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Data Preparation Guide for Quantum Geometric Learning

This guide explains how to prepare and preprocess data for use with the quantum geometric learning library.

Overview

Proper data preparation is crucial for quantum machine learning tasks. Our library provides tools and utilities to help prepare your data for optimal performance.

Data Formats

1. Basic Data Structure

// Define quantum data structure
quantum_data_t data = {
    .samples = samples_array,
    .labels = labels_array,
    .num_samples = n_samples,
    .feature_dim = n_features
};

2. Supported Formats

  • Dense tensors (float32, float64)
  • Sparse tensors (COO format)
  • Quantum states (complex64, complex128)
  • Distributed data shards

Data Preprocessing

1. Normalization

// Configure normalization
normalization_config_t config = {
    .type = NORMALIZATION_STANDARD,
    .axis = AXIS_FEATURES
};

// Apply normalization
quantum_normalize_data(data, &config);

Available normalization types:

  • Standard (zero mean, unit variance)
  • MinMax (scale to [0,1] range)
  • Robust (using quartiles)
  • Quantum (prepare for quantum circuits)

2. Feature Engineering

// Configure feature extraction
quantum_feature_config_t config = {
    .num_features = 256,
    .quantum_circuit_depth = 4,
    .feature_type = QUANTUM_GEOMETRIC_FEATURES
};

// Extract quantum features
quantum_features_t* features = quantum_extract_features(data, &config);

Feature types:

  • Classical geometric features
  • Quantum geometric features
  • Hybrid features
  • Attention-based features

3. Data Augmentation

// Configure augmentation
augmentation_config_t config = {
    .techniques = {
        .geometric = true,
        .quantum_noise = true,
        .mixup = true
    },
    .strength = 0.8
};

// Apply augmentation
quantum_augment_data(data, &config);

Distributed Data Handling

1. Data Sharding

// Configure data sharding
shard_config_t config = {
    .num_shards = world_size,
    .shard_index = rank,
    .shuffle = true
};

// Create data shard
quantum_data_shard_t* shard = quantum_create_shard(data, &config);

2. Distributed Loading

// Configure distributed loading
distributed_load_config_t config = {
    .world_size = size,
    .local_rank = rank,
    .cache_mode = CACHE_MODE_MEMORY
};

// Load data in distributed manner
distributed_data_t* dist_data = distributed_load_data(path, &config);

Data Pipeline

1. Basic Pipeline

// Create data pipeline
pipeline_config_t config = {
    .batch_size = 32,
    .shuffle = true,
    .prefetch = 2
};

quantum_pipeline_t* pipeline = quantum_create_pipeline(data, &config);

2. Advanced Pipeline

// Configure advanced pipeline
advanced_pipeline_config_t config = {
    .preprocessing = {
        .normalization = NORMALIZATION_STANDARD,
        .augmentation = true
    },
    .quantum = {
        .circuit_depth = 4,
        .measurement_shots = 1000
    },
    .performance = {
        .num_workers = 4,
        .prefetch_size = 2
    }
};

// Create advanced pipeline
advanced_pipeline_t* pipeline = quantum_create_advanced_pipeline(data, &config);

Memory Management

1. Efficient Loading

// Configure memory-efficient loading
memory_config_t config = {
    .max_memory = "16G",
    .storage_type = STORAGE_MEMORY_MAPPED,
    .precision = PRECISION_MIXED
};

// Load data efficiently
efficient_data_t* data = quantum_load_efficient(path, &config);

2. Cache Management

// Configure cache
cache_config_t config = {
    .size = "4G",
    .policy = CACHE_LRU,
    .persistent = true
};

// Create cache manager
cache_manager_t* cache = quantum_create_cache(config);

Data Validation

1. Basic Validation

// Configure validation
validation_config_t config = {
    .checks = {
        .missing_values = true,
        .outliers = true,
        .quantum_compatible = true
    }
};

// Validate data
validation_result_t result = quantum_validate_data(data, &config);

2. Advanced Validation

// Configure advanced validation
advanced_validation_config_t config = {
    .statistical_tests = true,
    .quantum_state_checks = true,
    .correlation_analysis = true
};

// Perform advanced validation
advanced_validation_result_t result = quantum_validate_advanced(data, &config);

Best Practices

1. Data Quality

  • Check for missing values
  • Remove outliers
  • Validate quantum compatibility
  • Ensure consistent scaling

2. Performance

  • Use appropriate data formats
  • Implement efficient loading
  • Enable caching when beneficial
  • Optimize memory usage

3. Distributed Training

  • Balance shard sizes
  • Implement proper shuffling
  • Monitor data loading performance
  • Handle stragglers

Common Issues

1. Memory Issues

// Monitor memory usage
memory_stats_t stats = quantum_monitor_memory();

// Optimize if needed
if (stats.usage > threshold) {
    quantum_optimize_memory(data, OPTIMIZATION_AGGRESSIVE);
}

2. Data Imbalance

// Check class balance
balance_stats_t stats = quantum_check_balance(data);

// Apply balancing if needed
if (stats.imbalance_ratio > 1.5) {
    quantum_balance_data(data, BALANCE_WEIGHTED);
}

3. Loading Performance

// Profile data loading
loading_profile_t profile = quantum_profile_loading(pipeline);

// Optimize based on profile
if (profile.bottleneck == BOTTLENECK_IO) {
    quantum_optimize_loading(pipeline, OPTIMIZATION_IO);
}

Monitoring

1. Data Pipeline Monitoring

# Monitor data pipeline
quantum_geometric-monitor --type=data --metrics=all

# Check loading performance
quantum_geometric-analyze --type=loading --detailed

2. Memory Monitoring

# Monitor memory usage
quantum_geometric-monitor --type=memory --focus=data

# Analyze memory patterns
quantum_geometric-analyze --type=memory --period=1h

Integration

1. Custom Data Sources

// Implement custom data source
custom_source_t* source = quantum_create_custom_source(callbacks);

// Register with pipeline
quantum_register_source(pipeline, source);

2. External Formats

// Convert from external format
external_data_t ext_data = load_external_data(path);
quantum_data_t* data = quantum_convert_data(ext_data);

// Export to external format
external_data_t exported = quantum_export_data(data, FORMAT_EXTERNAL);

These guidelines will help you prepare your data effectively for quantum geometric learning tasks, ensuring optimal performance and results.