quantrs2-sim 0.1.3

//! Benchmarking functions for quantum machine learning algorithms.
//!
//! This module provides performance benchmarking capabilities for different
//! QML algorithms across various hardware architectures.

use scirs2_core::ndarray::Array1;
use std::collections::HashMap;

use super::circuit::ParameterizedQuantumCircuit;
use super::config::{HardwareArchitecture, QMLAlgorithmType, QMLConfig};
use super::trainer::QuantumMLTrainer;
use crate::circuit_interfaces::InterfaceCircuit;
use crate::error::Result;

/// Benchmark quantum ML algorithms across different configurations
pub fn benchmark_quantum_ml_algorithms() -> Result<HashMap<String, f64>> {
    let mut results = HashMap::new();

    // Test different QML algorithms
    let algorithms = vec![
        QMLAlgorithmType::VQE,
        QMLAlgorithmType::QAOA,
        QMLAlgorithmType::QCNN,
        QMLAlgorithmType::QSVM,
    ];

    let hardware_archs = vec![
        HardwareArchitecture::NISQ,
        HardwareArchitecture::Superconducting,
        HardwareArchitecture::TrappedIon,
    ];

    for &algorithm in &algorithms {
        for &hardware in &hardware_archs {
            let benchmark_time = benchmark_algorithm_hardware_combination(algorithm, hardware)?;
            results.insert(format!("{algorithm:?}_{hardware:?}"), benchmark_time);
        }
    }

    Ok(results)
}

/// Benchmark a specific algorithm-hardware combination
fn benchmark_algorithm_hardware_combination(
    algorithm: QMLAlgorithmType,
    hardware: HardwareArchitecture,
) -> Result<f64> {
    let start = std::time::Instant::now();

    let config = QMLConfig {
        algorithm_type: algorithm,
        hardware_architecture: hardware,
        num_qubits: 4,
        circuit_depth: 2,
        num_parameters: 8,
        max_epochs: 5,
        batch_size: 4,
        ..Default::default()
    };

    // Create a simple parameterized circuit
    let circuit = create_test_circuit(config.num_qubits)?;
    let parameters = Array1::from_vec(vec![0.1; config.num_parameters]);
    let parameter_names = (0..config.num_parameters)
        .map(|i| format!("param_{i}"))
        .collect();

    let pqc = ParameterizedQuantumCircuit::new(circuit, parameters, parameter_names, hardware);

    let mut trainer = QuantumMLTrainer::new(config, pqc, None)?;

    // Simple quadratic loss function for testing
    let loss_fn = |params: &Array1<f64>| -> Result<f64> {
        // Simple quadratic loss: sum of squared parameters
        Ok(params.iter().map(|&x| x * x).sum::<f64>())
    };

    let _result = trainer.train(loss_fn)?;

    Ok(start.elapsed().as_secs_f64() * 1000.0)
}

/// Create a test circuit for benchmarking
fn create_test_circuit(num_qubits: usize) -> Result<InterfaceCircuit> {
    // Create a simple test circuit
    // In practice, this would create a proper parameterized circuit
    let circuit = InterfaceCircuit::new(num_qubits, 0);
    Ok(circuit)
}

/// Benchmark gradient computation methods
pub fn benchmark_gradient_methods() -> Result<HashMap<String, f64>> {
    let mut results = HashMap::new();

    let methods = vec![
        "parameter_shift",
        "finite_differences",
        "automatic_differentiation",
        "natural_gradients",
    ];

    for method in methods {
        let benchmark_time = benchmark_gradient_method(method)?;
        results.insert(method.to_string(), benchmark_time);
    }

    Ok(results)
}

/// Benchmark a specific gradient computation method
fn benchmark_gradient_method(method: &str) -> Result<f64> {
    let start = std::time::Instant::now();

    // Create a simple function to differentiate
    let test_function = |params: &Array1<f64>| -> Result<f64> {
        Ok(params
            .iter()
            .enumerate()
            .map(|(i, &x)| (i as f64 + 1.0) * x * x)
            .sum::<f64>())
    };

    let test_params = Array1::from_vec(vec![0.1, 0.2, 0.3, 0.4]);

    // Simulate gradient computation
    match method {
        "parameter_shift" => {
            compute_parameter_shift_gradient(&test_function, &test_params)?;
        }
        "finite_differences" => {
            compute_finite_difference_gradient(&test_function, &test_params)?;
        }
        "automatic_differentiation" => {
            compute_autodiff_gradient(&test_function, &test_params)?;
        }
        "natural_gradients" => {
            compute_natural_gradient(&test_function, &test_params)?;
        }
        _ => {
            return Err(crate::error::SimulatorError::InvalidInput(format!(
                "Unknown gradient method: {method}"
            )))
        }
    }

    Ok(start.elapsed().as_secs_f64() * 1000.0)
}

/// Compute parameter shift gradient (simplified implementation)
fn compute_parameter_shift_gradient<F>(
    function: &F,
    parameters: &Array1<f64>,
) -> Result<Array1<f64>>
where
    F: Fn(&Array1<f64>) -> Result<f64>,
{
    let num_params = parameters.len();
    let mut gradient = Array1::zeros(num_params);
    let shift = std::f64::consts::PI / 2.0;

    for i in 0..num_params {
        let mut params_plus = parameters.clone();
        let mut params_minus = parameters.clone();

        params_plus[i] += shift;
        params_minus[i] -= shift;

        let loss_plus = function(&params_plus)?;
        let loss_minus = function(&params_minus)?;

        gradient[i] = (loss_plus - loss_minus) / 2.0;
    }

    Ok(gradient)
}

/// Compute finite difference gradient
fn compute_finite_difference_gradient<F>(
    function: &F,
    parameters: &Array1<f64>,
) -> Result<Array1<f64>>
where
    F: Fn(&Array1<f64>) -> Result<f64>,
{
    let num_params = parameters.len();
    let mut gradient = Array1::zeros(num_params);
    let eps = 1e-8;

    for i in 0..num_params {
        let mut params_plus = parameters.clone();
        params_plus[i] += eps;

        let loss_plus = function(&params_plus)?;
        let loss_current = function(parameters)?;

        gradient[i] = (loss_plus - loss_current) / eps;
    }

    Ok(gradient)
}

/// Compute autodiff gradient (placeholder)
fn compute_autodiff_gradient<F>(function: &F, parameters: &Array1<f64>) -> Result<Array1<f64>>
where
    F: Fn(&Array1<f64>) -> Result<f64>,
{
    // For simplicity, use parameter shift
    compute_parameter_shift_gradient(function, parameters)
}

/// Compute natural gradient (placeholder)
fn compute_natural_gradient<F>(function: &F, parameters: &Array1<f64>) -> Result<Array1<f64>>
where
    F: Fn(&Array1<f64>) -> Result<f64>,
{
    // For simplicity, use parameter shift
    compute_parameter_shift_gradient(function, parameters)
}

/// Benchmark optimizer performance
pub fn benchmark_optimizers() -> Result<HashMap<String, f64>> {
    let mut results = HashMap::new();

    let optimizers = vec!["adam", "sgd", "rmsprop", "lbfgs"];

    for optimizer in optimizers {
        let benchmark_time = benchmark_optimizer(optimizer)?;
        results.insert(optimizer.to_string(), benchmark_time);
    }

    Ok(results)
}

/// Benchmark a specific optimizer
fn benchmark_optimizer(optimizer: &str) -> Result<f64> {
    let start = std::time::Instant::now();

    // Simulate optimizer performance on a simple quadratic function
    let mut params = Array1::from_vec(vec![1.0, 2.0, 3.0, 4.0]);
    let target = Array1::<f64>::zeros(4);

    for _iteration in 0..100 {
        // Compute gradient
        let gradient = &params - &target;

        // Apply optimizer update (simplified)
        match optimizer {
            "adam" => {
                // Simplified Adam update
                params = &params - 0.01 * &gradient;
            }
            "sgd" => {
                params = &params - 0.01 * &gradient;
            }
            "rmsprop" => {
                // Simplified RMSprop update
                params = &params - 0.01 * &gradient;
            }
            "lbfgs" => {
                // Simplified L-BFGS update
                params = &params - 0.01 * &gradient;
            }
            _ => {
                return Err(crate::error::SimulatorError::InvalidInput(format!(
                    "Unknown optimizer: {optimizer}"
                )))
            }
        }
    }

    Ok(start.elapsed().as_secs_f64() * 1000.0)
}

/// Run comprehensive benchmarks
pub fn run_comprehensive_benchmarks() -> Result<HashMap<String, HashMap<String, f64>>> {
    let mut all_results = HashMap::new();

    all_results.insert("algorithms".to_string(), benchmark_quantum_ml_algorithms()?);
    all_results.insert("gradients".to_string(), benchmark_gradient_methods()?);
    all_results.insert("optimizers".to_string(), benchmark_optimizers()?);

    Ok(all_results)
}