quantrs2-ml 0.1.3

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

use super::*;
use crate::error::{MLError, Result};
use scirs2_core::ndarray::*;
use scirs2_core::random::prelude::*;
use scirs2_core::{Complex32, Complex64};
use std::f64::consts::PI;

use super::types::QuantumContinuousFlowConfig;

impl Default for QuantumContinuousFlowConfig {
    fn default() -> Self {
        Self {
            input_dim: 32,
            latent_dim: 16,
            num_qubits: 8,
            num_flow_layers: 4,
            flow_architecture: FlowArchitecture::QuantumRealNVP {
                hidden_dims: vec![64, 64],
                num_coupling_layers: 4,
                quantum_coupling_type: QuantumCouplingType::QuantumEntangledCoupling,
            },
            quantum_enhancement_level: 0.5,
            integration_method: ODEIntegrationMethod::QuantumAdaptive,
            invertibility_tolerance: 1e-6,
            entanglement_coupling_strength: 0.1,
            quantum_divergence_type: QuantumDivergenceType::QuantumRelativeEntropy,
            use_quantum_attention_flows: true,
            adaptive_step_size: true,
            regularization_config: FlowRegularizationConfig {
                weight_decay: 1e-5,
                spectral_normalization: true,
                kinetic_energy_regularization: 0.01,
                entanglement_regularization: 0.1,
                jacobian_regularization: 0.01,
                quantum_volume_preservation: 0.05,
            },
        }
    }
}