Assess preclinical Alzheimer's risk with quantum kernel fidelity

Encode 68-region cortical MRI thickness and CSF tau biomarkers into an 8-qubit ZZFeatureMap Hilbert space — TauNet 1.0 delivers single-patient scoring, batch CSV assessment, and SHAP feature attribution on Kryptur infrastructure.

Top research capabilities

Recognised among leading quantum-assisted diagnostics platforms, Kryptur advances smarter neurodegenerative intelligence through Hilbert-space kernel methods, CSF biomarker fusion, and live Hetzner VPS API infrastructure.

TauNet 1.0™

Single-patient and batch CSV risk scoring with gauge visualization and Chart.js feature attribution.

1.0

ZZFeatureMap

Eight-qubit encoding with reps=2 and full entanglement — kernel fidelity |⟨φ(xᵢ)|φ(xⱼ)⟩|².

8

QPU Validation

Noise-normalised ibm_fez quantum kernel achieves 3-fold CV AUC 0.9167 vs simulator baseline 0.67.

0.9167

POST /alzheimers-risk

Authenticated JSON payload accepts 68 cortical values plus APOE, CSF, and MMSE biomarkers.

REST

MRI Genetics

Per-region cortical thickness parcellation PCA-reduced before quantum statevector assignment.

68

Kryptur Research

Partner with quantum-biology experts across kernel SVM design, SHAP attribution, and tier-1 reporting.

MRI

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API TauNet 1.0 · POST /alzheimers-risk

Same-origin proxy at /api/taunet/alzheimers-risk forwards JSON patient payloads to the ALZQAPI Flask backend on Hetzner VPS. Request body: 68 cortical thickness values, APOE ε4 count, CSF Aβ42, t-tau, p-tau181, and MMSE. Response includes risk_score (0–1) and top_3_features SHAP attributions.

Kryptur Research / Zius MRI Genetics
ibm_fez · qiskit runtime

Quantum-Assisted Alzheimer's Risk Assessment

A quantum kernel support-vector pipeline that encodes patient biomarkers into an 8-qubit Hilbert space and estimates preclinical Alzheimer's risk from state fidelity.

DEPLOYED KERNELSimulator · AUC 0.67
QPU VALIDATED KERNELibm_fez · AUC 0.9167
FEATURE MAPZZFeatureMap · 8 qubits
API ENDPOINT/api/taunet/alzheimers-risk

This is an experimental research tool. The risk scores are for demonstration purposes only and should not be used for clinical decision-making.

Patient biomarkers

68 values entered

Vector is PCA-reduced to 8 components before encoding into the ZZFeatureMap.

Kernel path for this request

  • Encoding circuitZZFeatureMap, reps=2
  • Qubits used8
  • Kernel definition|⟨φ(xᵢ)|φ(xⱼ)⟩|²
  • Serving kernelsimulator (noiseless)
  • ClassifierSVM, precomputed kernel
  • Training cohort200 patients
This request is scored against the simulator-trained kernel currently in production. Scores therefore cluster closer to the decision boundary than the QPU-validated model — see Model & quantum kernel for why.

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