quantrs2-ml 0.1.3

Quantum Machine Learning module for QuantRS2
Documentation 
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//! # QuantumINRConfig - Trait Implementations
//!
//! This module contains trait implementations for `QuantumINRConfig`.
//!
//! ## Implemented Traits
//!
//! - `Default`
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use super::types::{
    CompressionConfig, CompressionMethod, ConvergenceCriteria, FrequencyProgression,
    GradientEstimation, LearningRateSchedule, MetaLearningConfig, MetaLearningMethod,
    OptimizationConfig, PositionalEncodingType, PruningStrategy, QuantumActivation,
    QuantumActivationConfig, QuantumConvergenceMetric, QuantumINRConfig, QuantumOptimizerType,
    QuantumPositionalEncoding, QuantumRegularization, RegularizationConfig, RepresentationMethod,
    SignalType,
};

impl Default for QuantumINRConfig {
    fn default() -> Self {
        Self {
            signal_type: SignalType::Image2D {
                height: 256,
                width: 256,
                channels: 3,
            },
            coordinate_dim: 2,
            output_dim: 3,
            num_qubits: 8,
            network_depth: 8,
            hidden_dim: 256,
            quantum_enhancement_level: 0.7,
            representation_method: RepresentationMethod::QuantumSIREN {
                omega_0: 30.0,
                omega_hidden: 1.0,
                quantum_frequency_modulation: true,
            },
            positional_encoding: QuantumPositionalEncoding {
                encoding_type: PositionalEncodingType::QuantumSinusoidal {
                    base_frequency: 1.0,
                    frequency_progression: FrequencyProgression::Logarithmic,
                },
                num_frequencies: 10,
                frequency_scale: 1.0,
                quantum_enhancement: true,
                learnable_frequencies: true,
            },
            activation_config: QuantumActivationConfig {
                activation_type: QuantumActivation::QuantumSiren { omega: 30.0 },
                frequency_modulation: true,
                phase_modulation: true,
                amplitude_control: true,
                quantum_nonlinearity_strength: 0.5,
            },
            compression_config: CompressionConfig {
                compression_method: CompressionMethod::QuantumPruning {
                    sparsity_target: 0.8,
                    pruning_strategy: PruningStrategy::QuantumEntanglement,
                },
                target_compression_ratio: 10.0,
                quality_preservation: 0.95,
                quantum_compression_enhancement: 0.3,
                adaptive_compression: true,
            },
            meta_learning_config: MetaLearningConfig {
                meta_learning_method: MetaLearningMethod::QuantumMAML {
                    first_order: false,
                    quantum_gradient_estimation: true,
                },
                adaptation_steps: 5,
                meta_learning_rate: 1e-3,
                inner_learning_rate: 1e-4,
                quantum_meta_enhancement: 0.2,
            },
            optimization_config: OptimizationConfig {
                optimizer_type: QuantumOptimizerType::QuantumAdam {
                    beta1: 0.9,
                    beta2: 0.999,
                    epsilon: 1e-8,
                    quantum_momentum: true,
                },
                learning_rate_schedule: LearningRateSchedule::Cosine {
                    max_rate: 1e-3,
                    min_rate: 1e-6,
                    period: 1000,
                },
                gradient_estimation: GradientEstimation::ParameterShift,
                regularization: RegularizationConfig {
                    weight_decay: 1e-5,
                    spectral_normalization: true,
                    quantum_regularization: QuantumRegularization::EntanglementRegularization {
                        strength: 0.1,
                    },
                    smoothness_regularization: 0.01,
                },
                convergence_criteria: ConvergenceCriteria {
                    max_iterations: 10000,
                    tolerance: 1e-6,
                    patience: 100,
                    quantum_convergence_metric: QuantumConvergenceMetric::QuantumFidelity,
                },
            },
        }
    }
}