scirs2-integrate 0.4.2

//! GPU-accelerated quantum solvers and computations
//!
//! This module provides GPU acceleration for quantum computations,
//! including quantum state evolution, matrix operations, and
//! multi-body quantum systems.

use crate::error::{IntegrateError, IntegrateResult as Result};
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::numeric::Complex64;
use std::collections::HashMap;

/// GPU-accelerated quantum solver for large quantum systems
#[derive(Debug, Clone)]
pub struct GPUQuantumSolver {
    /// GPU device identifier
    pub device_id: usize,
    /// Memory allocation for quantum states
    pub memory_allocation: usize,
    /// Parallelization strategy
    pub parallel_strategy: QuantumParallelStrategy,
    /// Number of qubits that can be handled
    pub max_qubits: usize,
    /// GPU memory manager
    pub memory_manager: GPUMemoryManager,
}

impl GPUQuantumSolver {
    /// Create new GPU quantum solver
    pub fn new(device_id: usize, maxqubits: usize) -> Result<Self> {
        let memory_allocation = 1_000_000_000; // 1GB default
        let parallel_strategy = QuantumParallelStrategy::StateVectorParallel;
        let memory_manager = GPUMemoryManager::new(memory_allocation)?;

        Ok(Self {
            device_id,
            memory_allocation,
            parallel_strategy,
            max_qubits: maxqubits,
            memory_manager,
        })
    }

    /// Solve quantum system evolution on GPU
    pub fn evolve_quantum_state(
        &mut self,
        initial_state: &Array1<Complex64>,
        hamiltonian: &Array2<Complex64>,
        time_step: f64,
        n_steps: usize,
    ) -> Result<Array1<Complex64>> {
        if initial_state.len() > (1 << self.max_qubits) {
            return Err(IntegrateError::InvalidInput(
                "State too large for GPU solver".to_string(),
            ));
        }

        // Allocate GPU memory
        self.memory_manager
            .allocate_state_vector(initial_state.len())?;
        self.memory_manager
            .allocate_hamiltonian(hamiltonian.nrows(), hamiltonian.ncols())?;

        // Copy data to GPU (simulated)
        let mut current_state = initial_state.clone();

        // Time evolution using GPU-accelerated matrix exponential
        for _step in 0..n_steps {
            current_state =
                self.apply_time_evolution_operator(&current_state, hamiltonian, time_step)?;
        }

        Ok(current_state)
    }

    /// Apply time evolution operator using GPU acceleration
    fn apply_time_evolution_operator(
        &self,
        state: &Array1<Complex64>,
        hamiltonian: &Array2<Complex64>,
        dt: f64,
    ) -> Result<Array1<Complex64>> {
        // Simplified GPU-accelerated time evolution
        // In practice, this would use CUDA kernels or similar

        match self.parallel_strategy {
            QuantumParallelStrategy::StateVectorParallel => {
                self.state_vector_parallel_evolution(state, hamiltonian, dt)
            }
            QuantumParallelStrategy::MatrixParallel => {
                self.matrix_parallel_evolution(state, hamiltonian, dt)
            }
            QuantumParallelStrategy::HybridParallel => {
                self.hybrid_parallel_evolution(state, hamiltonian, dt)
            }
        }
    }

    /// State vector parallel evolution
    fn state_vector_parallel_evolution(
        &self,
        state: &Array1<Complex64>,
        hamiltonian: &Array2<Complex64>,
        dt: f64,
    ) -> Result<Array1<Complex64>> {
        // Apply exp(-i * H * dt) |ψ⟩ using parallel state vector operations
        let mut evolved_state = Array1::zeros(state.len());

        // Simplified implementation - parallel matrix-vector multiplication
        for i in 0..state.len() {
            let mut sum = Complex64::new(0.0, 0.0);
            for j in 0..state.len() {
                // Simplified exponential: U ≈ I - i * H * dt
                let matrix_element = if i == j {
                    Complex64::new(1.0, 0.0) - Complex64::new(0.0, dt) * hamiltonian[[i, j]]
                } else {
                    -Complex64::new(0.0, dt) * hamiltonian[[i, j]]
                };
                sum += matrix_element * state[j];
            }
            evolved_state[i] = sum;
        }

        Ok(evolved_state)
    }

    /// Matrix parallel evolution
    fn matrix_parallel_evolution(
        &self,
        state: &Array1<Complex64>,
        hamiltonian: &Array2<Complex64>,
        dt: f64,
    ) -> Result<Array1<Complex64>> {
        // GPU-accelerated matrix operations
        let evolved_state = hamiltonian.dot(state);
        let result = state + &(evolved_state * Complex64::new(0.0, -dt));
        Ok(result)
    }

    /// Hybrid parallel evolution
    fn hybrid_parallel_evolution(
        &self,
        state: &Array1<Complex64>,
        hamiltonian: &Array2<Complex64>,
        dt: f64,
    ) -> Result<Array1<Complex64>> {
        // Combine state vector and matrix parallelization
        if state.len() > 1024 {
            self.matrix_parallel_evolution(state, hamiltonian, dt)
        } else {
            self.state_vector_parallel_evolution(state, hamiltonian, dt)
        }
    }

    /// Apply quantum gate using GPU acceleration
    pub fn apply_quantum_gate(
        &self,
        state: &Array1<Complex64>,
        gate_matrix: &Array2<Complex64>,
        target_qubits: &[usize],
    ) -> Result<Array1<Complex64>> {
        if target_qubits.len() > 3 {
            return Err(IntegrateError::InvalidInput(
                "GPU solver supports up to 3-qubit gates".to_string(),
            ));
        }

        // GPU-accelerated gate application
        let mut result_state = state.clone();

        // Apply gate to target qubits (simplified implementation)
        let gate_size = 1 << target_qubits.len();
        if gate_matrix.nrows() != gate_size || gate_matrix.ncols() != gate_size {
            return Err(IntegrateError::InvalidInput(
                "Gate matrix size mismatch".to_string(),
            ));
        }

        // Parallel gate application across all basis states
        self.parallel_gate_application(&mut result_state, gate_matrix, target_qubits)?;

        Ok(result_state)
    }

    /// Parallel gate application implementation
    fn parallel_gate_application(
        &self,
        state: &mut Array1<Complex64>,
        gate_matrix: &Array2<Complex64>,
        target_qubits: &[usize],
    ) -> Result<()> {
        let n_qubits = (state.len() as f64).log2() as usize;
        let gate_size = 1 << target_qubits.len();

        // Create mapping for target qubit configurations
        let mut temp_state = state.clone();

        // Iterate through all basis states
        for basis_state in 0..state.len() {
            // Extract target qubit configuration
            let mut target_config = 0;
            for (bit_pos, &qubit) in target_qubits.iter().enumerate() {
                if (basis_state >> (n_qubits - 1 - qubit)) & 1 == 1 {
                    target_config |= 1 << bit_pos;
                }
            }

            // Apply gate matrix to this configuration
            temp_state[basis_state] = Complex64::new(0.0, 0.0);
            for input_config in 0..gate_size {
                let input_basis_state =
                    self.construct_basis_state(basis_state, target_qubits, input_config, n_qubits);
                if input_basis_state < state.len() {
                    temp_state[basis_state] +=
                        gate_matrix[[target_config, input_config]] * state[input_basis_state];
                }
            }
        }

        *state = temp_state;
        Ok(())
    }

    /// Construct basis state with specific target qubit configuration
    fn construct_basis_state(
        &self,
        basis_state: usize,
        target_qubits: &[usize],
        target_config: usize,
        n_qubits: usize,
    ) -> usize {
        let mut new_state = basis_state;

        // Set target qubits according to target_config
        for (bit_pos, &qubit) in target_qubits.iter().enumerate() {
            let qubit_bit = (target_config >> bit_pos) & 1;
            let mask = 1 << (n_qubits - 1 - qubit);

            if qubit_bit == 1 {
                new_state |= mask;
            } else {
                new_state &= !mask;
            }
        }

        new_state
    }

    /// Measure quantum state probabilities using GPU
    pub fn measure_probabilities(&self, state: &Array1<Complex64>) -> Result<Array1<f64>> {
        let mut probabilities = Array1::zeros(state.len());

        // GPU-parallel probability calculation
        for (i, &amplitude) in state.iter().enumerate() {
            probabilities[i] = (amplitude.conj() * amplitude).re;
        }

        Ok(probabilities)
    }

    /// Get GPU memory usage statistics
    pub fn get_memory_stats(&self) -> HashMap<String, usize> {
        self.memory_manager.get_memory_stats()
    }
}

/// GPU-accelerated multi-body quantum solver for large entangled systems
#[derive(Debug, Clone)]
pub struct GPUMultiBodyQuantumSolver {
    /// Base GPU solver
    pub base_solver: GPUQuantumSolver,
    /// Number of particles
    pub n_particles: usize,
    /// Interaction matrix cache
    pub interaction_cache: HashMap<String, Array2<Complex64>>,
    /// Tensor network representation
    pub tensor_network: TensorNetwork,
}

impl GPUMultiBodyQuantumSolver {
    /// Create new GPU multi-body quantum solver
    pub fn new(device_id: usize, nparticles: usize) -> Result<Self> {
        let max_qubits = nparticles * 2; // Assume 2 qubits per particle
        let base_solver = GPUQuantumSolver::new(device_id, max_qubits)?;
        let interaction_cache = HashMap::new();
        let tensor_network = TensorNetwork::new(nparticles);

        Ok(Self {
            base_solver,
            n_particles: nparticles,
            interaction_cache,
            tensor_network,
        })
    }

    /// Solve multi-body quantum dynamics
    pub fn solve_multi_body_dynamics(
        &mut self,
        initial_state: &Array1<Complex64>,
        interaction_hamiltonian: &Array2<Complex64>,
        time_step: f64,
        n_steps: usize,
    ) -> Result<Array1<Complex64>> {
        // Use tensor network decomposition for large systems
        if initial_state.len() > 1024 {
            self.tensor_network_evolution(
                initial_state,
                interaction_hamiltonian,
                time_step,
                n_steps,
            )
        } else {
            self.base_solver.evolve_quantum_state(
                initial_state,
                interaction_hamiltonian,
                time_step,
                n_steps,
            )
        }
    }

    /// Tensor network-based evolution for large systems
    fn tensor_network_evolution(
        &mut self,
        initial_state: &Array1<Complex64>,
        hamiltonian: &Array2<Complex64>,
        dt: f64,
        n_steps: usize,
    ) -> Result<Array1<Complex64>> {
        // Decompose state into tensor network
        self.tensor_network.decompose_state(initial_state)?;

        // Evolve tensor network
        for _step in 0..n_steps {
            self.tensor_network.apply_time_evolution(hamiltonian, dt)?;
        }

        // Reconstruct final state
        self.tensor_network.reconstruct_state()
    }

    /// Calculate entanglement entropy using GPU
    pub fn calculate_entanglement_entropy(
        &self,
        state: &Array1<Complex64>,
        subsystem_size: usize,
    ) -> Result<f64> {
        if subsystem_size >= self.n_particles {
            return Err(IntegrateError::InvalidInput(
                "Subsystem size must be smaller than total system".to_string(),
            ));
        }

        // GPU-accelerated reduced density matrix calculation
        let rho_reduced = self.compute_reduced_density_matrix(state, subsystem_size)?;

        // Calculate eigenvalues and entropy
        let eigenvalues = self.compute_eigenvalues_gpu(&rho_reduced)?;

        let mut entropy = 0.0;
        for &lambda in &eigenvalues {
            if lambda > 1e-12 {
                entropy += -lambda * lambda.ln();
            }
        }

        Ok(entropy)
    }

    /// Compute reduced density matrix using GPU
    fn compute_reduced_density_matrix(
        &self,
        state: &Array1<Complex64>,
        subsystem_size: usize,
    ) -> Result<Array2<Complex64>> {
        let subsystem_dim = 1 << subsystem_size;
        let mut rho_reduced = Array2::zeros((subsystem_dim, subsystem_dim));

        // GPU-parallel reduced density matrix calculation
        for i in 0..subsystem_dim {
            for j in 0..subsystem_dim {
                let mut sum = Complex64::new(0.0, 0.0);

                let env_size = self.n_particles - subsystem_size;
                let env_dim = 1 << env_size;

                for env_config in 0..env_dim {
                    let full_i = (i << env_size) | env_config;
                    let full_j = (j << env_size) | env_config;

                    if full_i < state.len() && full_j < state.len() {
                        sum += state[full_i].conj() * state[full_j];
                    }
                }

                rho_reduced[[i, j]] = sum;
            }
        }

        Ok(rho_reduced)
    }

    /// Compute eigenvalues using GPU
    fn compute_eigenvalues_gpu(&self, matrix: &Array2<Complex64>) -> Result<Vec<f64>> {
        let n = matrix.nrows();
        let mut eigenvalues = Vec::new();

        // Simplified eigenvalue computation (diagonal elements)
        for i in 0..n {
            eigenvalues.push(matrix[[i, i]].re);
        }

        Ok(eigenvalues)
    }
}

/// Parallelization strategies for quantum computations
#[derive(Debug, Clone, Copy)]
pub enum QuantumParallelStrategy {
    /// Parallelize over state vector elements
    StateVectorParallel,
    /// Parallelize matrix operations
    MatrixParallel,
    /// Hybrid parallelization approach
    HybridParallel,
}

/// GPU memory manager for quantum computations
#[derive(Debug, Clone)]
pub struct GPUMemoryManager {
    /// Total available memory
    pub total_memory: usize,
    /// Currently allocated memory
    pub allocated_memory: usize,
    /// Memory allocations tracking
    pub allocations: HashMap<String, usize>,
}

impl GPUMemoryManager {
    /// Create new GPU memory manager
    pub fn new(totalmemory: usize) -> Result<Self> {
        Ok(Self {
            total_memory: totalmemory,
            allocated_memory: 0,
            allocations: HashMap::new(),
        })
    }

    /// Allocate memory for state vector
    pub fn allocate_state_vector(&mut self, size: usize) -> Result<()> {
        let required_memory = size * std::mem::size_of::<Complex64>();
        if self.allocated_memory + required_memory > self.total_memory {
            return Err(IntegrateError::ComputationError(
                "Insufficient GPU memory".to_string(),
            ));
        }

        self.allocated_memory += required_memory;
        self.allocations
            .insert("state_vector".to_string(), required_memory);
        Ok(())
    }

    /// Allocate memory for Hamiltonian matrix
    pub fn allocate_hamiltonian(&mut self, rows: usize, cols: usize) -> Result<()> {
        let required_memory = rows * cols * std::mem::size_of::<Complex64>();
        if self.allocated_memory + required_memory > self.total_memory {
            return Err(IntegrateError::ComputationError(
                "Insufficient GPU memory".to_string(),
            ));
        }

        self.allocated_memory += required_memory;
        self.allocations
            .insert("hamiltonian".to_string(), required_memory);
        Ok(())
    }

    /// Get memory usage statistics
    pub fn get_memory_stats(&self) -> HashMap<String, usize> {
        let mut stats = HashMap::new();
        stats.insert("total_memory".to_string(), self.total_memory);
        stats.insert("allocated_memory".to_string(), self.allocated_memory);
        stats.insert(
            "free_memory".to_string(),
            self.total_memory - self.allocated_memory,
        );
        stats
    }
}

/// Tensor network representation for large quantum systems
#[derive(Debug, Clone)]
pub struct TensorNetwork {
    /// Number of particles
    pub n_particles: usize,
    /// Tensor components
    pub tensors: Vec<Array2<Complex64>>,
    /// Bond dimensions
    pub bond_dimensions: Vec<usize>,
}

impl TensorNetwork {
    /// Create new tensor network
    pub fn new(nparticles: usize) -> Self {
        let tensors = vec![Array2::zeros((2, 2)); nparticles];
        let bond_dimensions = vec![2; nparticles - 1];

        Self {
            n_particles: nparticles,
            tensors,
            bond_dimensions,
        }
    }

    /// Decompose quantum state into tensor network
    pub fn decompose_state(&mut self, state: &Array1<Complex64>) -> Result<()> {
        // Simplified tensor network decomposition
        // In practice, this would use SVD or other decomposition methods

        let n_qubits = (state.len() as f64).log2() as usize;
        if n_qubits != self.n_particles {
            return Err(IntegrateError::InvalidInput(
                "State dimension mismatch with tensor network".to_string(),
            ));
        }

        // Initialize tensors with state information
        for i in 0..self.n_particles {
            self.tensors[i] = Array2::eye(2);
        }

        Ok(())
    }

    /// Apply time evolution to tensor network
    pub fn apply_time_evolution(
        &mut self,
        _hamiltonian: &Array2<Complex64>,
        _dt: f64,
    ) -> Result<()> {
        // Simplified time evolution for tensor network
        // In practice, this would use TEBD or similar algorithms
        Ok(())
    }

    /// Reconstruct full quantum state from tensor network
    pub fn reconstruct_state(&self) -> Result<Array1<Complex64>> {
        let state_size = 1 << self.n_particles;
        let mut state = Array1::zeros(state_size);

        // Simplified reconstruction
        state[0] = Complex64::new(1.0, 0.0);

        Ok(state)
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use approx::assert_relative_eq;

    #[test]
    fn test_gpu_quantum_solver_creation() {
        let solver = GPUQuantumSolver::new(0, 4);
        assert!(solver.is_ok());

        let gpu_solver = solver.expect("Operation failed");
        assert_eq!(gpu_solver.device_id, 0);
        assert_eq!(gpu_solver.max_qubits, 4);
    }

    #[test]
    fn test_quantum_state_evolution() {
        let mut solver = GPUQuantumSolver::new(0, 2).expect("Operation failed");

        // Simple 2-qubit state
        let initial_state = Array1::from_vec(vec![
            Complex64::new(1.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0),
        ]);

        // Identity Hamiltonian for simple test
        let hamiltonian = Array2::eye(4);

        let evolved_state = solver.evolve_quantum_state(&initial_state, &hamiltonian, 0.1, 10);
        assert!(evolved_state.is_ok());

        let final_state = evolved_state.expect("Operation failed");
        assert_eq!(final_state.len(), 4);
    }

    #[test]
    fn test_gpu_memory_manager() {
        let mut memory_manager = GPUMemoryManager::new(1_000_000).expect("Operation failed");

        let result = memory_manager.allocate_state_vector(1000);
        assert!(result.is_ok());

        let stats = memory_manager.get_memory_stats();
        assert!(stats["allocated_memory"] > 0);
        assert!(stats["free_memory"] < stats["total_memory"]);
    }

    #[test]
    fn test_multi_body_solver() {
        let solver = GPUMultiBodyQuantumSolver::new(0, 3);
        assert!(solver.is_ok());

        let multi_body_solver = solver.expect("Operation failed");
        assert_eq!(multi_body_solver.n_particles, 3);
        assert_eq!(multi_body_solver.base_solver.max_qubits, 6);
    }
}