Quantum Torch

A quantum-enhanced deep learning framework for real-time optimization and pattern recognition. Harness entangled neural networks for breakthrough performance.

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Entangled Neural Layers

Leverage quantum entanglement between neural network layers for exponential increases in processing complexity with minimal energy consumption.

Dynamic Superposition

Continuously optimize model parameters using quantum superposition states that adapt in real-time during training and inference.

Energy-Efficient Training

Reduce computational energy consumption by 90% through entanglement-based state sharing between compute nodes.

Code Example


from quantumtorch import QuantumModel, EntangledLayer

model = QuantumModel([
    EntangledLayer(64, entanglement='bell_pair', qubits=8),
    QuantumDense(32, activation='quantum_sigmoid'),
    QuantumFlux(128, entanglement_threshold=0.01)
])

model.optimize(
    objective='max_entanglement',
    iterations=10000,
    quantum_budget=128
).then(results => {
    console.log('Quantum Entanglement Accuracy:', results.quantum_efficiency)
})

Real-World Applications

🧠

Financial Risk Prediction

Predict market shifts with quantum-optimized financial models that analyze entangled probability distributions between global markets.

🔐

Quantum Cryptanalysis

Analyze cryptographic protocols using entangled quantum circuits simulations to identify potential vulnerabilities.

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