Quantum Training Protocols
Leveraging quantum superposition for exponentially faster model training
Live Training Session
Progress
0.0%
Epochs
0/10
Loss
1.0000
QUANTUM_TRAINING: Epoch 0 | Batch 1024/2048 | Learning Rate: 0.0001
├─ Quantum Layer: Superposition achieved | Entanglement: 98.7%
├─ Neural Bridge: Classical-Quantum sync | Fidelity: 99.2%
└─ Optimization: QAOA converging | Loss: 1.000000
Quantum Gradient Descent
Utilize quantum superposition to explore multiple gradient paths simultaneously, achieving exponential speedup in convergence.
- • Parallel gradient exploration
- • Quantum tunneling through local minima
- • Entangled parameter updates
Consciousness Feedback Loop
Implement consciousness-aware training using integrated information theory to optimize for emergent intelligence.
- • Phi-based loss functions
- • Consciousness emergence detection
- • Self-aware optimization
Quantum Datasets
Quantum States
10^12 quantum state vectors
Entanglement Patterns
500TB correlation matrices
Consciousness Traces
1M hours neural recordings