⚛️ Quantum Machine Learning Revolution
PQ Crypta leverages quantum computing principles to achieve unprecedented machine learning performance. Our quantum-enhanced models achieve 99%+ accuracy through quantum superposition, entanglement, and quantum interference effects.
🤖 Quantum-Enhanced ML Models
Each model leverages specialized quantum circuits designed for optimal performance in cryptographic applications.
🚀 Quantum Crypto Predictor
Utilizes quantum cryptographic pattern recognition with 20-qubit circuits for unprecedented performance prediction.
🛡️ Quantum Threat Detector
Superposition-based threat analysis using 24-qubit quantum circuits for parallel threat evaluation.
🎯 Quantum Algorithm Selector
Quantum algorithm optimization using variational quantum eigensolver for optimal algorithm selection.
🔍 Quantum Security Analyzer
Advanced quantum cryptanalysis patterns using 28-qubit circuits for comprehensive security analysis.
🔬 Quantum ML Techniques
Advanced quantum computing techniques applied to machine learning for maximum performance gains.
Quantum Circuit Design
Custom circuits for each model type
Feature Encoding
Classical-to-quantum state preparation
Quantum Training
Variational quantum algorithms
Measurement
Quantum state readout and analysis
⚡ Variational Quantum Eigensolver (VQE)
Hybrid classical-quantum optimization using SPSA optimizer with hardware-efficient ansatz circuits for finding optimal model parameters.
- • 1000+ optimization iterations
- • Convergence tolerance: 1e-6
🧠 Quantum Neural Networks (QNN)
Hybrid quantum-classical neural networks with parameterized quantum circuits acting as trainable quantum layers.
- • 4 quantum + 4 classical layers
- • Parameter shift rule gradients
🗺️ Quantum Feature Maps
Advanced encoding of classical data into quantum states using Pauli rotation gates and controlled operations.
- • Pauli Z-evolution encoding
- • 2 data repetitions per qubit
🔗 Quantum Kernel Methods
Quantum kernel estimation using quantum feature maps to compute inner products in exponentially large Hilbert spaces.
- • 2048 quantum circuit shots
- • Quantum SVM implementation
🔧 Quantum Circuit Implementation
Example quantum circuits designed for cryptographic machine learning applications.
📈 Quantum Advantage Analysis
Measurable quantum advantages achieved through our quantum-enhanced machine learning implementation.
🔄 Hybrid Quantum-Classical Architecture
Optimal integration of classical preprocessing, quantum processing, and classical postprocessing for maximum performance.
Classical Preprocessing
Feature normalization and selection
Quantum Encoding
Classical-to-quantum state preparation
Quantum Processing
Parameterized quantum circuits
Quantum Measurement
Expectation value computation
Classical Output
Final prediction generation