Our work on quantum reservoir scaling and its theoretical limits, alongside the literature that informs the wider programme.
The two QMikeAI research papers below are Daniel Mo Houshmand’s preprints. Related publications are credited to their authors and are not QMikeAI results.
An empirical study of the training data needed to turn quantum reservoir measurements into useful predictions.
A versioned preprint, research code and a later hardware-provenance correction are public. The Zenodo deposit predates that correction; use the pinned repository record for corrected hardware attribution.
These results do not establish weather-forecast skill or a general quantum advantage. A registered preprint is not evidence of peer review.
The Zenodo deposit has a registered version. The separate SSRN record has no verified manuscript version; consult the corrected provenance for hardware attribution.
A theoretical study of the tension between reversible quantum evolution and the fading of a reservoir’s initial state.
The original no-go preprint and revised tension preprint share one Zenodo version lineage. They are versions of the same research record, not independent studies.
The argument depends on the stated dynamics and echo-state assumptions. It is not a blanket prohibition on dissipative or measurement-assisted quantum reservoirs.
Versions matter. The original no-go and revised tension preprints belong to one Zenodo lineage. The separate SSRN records have a registry year of 2026, with no verified manuscript version. For the scaling study, consult the pinned corrected hardware provenance before comparing archived claims.
Research software
Inspect the simulation assumptions.
FakeNovera and FakeCepheus are independently developed, device-inspired simulation tools. They are not an official Rigetti provider or evidence of a hardware run.
Natalie Wolchover · Quanta Magazine · 18 April 2018.
An accessible introduction to classical reservoir forecasting and the questions behind the primary Pathak studies above. It is context for the research programme, rather than evidence of a QMikeAI result.
