GBS graph ranking
The veterinary knowledge graph can be anonymized into node IDs and weights for Gaussian boson sampling clique search.
- No patient text sent to the quantum service
- Weighted clique ranking
- Classical fallback when unavailable
QIVS screening
Quantum inverse virtual screening stores hashed drug inputs and derived binding-pose outputs for veterinary AMR pathogen research.
- SMILES hashed before storage
- Target pathogen seed set
- Quantum advantage tracked per run
AMR RNA folding
RNA sequences are hashed, transformed into weighted full stem graphs, and evaluated for secondary-structure predictions.
- Raw sequence never persisted
- WFSG node and edge counts stored
- MCC computed when references exist
Why this matters
VetIOS is built as infrastructure rather than a standalone chatbot. The platform connects structured veterinary inputs, graph priors, model execution, reliability signals, outcomes, simulations, and public-health research surfaces into one auditable loop.
Frequently asked questions
Does VetIOS use Jiuzhang directly?
No. Jiuzhang hardware is not publicly accessible. VetIOS uses accessible Gaussian boson sampling methods through a service layer designed to support Strawberry Fields and future photonic backends.
What quantum method does VetIOS use?
The current implementation focuses on Gaussian boson sampling for maximum weighted clique search, QIVS-style binding interaction graphs, and weighted full stem graphs for RNA folding.
Is patient data sent to the quantum service?
No. Clinical graph ranking sends anonymized node IDs and weights only. QIVS stores hashed SMILES strings, and RNA folding stores sequence hashes rather than raw sequences.