Quantum AI

A trained quantum neural network. Run it yourself.

Live on Brion GPU Statevector Simulator

Run the model

Four measurements go in as rotation angles on four qubits. The circuit runs on Brion GPU Statevector Simulator for 2048 shots, and the parity of the measured bits is the model's output. Nothing is cached — every press executes the circuit again, which is why two runs of the same input differ slightly.

sepal length (cm) —

sepal width (cm) —

petal length (cm) —

petal width (cm) —

Result

Press run. The measurement histogram below is the raw output of the circuit — the prediction is derived from it, not the other way round.

Model card

Every figure here is read from the deployed artifact, which was written by the training run itself. Accuracy is quoted next to what it has to beat.

Task	iris-versicolor-vs-virginica
Data	sklearn.datasets.load_iris (Fisher 1936, UCI)
Classes	versicolor, virginica
Held-out accuracy	96.7% on 30 samples
Majority-class baseline	50.0%
Logistic regression, same split	96.7%

Qubits × layers	4 × 3
Trained parameters	24
Readout	register_parity_z
Optimiser	SPSA
Circuits run in training	56,000
Trained on	brion-sim
Trained at	2026-08-19T20:48:06+00:00
Commit	13018a7

What this actually is

A variational quantum classifier. The four input measurements are written into the angles of RY rotations, one per qubit. A trained circuit of 24 rotation parameters and a ring of CNOT gates then acts on that state, and every qubit is measured. The model's output is the parity of the measured bits, averaged over 2048 shots — a single number between −1 and +1. Above the fitted threshold means one class, below means the other.

The CNOT ring is the part that makes this a quantum model rather than four independent rotations. Without it the circuit could not represent any correlation between the four features, and the parity readout would carry no more information than the features separately.

What it is not

It is not faster than a classical model at this task, and it does not beat one: logistic regression scores 96.7% on the same split, which is the same as the quantum model, on a laptop, instantly. Four qubits is a state space of sixteen amplitudes; a classical computer holds that without noticing. That number is on the model card because a benchmark quoted without its classical reference is not a result.

What it does demonstrate is a complete pipeline: parameters fitted by running real circuits, an artifact carrying its own provenance, and a served endpoint whose answer comes from a circuit executed at request time. That pipeline is the thing that scales to a problem where the quantum side might matter — the model on it today is a small one.

Run it from the API

curl -X POST https://brionquantum.com/api/v1/models/qnn-hybrid/inference \
  -H "X-API-Key: $BRION_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"input": [6.3, 3.3, 6.0, 2.5]}'

API documentation · Technology