use quantrs2_core::qml::{
QuantumAttention, QuantumAugmentation, QuantumContrastiveConfig, QuantumContrastiveLearner,
QuantumMAML, QuantumMemoryConfig, QuantumMemoryNetwork, QuantumMetaLearningConfig,
QuantumReservoirComputer, QuantumReservoirConfig, QuantumTask, QuantumTransformer,
QuantumTransformerConfig,
};
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::random::prelude::*;
use scirs2_core::Complex64;
fn main() -> Result<(), Box<dyn std::error::Error>> {
println!("╔══════════════════════════════════════════════════════════════════════════╗");
println!("║ Advanced Quantum Machine Learning Features - QuantRS2-Core ║");
println!("║ ║");
println!("║ Demonstrating cutting-edge quantum ML algorithms ║");
println!("╚══════════════════════════════════════════════════════════════════════════╝\n");
println!("═══════════════════════════════════════════════════════════════════");
println!("Part 1: Quantum Transformers with Attention Mechanisms");
println!("═══════════════════════════════════════════════════════════════════\n");
demo_quantum_transformer()?;
println!("\n═══════════════════════════════════════════════════════════════════");
println!("Part 2: Quantum Reservoir Computing for Time-Series");
println!("═══════════════════════════════════════════════════════════════════\n");
demo_quantum_reservoir()?;
println!("\n═══════════════════════════════════════════════════════════════════");
println!("Part 3: Quantum Memory Networks");
println!("═══════════════════════════════════════════════════════════════════\n");
demo_quantum_memory_network()?;
println!("\n═══════════════════════════════════════════════════════════════════");
println!("Part 4: Quantum Contrastive Learning");
println!("═══════════════════════════════════════════════════════════════════\n");
demo_quantum_contrastive()?;
println!("\n═══════════════════════════════════════════════════════════════════");
println!("Part 5: Quantum Meta-Learning (MAML)");
println!("═══════════════════════════════════════════════════════════════════\n");
demo_quantum_meta_learning()?;
println!("\n═══════════════════════════════════════════════════════════════════");
println!("Summary: Advanced QML Capabilities");
println!("═══════════════════════════════════════════════════════════════════\n");
print_summary();
Ok(())
}
fn demo_quantum_transformer() -> Result<(), Box<dyn std::error::Error>> {
println!("🔷 Quantum Transformers enable attention-based sequence processing");
println!(" on quantum computers, leveraging quantum superposition for");
println!(" enhanced representation learning.\n");
let config = QuantumTransformerConfig {
num_qubits: 4,
num_heads: 2,
head_dim: 2,
num_layers: 2,
ffn_dim: 8,
dropout_rate: 0.1,
max_seq_length: 16,
use_layer_norm: true,
};
println!("Configuration:");
println!(
" • Qubits: {} (Hilbert space dimension: 2^{} = {})",
config.num_qubits,
config.num_qubits,
1 << config.num_qubits
);
println!(" • Attention heads: {}", config.num_heads);
println!(" • Transformer layers: {}", config.num_layers);
println!(" • Feed-forward dimension: {}", config.ffn_dim);
println!(" • Layer normalization: {}\n", config.use_layer_norm);
let transformer = QuantumTransformer::new(config)?;
println!("Creating test sequence of 3 quantum states...");
let mut input = Array2::zeros((3, 4));
for i in 0..3 {
for j in 0..4 {
input[[i, j]] = Complex64::new((i + j) as f64 * 0.1, 0.0);
}
}
println!("Processing through quantum transformer...");
let output = transformer.forward(&input)?;
println!("✓ Successfully processed sequence");
println!(
" Output shape: {} states × {} qubits",
output.shape()[0],
output.shape()[1]
);
println!("\n Key Features:");
println!(" → Multi-head quantum attention for pattern recognition");
println!(" → Quantum positional encoding preserves sequence information");
println!(" → Feed-forward quantum networks for non-linear transformations");
println!(" → Layer normalization maintains quantum state properties");
println!("\n Applications:");
println!(" • Quantum natural language processing");
println!(" • Quantum time-series prediction");
println!(" • Quantum molecular sequence analysis");
Ok(())
}
fn demo_quantum_reservoir() -> Result<(), Box<dyn std::error::Error>> {
println!("🔷 Quantum Reservoir Computing leverages the natural dynamics of");
println!(" quantum systems as computational resources without training");
println!(" the reservoir itself.\n");
let config = QuantumReservoirConfig {
num_qubits: 6,
depth: 8,
spectral_radius: 0.9,
input_scaling: 1.0,
leak_rate: 0.3,
use_entanglement: true,
seed: Some(42),
};
println!("Configuration:");
println!(" • Reservoir qubits: {}", config.num_qubits);
println!(" • Reservoir depth: {} (circuit layers)", config.depth);
println!(
" • Spectral radius: {:.2} (controls dynamics)",
config.spectral_radius
);
println!(
" • Leak rate: {:.2} (memory fading factor)",
config.leak_rate
);
println!(" • Entanglement: {}\n", config.use_entanglement);
let mut qrc = QuantumReservoirComputer::new(config, 2)?;
println!("Creating test time-series (10 time steps)...");
let inputs = Array2::from_shape_fn((10, 6), |(i, j)| (i + j) as f64 * 0.1);
println!("Processing through quantum reservoir...");
let outputs = qrc.process_sequence(&inputs)?;
println!("✓ Successfully processed time-series");
println!(
" Output shape: {} steps × {} outputs",
outputs.shape()[0],
outputs.shape()[1]
);
println!("\n Key Features:");
println!(" → Fixed random quantum circuit (no training needed)");
println!(" → Quantum echo state property for temporal patterns");
println!(" → Pauli expectation features (3 per qubit)");
println!(" → Linear readout layer (trainable)");
println!("\n Echo State Property:");
println!(" The quantum reservoir projects input sequences into a high-");
println!(" dimensional Hilbert space where temporal patterns become");
println!(" linearly separable for the readout layer.");
println!("\n Applications:");
println!(" • Quantum time-series forecasting");
println!(" • Chaotic system prediction");
println!(" • Real-time quantum signal processing");
Ok(())
}
fn demo_quantum_memory_network() -> Result<(), Box<dyn std::error::Error>> {
println!("🔷 Quantum Memory Networks augment quantum neural networks with");
println!(" external quantum memory for complex reasoning tasks.\n");
let config = QuantumMemoryConfig {
memory_slots: 32,
qubits_per_slot: 3,
controller_size: 16,
num_read_heads: 1,
num_write_heads: 1,
init_strategy: quantrs2_core::qml::MemoryInitStrategy::Zero,
};
println!("Configuration:");
println!(" • Memory slots: {}", config.memory_slots);
println!(
" • Qubits per slot: {} (2^{} = {} dimensional states)",
config.qubits_per_slot,
config.qubits_per_slot,
1 << config.qubits_per_slot
);
println!(" • Controller size: {} neurons", config.controller_size);
println!(
" • Read/Write heads: {}/{}\n",
config.num_read_heads, config.num_write_heads
);
let mut network = QuantumMemoryNetwork::new(8, config);
println!("Processing input sequence...");
let input = Array1::from_vec(vec![0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]);
let outputs = network.step(&input)?;
println!("✓ Successfully performed memory operations");
println!(" Read outputs: {} quantum states", outputs.len());
println!(" State dimension: {}", outputs[0].len());
println!("\n Memory Operations:");
println!(" 1. Controller processes input → generates attention weights");
println!(" 2. Read: Weighted sum of memory slots using attention");
println!(" 3. Write: Erase-then-add operations on memory");
println!(" 4. Usage tracking for least-used slot allocation");
println!("\n Architecture:");
println!(" • Attention-based addressing (quantum fidelity metric)");
println!(" • Differentiable read/write operations");
println!(" • Quantum state normalization preservation");
println!(" • Neural Turing Machine-inspired design");
println!("\n Applications:");
println!(" • Question answering with quantum reasoning");
println!(" • Quantum program synthesis");
println!(" • Long-term quantum dependency learning");
Ok(())
}
fn demo_quantum_contrastive() -> Result<(), Box<dyn std::error::Error>> {
println!("🔷 Quantum Contrastive Learning enables self-supervised quantum");
println!(" representation learning without labeled data.\n");
let config = QuantumContrastiveConfig {
num_qubits: 3,
encoder_depth: 3,
temperature: 0.5,
momentum: 0.999,
batch_size: 4,
num_views: 2,
};
println!("Configuration:");
println!(" • Encoder qubits: {}", config.num_qubits);
println!(" • Encoder depth: {} layers", config.encoder_depth);
println!(
" • Temperature: {:.2} (contrastive loss scaling)",
config.temperature
);
println!(
" • Momentum: {:.4} (for momentum encoder)",
config.momentum
);
println!(" • Batch size: {}\n", config.batch_size);
let mut learner = QuantumContrastiveLearner::new(config);
println!("Creating batch of quantum states...");
let mut batch = Vec::new();
for i in 0..4 {
let state = Array1::from_vec(vec![
Complex64::new(((i + 1) as f64 * 0.3).cos(), 0.0),
Complex64::new(((i + 1) as f64 * 0.3).sin(), 0.0),
Complex64::new(0.0, 0.0),
Complex64::new(0.0, 0.0),
Complex64::new(0.0, 0.0),
Complex64::new(0.0, 0.0),
Complex64::new(0.0, 0.0),
Complex64::new(0.0, 0.0),
]);
batch.push(state);
}
println!("Training one contrastive learning step...");
let loss = learner.train_step(&batch, 0.01)?;
println!("✓ Successfully performed contrastive learning");
println!(" Loss: {loss:.6}");
println!("\n Contrastive Learning Pipeline:");
println!(" 1. Quantum data augmentation (rotations, noise)");
println!(" 2. Encode multiple views through quantum circuits");
println!(" 3. Maximize agreement between views of same state");
println!(" 4. Minimize agreement with different states");
println!(" 5. NT-Xent loss with quantum fidelity metric");
println!("\n Quantum Augmentations:");
println!(" • Random unitary rotations");
println!(" • Depolarizing noise");
println!(" • Amplitude/phase damping");
println!(" • Random Pauli gates");
println!("\n Applications:");
println!(" • Unsupervised quantum feature learning");
println!(" • Robust quantum representations for NISQ devices");
println!(" • Pre-training for downstream quantum tasks");
Ok(())
}
fn demo_quantum_meta_learning() -> Result<(), Box<dyn std::error::Error>> {
println!("🔷 Quantum Meta-Learning (MAML) enables rapid adaptation to new");
println!(" tasks with minimal quantum training data.\n");
let config = QuantumMetaLearningConfig {
num_qubits: 2,
circuit_depth: 2,
inner_lr: 0.01,
outer_lr: 0.001,
inner_steps: 3,
n_support: 2,
n_query: 4,
n_way: 2,
meta_batch_size: 2,
};
println!("Configuration:");
println!(" • Circuit qubits: {}", config.num_qubits);
println!(" • Circuit depth: {} layers", config.circuit_depth);
println!(
" • Inner/Outer learning rate: {}/{}",
config.inner_lr, config.outer_lr
);
println!(" • Inner adaptation steps: {}", config.inner_steps);
println!(
" • {}-way {}-shot classification\n",
config.n_way, config.n_support
);
let mut maml = QuantumMAML::new(config.clone());
println!("Creating random few-shot learning task...");
let task = QuantumTask::random(
config.num_qubits,
config.n_way,
config.n_support,
config.n_query,
);
println!(
" Support set: {} examples ({} per class)",
task.support_states.len(),
config.n_support
);
println!(
" Query set: {} examples ({} per class)",
task.query_states.len(),
config.n_query
);
println!("\nAdapting quantum circuit to new task...");
let adapted_model = maml.adapt(&task)?;
println!(
"✓ Successfully adapted to task in {} gradient steps",
config.inner_steps
);
let accuracy = maml.evaluate(&task)?;
println!(" Adaptation accuracy: {:.1}%", accuracy * 100.0);
println!("\n MAML Training Loop:");
println!(" 1. Sample batch of tasks from task distribution");
println!(" 2. For each task:");
println!(" a) Clone meta-parameters θ");
println!(" b) Adapt: θ' = θ - α∇L_support(θ) [inner loop]");
println!(" c) Compute loss on query set: L_query(θ')");
println!(" 3. Meta-update: θ = θ - β∇Σ L_query(θ') [outer loop]");
println!("\n Key Advantages:");
println!(" • Rapid task adaptation (few gradient steps)");
println!(" • Learns good initialization for quantum parameters");
println!(" • Efficient use of limited quantum data");
println!(" • Task-agnostic meta-learning framework");
println!("\n Applications:");
println!(" • Few-shot quantum classification");
println!(" • Fast quantum state tomography");
println!(" • Adaptive quantum control");
println!(" • Drug discovery with limited molecular data");
Ok(())
}
fn print_summary() {
println!("🌟 QuantRS2-Core now includes 5 cutting-edge QML algorithms:");
println!();
println!("┌──────────────────────────────────────────────────────────────────┐");
println!("│ 1. Quantum Transformers │");
println!("│ ✓ Multi-head quantum attention mechanisms │");
println!("│ ✓ Quantum positional encoding for sequences │");
println!("│ ✓ Feed-forward quantum networks with layer norm │");
println!("│ → Applications: NLP, time-series, molecular sequences │");
println!("├──────────────────────────────────────────────────────────────────┤");
println!("│ 2. Quantum Reservoir Computing │");
println!("│ ✓ Fixed random quantum circuits (no training) │");
println!("│ ✓ Quantum echo state property for temporal memory │");
println!("│ ✓ Pauli expectation feature extraction │");
println!("│ → Applications: Time-series forecasting, signal processing │");
println!("├──────────────────────────────────────────────────────────────────┤");
println!("│ 3. Quantum Memory Networks │");
println!("│ ✓ External quantum memory with addressable slots │");
println!("│ ✓ Attention-based read/write operations │");
println!("│ ✓ Neural Turing Machine architecture │");
println!("│ → Applications: Q&A, reasoning, program synthesis │");
println!("├──────────────────────────────────────────────────────────────────┤");
println!("│ 4. Quantum Contrastive Learning │");
println!("│ ✓ Self-supervised representation learning │");
println!("│ ✓ Quantum data augmentation strategies │");
println!("│ ✓ NT-Xent loss with quantum fidelity │");
println!("│ → Applications: Unsupervised learning, robust features │");
println!("├──────────────────────────────────────────────────────────────────┤");
println!("│ 5. Quantum Meta-Learning (MAML & Reptile) │");
println!("│ ✓ Model-agnostic few-shot learning │");
println!("│ ✓ Rapid task adaptation with minimal data │");
println!("│ ✓ Bi-level optimization for quantum circuits │");
println!("│ → Applications: Few-shot classification, drug discovery │");
println!("└──────────────────────────────────────────────────────────────────┘");
println!();
println!("📊 Performance Characteristics:");
println!(" • Hilbert space scaling: Exponential in qubit count");
println!(" • Quantum memory: Intrinsic quantum dynamics");
println!(" • Few-shot learning: Quantum interference patterns");
println!(" • NISQ-friendly: Designed for current quantum hardware");
println!();
println!("🔬 Research Impact:");
println!(" These implementations represent state-of-the-art quantum ML");
println!(" research, providing researchers and developers");
println!(" with production-ready quantum learning algorithms.");
println!();
println!("📚 For detailed documentation and theory:");
println!(" See individual module documentation in src/qml/");
println!();
println!("✨ QuantRS2: Pushing the boundaries of quantum machine learning!");
}