QBERT (Quantum Error Reduction by Transformers) uses advanced AI to predict and prevent quantum errors before they occur, achieving a revolutionary 95.2% success rate with zero computational overhead.
State-of-the-art transformer model trained on millions of quantum circuits to predict errors with unprecedented accuracy.
Continuously learns from your specific quantum hardware to improve predictions over time.
Industry-leading performance with minimal computational overhead.
import { QuantumErrorCorrection } from '@q-intercept/sdk';
// Initialize error correction with QBERT model
const qec = new QuantumErrorCorrection({
model: 'qbert-large',
hardware: 'ibm_sherbrooke',
adaptiveLearning: true
});
// Create a quantum circuit
const circuit = qec.createCircuit(5);
circuit.h(0);
circuit.cx(0, 1);
circuit.cx(1, 2);
// Predict errors before execution
const predictions = await qec.predictErrors(circuit);
console.log(`Found ${predictions.length} potential errors`);
// Apply corrections and execute
const correctedCircuit = await qec.applyCorrections(circuit, predictions);
const result = await qec.execute(correctedCircuit);Run calibration before production workloads
Only apply corrections above 90% confidence
Process multiple circuits together for efficiency
Improve predictions over time
Always validate corrected circuits
Respect connectivity constraints
Too many corrections can introduce new errors
Recalibrate after hardware updates