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Regime Transition Probability (7d) (M10121)

Metric IDM10121
CategoryAI
Accesspublic
Classificationproprietary
ComputationClassical + Quantum (hardware) + Quantum (simulated)
Computed byBlack Belt Labs
Freshnessfresh
Last refreshed

Calculation methods

MethodLast refreshed
Classical
Quantum (simulated)
Quantum (hardware)

This is a proprietary Madjik metric. Its methodology is a trade secret; the values are available through the API.

Quantum configuration

Every quantum calculation of this metric, with the machine and circuit setup it runs on. Hardware values come from real IBM quantum computers; simulator values from a classical simulation of the same kind of circuit.

MethodQuantum computing on hardware (quant-hw)
ProviderIBM Quantum Platform (Open plan)
Machineibm_marrakesh (156 qubits)
Machine typesuperconducting QPU (IBM Heron)
Qubits used4
Circuits per run1
Shots per run8,192
Circuit depth63
Two-qubit gates14
Gate basiscz, rz, sx, x (IBM Heron native)
Algorithmdirect amplitude readout on hardware (shallow profile)
Profileshallow
Error mitigationdynamical decoupling + measurement twirling; no post-hoc correction
Hardware runs2
Last run2026-10-05T06:30:50
IBM job iddb1k3i3id5ic73er8ccg
How measuredIBM job record (transpiled circuit stats)
MethodQuantum computing simulated (quant-sim)
ProviderQiskit Aer simulator (IBM open-source software) on Google Cloud
Machineaer_simulator
Machine typeclassical simulator (CPU, noise-free)
Qubits used6
Circuits per run4
Shots per run8,192
Circuit depth1521
Two-qubit gates858
Gate basisu, cx (Aer, flattened)
Algorithmquantum amplitude estimation (MLAE) of the 7-day regime-change frequency
Profilefull
Error mitigationnone (noise-free simulation)
How measuredrecorded circuits, compiled to u/cx

Personal metrics: figures are the maximum across submitted portfolios (circuit size follows portfolio size). Machine qubits is the size of the whole chip; qubits used is what this calculation needs.

Method comparison (experimental)

The same metric computed several ways on the same frozen inputs, one change at a time, so a gap between two rows has a known cause. Each plus/minus is the standard error of that value. How to read this, and how it scales.

Daily simulator-only comparison

Snapshot 20261016T0002_224e50bf · 2026-10-16 00:02 UTC

StepMethodValue
Classical, exactclassical61
Production value (-0.1 h from the snapshot)classical20
Quantum simulator, idealquantum simulated61
Quantum simulator, ideal (hardware-sized circuit)quantum simulated61
Quantum simulator, sampledquantum simulated61.2 ± 0.3801
Noisy device modelquantum simulated60 ± 0.4221

Scored against what actually happened

Probability forecast. Outcome: 1 if the AI regime label (M50016) differs 7 days later. The label comes from the same AI, so this check is partly circular. Lower Brier score is better.

MethodResult
classicalBrier 0.0617 over 135 forecasts; 5 events; mean forecast 20.7% vs observed 3.7%
quantum hardwareBrier 0.1082 over 1 forecasts; 0 events; mean forecast 32.9% vs observed 0.0%
quantum simulatedBrier 0.1374 over 38 forecasts; 4 events; mean forecast 31.3% vs observed 10.5%

Quantum values are not expected to beat the classical value at these problem sizes. The purpose of this section is to show, with uncertainty, how and why the methods differ. Small samples: treat scores as indicative.

Hypotheses monitored by this metric

Access this metric

Query it via the Madjik API:

curl -H "X-API-Key: $MADJIK_API_KEY" \
  https://api.madjik.io/v1/metrics/M10121

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