
            "a_LT": 2.0,
            "shear": 0.6,
            "fidelity": "twin",
            "gpu_h": 0.1,
            "gp_std": 0.3838,
            "truth_confidence_rho": 0.55
          }
        ],
        "mf_runs": 16,
        "mf_gpu_h_used": 13.56,
        "mf_runs_by_fidelity": {
          "twin": 12,
          "cgyro_mu400": 4,
          "gold_mu3672": 0
        },
        "mf_mean_confidence_rho": 0.638,
        "baseline_all_mu400": {
          "n_runs": 5,
          "gpu_h_used": 14.95,
          "mean_confidence_rho": 0.281,
          "unvisited_hotspots": 11
        },
        "mf_confidence_gain_x_vs_all_mu400": 2.27,
        "verdict": "Under a tight 15.0 GPU-h budget across 16 uncertain operating points, the multi-fidelity policy triages ALL 16 with cheap twin runs and escalates 4 to mu=400 (0 to real-mass gold), reaching mean truth-confidence rho 0.638 \u2014 vs 0.281 for a naive all-mu=400 plan that exhausts the budget after 5 points and leaves 11 hotspots unexamined (2.27x). A real cost-aware campaign manager: look everywhere cheaply, spend the expensive fidelity where KYRO is least sure."
      },
      "caveats": [
        "the per-fidelity rho (correlation-with-truth) values are ASSUMPTIONS \u2014 the real-mass gold has not been run \u2014 so this is a planning heuristic, not a measured speedup; the advantage holds only while budget < hotspots x mu=400 cost",
        "GP std comes from only 16 points (directional, like KSEEK)",
        "GPU-hours only; no compute-cost figures (product-econ rule)"
      ]
    }
  },
  "KSEEK": {
    "card": {
      "name": "KSEEK",
      "function": "ACTIVE",
      "status": "BUILT",
      "phase": 3,
      "provenance": "SIM",
      "retired_by": "the full CGYRO parameter scan (once every point is simulated)",
      "gates": [
        "BR-L2-A1e"
      ],
      "note": "active-learning acquisition \u2014 proposes the NEXT most-informative CGYRO run from KYRO's GP posterior (max-variance / uncertainty sampling), so expensive GPU-hours go where the surrogate is least sure. A real experimental-design tool",
      "available": true
    },
    "benchmark": {
      "member": "KSEEK",
      "live_active_learning": {
        "source": "KYRO GP over the completed 16-pt CGYRO A1e map",
        "acquisition": "max GP posterior std (uncertainty sampling), min-separation filtered",
        "next_runs": [
          {
            "a_LT": 2.225,
            "shear": 0.58,
            "gp_std": 0.5046
          },
          {
            "a_LT": 3.275,
            "shear": 0.58,
            "gp_std": 0.5046
          },
          {
            "a_LT": 3.275,
            "shear": 1.42,
            "gp_std": 0.5046
          }
        ],
        "loo_std_vs_error_corr": -0.283,
        "cgyro_gpu_h_per_point": 2.89,
        "verdict": "Proposes the next CGYRO run at a/L_T=2.225, shear=0.58 (highest GP uncertainty). Leave-one-out: GP posterior-std vs actual error correlation = -0.283 \u2014 weak on this small map. A real experimental-design tool: spend ~2.9 GPU-h/point where it matters."
      },
      "caveat": "16 training points is small \u2014 acquisition is directional guidance, not a guarantee"
    }
  },
  "KSENSE": {
    "card": {
      "name": "KSENSE",
      "function": "QSENSE",
      "status": "BUILT",
      "phase": 2,
      "provenance": "SIM",
      "retired_by": "deployed physical diagnostic hardware",
      "gates": [
        "HX-29"
      ],
      "note": "REAL quantum-metrology evaluation (PennyLane mixed-state) \u2014 GHZ interferometry: ideal gives Heisenberg (gain ~ N) scaling, but realistic dephasing (NV/SQUID/SERF reality) collapses it back to the standard quantum limit (~sqrt(N)). Honest null where fusion disruptions live; quantum's role in fusion is post-~2036",
      "available": true
    },
    "benchmark": {
      "member": "KSENSE",
      "live_quantum_metrology": {
        "method": "PennyLane mixed-state GHZ interferometry; Heisenberg vs SQL vs dephasing",
        "dephasing_per_qubit": 0.2,
        "scaling": [
          {
            "N": 2,
            "visibility_dephased": 0.8,
            "GHZ_ideal_gain": 2.0,
            "GHZ_dephased_gain": 1.6,
            "SQL_gain": 1.41
          },
          {
            "N": 3,
            "visibility_dephased": 0.716,
            "GHZ_ideal_gain": 3.0,
            "GHZ_dephased_gain": 2.15,
            "SQL_gain": 1.73
          },
          {
            "N": 4,
            "visibility_dephased": 0.64,
            "GHZ_ideal_gain": 4.0,
            "GHZ_dephased_gain": 2.56,
            "SQL_gain": 2.0
          },
          {
            "N": 5,
            "visibility_dephased": 0.572,
            "GHZ_ideal_gain": 5.0,
            "GHZ_dephased_gain": 2.86,
            "SQL_gain": 2.24
          },
          {
            "N": 6,
            "visibility_dephased": 0.512,
            "GHZ_ideal_gain": 6.0,
            "GHZ_dephased_gain": 3.07,
            "SQL_gain": 2.45
          },
          {
            "N": 7,
            "visibility_dephased": 0.458,
            "GHZ_ideal_gain": 7.0,
            "GHZ_dephased_gain": 3.21,
            "SQL_gain": 2.65
          },
          {
            "N": 8,
            "visibility_dephased": 0.41,
            "GHZ_ideal_gain": 8.0,
            "GHZ_dephased_gain": 3.28,
            "SQL_gain": 2.83
          }
        ],
        "gain_growth_N2_to_N8": {
          "GHZ_ideal": 4.0,
          "GHZ_dephased": 2.05,
          "SQL": 2.01
        },
        "analytic_turnover_N": 8,
        "backend": {
          "active_backend": "default",
          "ran_on_real_hardware": false,
          "how_to_use_real_qc": "set KODEX_QC_BACKEND=ibm and KODEX_QC_TOKEN=<your IBM Quantum token> to run this exact circuit on real quantum hardware",
          "honest_timeline": "fault-tolerant quantum ADVANTAGE for fusion kernels is ~8-10 yr out \u2014 a meaningful role in fusion only after ~2036; this tooling makes the TESTING real and runnable TODAY, same code path"
        },
        "verdict": "REAL quantum-metrology computation: over N=2->8 the ideal GHZ gain grows 4.0x (Heisenberg ~ N), but with per-qubit dephasing (p=0.2) the dephased GHZ grows only 2.05x \u2014 essentially the SQL rate (2.01x, ~sqrt(N)). Dephasing ERASES the Heisenberg *scaling* to a bounded constant (gain turns over near N~9). HONEST NULL: no quantum-sensing advantage for fusion disruptions this decade \u2014 but a real, runnable metrology tool."
      },
      "sourced": {
        "file": "TRACK1_QUANTUM_RESULTS/track1_results.json (A7_metrology)",
        "note": "independent Track-1: correlated dephasing erases GHZ Heisenberg back to SQL"
      }
    }
  },
  "KTENSOR": {
    "card": {
      "name": "KTENSOR",
      "function": "TN",
      "status": "BUILT",
      "phase": 2,
      "provenance": "SIM",
      "retired_by": "exact many-body / quantum simulation",
      "gates": [
        "KX-L3"
      ],
      "note": "tensor-network / low-rank ROM compression of the real CGYRO A1e flux database (classical MPS-style SVD; a many-body method and bridge to quantum kernels \u2014 NO quantum-advantage claim; quantum's role in fusion is post-~2036)",
      "available": true
    },
    "benchmark": {
      "member": "KTENSOR",
      "live_low_rank_rom": {
        "source": "real CGYRO A1e flux database (data.cgyro_flux_map_final)",
        "columns": [
          "a_LT",
          "shear",
          "Q_i",
          "Q_e"
        ],
        "full_16pt_map": {
          "shape": [
            16,
            4
          ],
          "rel_error_vs_bond_dim": {
            "1": 0.5903,
            "2": 0.3009,
            "3": 0.1214,
            "4": 0.0
          },
          "cumulative_variance": {
            "1": 0.6515,
            "2": 0.9094,
            "3": 0.9853,
            "4": 1.0
          },
          "effective_rank": 2.93
        },
        "turbulent_branch": {
          "shape": [
            12,
            4
          ],
          "rel_error_vs_bond_dim": {
            "1": 0.4964,
            "2": 0.2879,
            "3": 0.1304,
            "4": 0.0
          },
          "cumulative_variance": {
            "1": 0.7535,
            "2": 0.9171,
            "3": 0.983,
            "4": 1.0
          },
          "effective_rank": 2.75
        },
        "verdict": "MEASURED ROM characterization: the CGYRO flux database is only MODESTLY compressible \u2014 effective rank ~2.93 of 4; a bond-dim-2 MPS keeps 91% of the variance but 30% rel-L2 error (bond-dim 3 -> 12%). The turbulent flux spans 3+ orders of magnitude with a sharp turbulent<->quiet transition, so it RESISTS dramatic low-rank compression. An honest counter to the naive 'flux is trivially low-rank' expectation. Classical SVD/MPS; NO quantum-advantage claim.",
        "caveats": [
          "NOT a dramatic low-rank collapse: bond-dim-2 keeps ~91% variance but ~30% rel-L2 error",
          "classical SVD/MPS ROM of real CGYRO data \u2014 a many-body / quantum-bridge method, NOT a quantum-advantage claim",
          "the 9-pt regen flux DB is mostly NaN (a sparse linear scan); the completed 16-pt map is the honest dataset used here"
        ]
      },
      "method": "SVD low-rank / MPS bond-dimension compression (deterministic, CPU)",
      "sourced": {
        "file": "TRACK1_QUANTUM_RESULTS/track1_results.json (A5_MPS_flux)",
        "note": "the Track-1 'rank-2 ~1%' figure was on the sparse 9x4 regen table; on the complete map the flux is not strongly low-rank (this card)"
      }
    }
  },
  "KWARD": {
    "card": {
      "name": "KWARD",
      "function": "DISRUPT",
      "status": "BUILT",
      "phase": 1,
      "provenance": "REAL-ANCHOR",
      "retired_by": "physicist-verified disruption labels + full precursor diagnostics",
      "gates": [
        "AC-20",
        "AC-21"
      ],
      "note": "disruption early-warning \u2014 real-device (592 MAST shots), calibrated ensemble + per-shot OOD gate; advisory, downstream of the KGATE clamp",
      "available": true
    },
    "benchmark": {
      "member": "KWARD",
      "live_real_device": {
        "source": "592 real MAST shots (FAIR-MAST), fixed 50 ms window",
        "auc": 0.98,
        "auc_precursor_only_no_Ip_variability": 0.984,
        "ece": 0.0349,
        "n_shots": 591,
        "n_disruptive": 276,
        "n_test_shots": 178,
        "features": [
          "ip_mean_MA",
          "ip_std",
          "ip_slope",
          "ip_range",
          "ne_mean",
          "greenwald_proxy",
          "beta_n",
          "li",
          "q95",
          "n1_rms_mean",
          "n1_rms_slope",
          "prad_mean",
          "prad_slope"
        ],
        "caveats": [
          "labels DERIVED from Ip current-quench (heuristic, not physicist-verified)",
          "fixed-window physics features only (no duration leakage)",
          "Ip-variability features SHARE information with the Ip-derived label; the independent_precursor_only_auc below (n=1 Mirnov + P_rad ONLY) is the label-independent number and is the one to cite",
          "'n1_rms' is a broadband Mirnov fluctuation RMS (low-n MHD-activity proxy), NOT a toroidal-n=1 Fourier decomposition (array geometry on disk lacks toroidal-angle metadata)",
          "EFIT kinetics sparse (~16%); n=1 Mirnov ~99% / P_rad ~78% shot coverage (FAIR-MAST, pulled 2026-09-10)"
        ],
        "independent_precursors_present": true,
        "independent_precursor_only_auc": 0.975,
        "note_independent": "n=1 Mirnov + P_rad present -> this AUC uses ONLY diagnostics independent of Ip and the Ip-derived label"
      },
      "machinery_check_synth": {
        "auc": 0.916
      },
      "sourced_twin": {
        "file": "track4_disruption/disruption_metrics.csv",
        "note": "prior twin-synthetic result AUC 0.990 (superseded by real-device above)"
      }
    }
  },
  "KYRO": {
    "card": {
      "name": "KYRO",
      "function": "TRANSPORT",
      "status": "BUILT",
      "phase": 1,
      "provenance": "CGYRO-urep",
      "retired_by": "CGYRO (nonlinear gyrokinetic)",
      "gates": [
        "BR-L2-A12",
        "BR-L2-A1e",
        "BR-L2-A1c"
      ],
      "note": "CGYRO turbulence-transport surrogate over the complete 16/16 A1e map (total heat flux Q_tot + turbulent/quiet); mu=400 rep, real-mass gold deferred",
      "available": true
    },
    "benchmark": {
      "member": "KYRO",
      "accuracy": {
        "r2_log_Qtot": 0.858,
        "rmse_log_Qtot": 0.857,
        "coverage_90": 0.875,
        "turbulent_quiet_LOO": "16/16 correct",
        "map": "12 turbulent / 4 quiet (16/16 complete)"
      },
      "speed": {
        "surrogate_ms_per_point": 0.257,
        "cgyro_gpu_h_per_point_measured": 2.89,
        "speedup_x_vs_cgyro": "4.1e+07",
        "speedup_note": "ms inference vs GPU-hours for the CONVERGED flux value",
        "fidelity": "representative mu=400; real-mass gold deferred"
      },
      "provenance": "COMPLETE 16/16 CGYRO A1e map (mu=400); operating point subcritical"
    }
  }
}