Testing hardware quantum machine learning claims with a null circuit: fill prediction on Binance futures with 129 qubits of an IBM Heron processor

arXiv:2610.41268v1 Announce Type: new Abstract: Quantum processors are now used as feature generators for classical models on tabular data. A circuit encodes each record, the device returns expectation values, and a classical model learns from them. A recent report claimed large gains from such features, gains that appeared only on noisy hardware. These claims are hard to check, because a quantum device is not a static function. Its state changes from job to job and from one calibration to the next, and that drift can leak information about the outcome into the features. We propose a five-step protocol for testing claims of this kind. It runs a circuit that carries no data in the same jobs as the real one. It checks whether the hardware leaves a recognizable mark on each job.