Build on the full-stack platform that ingests brain–computer interface data streams, reconstructs an executable quantum-classical state model, and routes that state into agentic workflows that generate hypotheses, triage risks, and recommend next actions — for learning, and for neurological disorder research.
The Quantum Virtual Mind (QVM) learns the dynamics in a person’s BCI recordings. Everything else is a question about how those dynamics depart from their own baseline — upward into learning, or downward into disorder.
WHERE THE WORK STANDS
Every number below is something we can point at today, not a forecast.
The Quantum Virtual Mind, built and benchmarked in house against a classical baseline on public EEG data.
Named roles across the lifecycle, from data access through to designing what to do next.
Superlearning and clinical research — the same perturbation measured in opposite directions.
In preparation, covering the foundation model, the healthy atlas, and the clinical record.
Early benchmark — in-house
Public brain-recording data. Full detail in the manuscripts, in preparation.
We put the state model head to head with a conventional method on public brain-recording data. It recovered real physical structure markedly better, and held physical consistency exactly — while conventional methods still lead on short-horizon signal prediction.
Where this sits
This is an early in-house benchmark and the first of a planned series. Three manuscripts are in preparation, and independent replication on larger cohorts is the next milestone.
RESEARCH ROADMAP
Sequenced by what each stage depends on. Restoration comes first because consumer distribution already exists and the bar for a useful signal is lowest; clinical work comes last because it needs years of longitudinal data before anything can be claimed.
Dates from 2027 onward are targets for research programmes still at proposal stage, not delivery commitments.
We are looking for research collaborators, a first education pilot partner, and investors who want to look closely at the science.