What is Quantum Machine Learning?
Quantum Advantages
- Exponential speedup in pattern recognition
- Better optimization in hyperparameter search
- Enhanced data classification with qubit states
Core Concepts
- Quantum neural network architectures
- Hybrid quantum-classical models
Key Use Cases
Drug Discovery
Quantum machine learning accelerates molecular simulations and compound screening by:
- Identifying optimal molecular structures
- Optimizing biochemical reactions
Financial Modeling
Quantum ML applications in finance include:
- Portfolio optimization with quantum annealing
- Risk analysis using qubit-based monte carlo
Quantum Machine Learning Code
// Quantum Neural Network Example
const qml = new QuantumMLModel({
qubits: 8,
layers: 4,
optimizer: 'quantum-gradient-descent'
});
// Sample dataset
const data = [
{ features: [0.5, 1.2], label: 1 },
{ features: [-1.3, 0.8], label: 0 },
];
// Train model
qml.train(data, {
epochs: 100,
batchSize: 4
});
// Predict new data
const prediction = qml.predict([0.8, 0.4]);
console.log("Quantum prediction:", prediction);
This example demonstrates a simplified quantum machine learning model setup. The quantum circuit uses parameterized quantum gates to learn patterns from data.
The Future of Quantum ML
Quantum machine learning is still in its early stages but holds promise to transform AI capabilities. Our research division is focusing on:
Quantum Data Encoding
Better methods to convert classical data into quantum states
Quantum-Classical Hybrids
New algorithms that leverage both classical and quantum strengths
Quantum Transfer Learning
Using pre-trained quantum weights to solve new problems faster