quantrs2-core 0.1.3

//! GPU Acceleration for Large-Scale Quantum Simulations
//!
//! This module provides GPU-accelerated implementations of quantum operations
//! for large-scale simulations, supporting CUDA, OpenCL, and ROCm backends.

use crate::{
    error::{QuantRS2Error, QuantRS2Result},
    matrix_ops::QuantumMatrix,
    tensor_network::{Tensor, TensorNetwork},
};
use scirs2_core::ndarray::{Array1, Array2, ArrayD, Axis, IxDyn};
use scirs2_core::Complex64;
use std::{
    collections::HashMap,
    sync::{Arc, Mutex, RwLock},
    fmt,
};

/// GPU backend types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum GpuBackend {
    CUDA,
    OpenCL,
    ROCm,
    WebGPU,
    Metal,
}

impl fmt::Display for GpuBackend {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        match self {
            GpuBackend::CUDA => write!(f, "CUDA"),
            GpuBackend::OpenCL => write!(f, "OpenCL"),
            GpuBackend::ROCm => write!(f, "ROCm"),
            GpuBackend::WebGPU => write!(f, "WebGPU"),
            GpuBackend::Metal => write!(f, "Metal"),
        }
    }
}

/// GPU device information
#[derive(Debug, Clone)]
pub struct GpuDevice {
    pub id: u32,
    pub name: String,
    pub backend: GpuBackend,
    pub memory_size: usize,
    pub compute_units: u32,
    pub max_work_group_size: usize,
    pub supports_double_precision: bool,
    pub is_available: bool,
}

/// GPU memory buffer for quantum data
#[derive(Debug)]
pub struct GpuBuffer {
    buffer_id: u64,
    size: usize,
    device_id: u32,
    backend: GpuBackend,
    is_pinned: bool,
}

impl GpuBuffer {
    /// Create a new GPU buffer
    pub fn new(device_id: u32, backend: GpuBackend, size: usize, pinned: bool) -> QuantRS2Result<Self> {
        let buffer_id = Self::allocate_buffer(device_id, backend, size, pinned)?;
        Ok(Self {
            buffer_id,
            size,
            device_id,
            backend,
            is_pinned: pinned,
        })
    }

    /// Allocate GPU buffer (mock implementation)
    fn allocate_buffer(device_id: u32, backend: GpuBackend, size: usize, _pinned: bool) -> QuantRS2Result<u64> {
        // In a real implementation, this would call CUDA/OpenCL/ROCm APIs
        static NEXT_BUFFER_ID: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(1);

        if size == 0 {
            return Err(QuantRS2Error::InvalidParameter("Buffer size cannot be zero".to_string()));
        }

        // Simulate device availability check
        match backend {
            GpuBackend::CUDA => {
                if device_id >= 8 {
                    return Err(QuantRS2Error::InvalidParameter("CUDA device ID out of range".to_string()));
                }
            }
            GpuBackend::OpenCL | GpuBackend::ROCm => {
                if device_id >= 16 {
                    return Err(QuantRS2Error::InvalidParameter("Device ID out of range".to_string()));
                }
            }
            _ => {}
        }

        Ok(NEXT_BUFFER_ID.fetch_add(1, std::sync::atomic::Ordering::Relaxed))
    }

    /// Copy data to GPU
    pub fn copy_from_host(&mut self, data: &[Complex64]) -> QuantRS2Result<()> {
        if data.len() * std::mem::size_of::<Complex64>() > self.size {
            return Err(QuantRS2Error::InvalidParameter("Data size exceeds buffer capacity".to_string()));
        }

        // Mock implementation - in real code, this would copy to GPU memory
        println!("Copying {} complex numbers to GPU buffer {} on {} device {}",
                 data.len(), self.buffer_id, self.backend, self.device_id);
        Ok(())
    }

    /// Copy data from GPU
    pub fn copy_to_host(&self, data: &mut [Complex64]) -> QuantRS2Result<()> {
        if data.len() * std::mem::size_of::<Complex64>() > self.size {
            return Err(QuantRS2Error::InvalidParameter("Data size exceeds buffer capacity".to_string()));
        }

        // Mock implementation - in real code, this would copy from GPU memory
        println!("Copying {} complex numbers from GPU buffer {} on {} device {}",
                 data.len(), self.buffer_id, self.backend, self.device_id);
        Ok(())
    }
}

impl Drop for GpuBuffer {
    fn drop(&mut self) {
        // Mock cleanup
        println!("Deallocating GPU buffer {} on {} device {}",
                 self.buffer_id, self.backend, self.device_id);
    }
}

/// GPU context for quantum operations
#[derive(Debug)]
pub struct GpuContext {
    devices: Vec<GpuDevice>,
    active_device: Option<u32>,
    backend: GpuBackend,
    kernels: Arc<RwLock<HashMap<String, CompiledKernel>>>,
    memory_pool: Arc<Mutex<GpuMemoryPool>>,
}

/// Compiled GPU kernel
#[derive(Debug, Clone)]
pub struct CompiledKernel {
    kernel_id: u64,
    name: String,
    source_code: String,
    device_id: u32,
    backend: GpuBackend,
    work_group_size: usize,
}

/// GPU memory pool for efficient allocation
#[derive(Debug)]
pub struct GpuMemoryPool {
    free_buffers: Vec<GpuBuffer>,
    allocated_bytes: usize,
    peak_allocation: usize,
    backend: GpuBackend,
}

impl GpuMemoryPool {
    fn new(backend: GpuBackend) -> Self {
        Self {
            free_buffers: Vec::new(),
            allocated_bytes: 0,
            peak_allocation: 0,
            backend,
        }
    }

    fn allocate(&mut self, device_id: u32, size: usize) -> QuantRS2Result<GpuBuffer> {
        // Try to reuse existing buffer
        if let Some(pos) = self.free_buffers.iter().position(|buf|
            buf.device_id == device_id && buf.size >= size) {
            return Ok(self.free_buffers.remove(pos));
        }

        // Allocate new buffer
        let buffer = GpuBuffer::new(device_id, self.backend, size, false)?;
        self.allocated_bytes += size;
        self.peak_allocation = self.peak_allocation.max(self.allocated_bytes);
        Ok(buffer)
    }

    fn deallocate(&mut self, buffer: GpuBuffer) {
        self.allocated_bytes = self.allocated_bytes.saturating_sub(buffer.size);
        self.free_buffers.push(buffer);
    }
}

impl GpuContext {
    /// Create a new GPU context
    pub fn new(backend: GpuBackend) -> QuantRS2Result<Self> {
        let devices = Self::discover_devices(backend)?;

        if devices.is_empty() {
            return Err(QuantRS2Error::NoHardwareAvailable(
                format!("No {} devices found", backend)
            ));
        }

        Ok(Self {
            active_device: Some(devices[0].id),
            devices,
            backend,
            kernels: Arc::new(RwLock::new(HashMap::new())),
            memory_pool: Arc::new(Mutex::new(GpuMemoryPool::new(backend))),
        })
    }

    /// Discover available GPU devices
    fn discover_devices(backend: GpuBackend) -> QuantRS2Result<Vec<GpuDevice>> {
        // Mock device discovery - in real implementation, this would query actual devices
        match backend {
            GpuBackend::CUDA => Ok(vec![
                GpuDevice {
                    id: 0,
                    name: "NVIDIA GeForce RTX 4090".to_string(),
                    backend,
                    memory_size: 24 * 1024 * 1024 * 1024, // 24GB
                    compute_units: 128,
                    max_work_group_size: 1024,
                    supports_double_precision: true,
                    is_available: true,
                },
                GpuDevice {
                    id: 1,
                    name: "NVIDIA A100".to_string(),
                    backend,
                    memory_size: 80 * 1024 * 1024 * 1024, // 80GB
                    compute_units: 108,
                    max_work_group_size: 1024,
                    supports_double_precision: true,
                    is_available: true,
                },
            ]),
            GpuBackend::OpenCL => Ok(vec![
                GpuDevice {
                    id: 0,
                    name: "Intel UHD Graphics".to_string(),
                    backend,
                    memory_size: 8 * 1024 * 1024 * 1024, // 8GB
                    compute_units: 24,
                    max_work_group_size: 256,
                    supports_double_precision: false,
                    is_available: true,
                },
            ]),
            GpuBackend::ROCm => Ok(vec![
                GpuDevice {
                    id: 0,
                    name: "AMD Radeon RX 7900 XTX".to_string(),
                    backend,
                    memory_size: 24 * 1024 * 1024 * 1024, // 24GB
                    compute_units: 96,
                    max_work_group_size: 1024,
                    supports_double_precision: true,
                    is_available: true,
                },
            ]),
            _ => Ok(vec![]),
        }
    }

    /// Set the active device
    pub fn set_active_device(&mut self, device_id: u32) -> QuantRS2Result<()> {
        if !self.devices.iter().any(|d| d.id == device_id && d.is_available) {
            return Err(QuantRS2Error::InvalidParameter(
                format!("Device {} not available", device_id)
            ));
        }
        self.active_device = Some(device_id);
        Ok(())
    }

    /// Get active device information
    pub fn active_device(&self) -> Option<&GpuDevice> {
        self.active_device.and_then(|id|
            self.devices.iter().find(|d| d.id == id))
    }

    /// Compile a kernel
    pub fn compile_kernel(&self, name: &str, source: &str) -> QuantRS2Result<()> {
        let device_id = self.active_device.ok_or_else(||
            QuantRS2Error::InvalidOperation("No active device".to_string()))?;

        let kernel = CompiledKernel {
            kernel_id: rand::random(),
            name: name.to_string(),
            source_code: source.to_string(),
            device_id,
            backend: self.backend,
            work_group_size: 256, // Default work group size
        };

        println!("Compiling kernel '{}' for {} device {}", name, self.backend, device_id);

        self.kernels
            .write()
            .map_err(|e| QuantRS2Error::LockPoisoned(format!("Kernels RwLock poisoned: {e}")))?
            .insert(name.to_string(), kernel);
        Ok(())
    }

    /// Execute a kernel
    pub fn execute_kernel(&self, name: &str, buffers: &[&GpuBuffer], params: &[f64]) -> QuantRS2Result<()> {
        let kernels = self
            .kernels
            .read()
            .map_err(|e| QuantRS2Error::LockPoisoned(format!("Kernels RwLock poisoned: {e}")))?;
        let kernel = kernels.get(name).ok_or_else(||
            QuantRS2Error::InvalidOperation(format!("Kernel '{}' not found", name)))?;

        println!("Executing kernel '{}' with {} buffers and {} parameters",
                 name, buffers.len(), params.len());

        // Mock kernel execution
        std::thread::sleep(std::time::Duration::from_millis(1));
        Ok(())
    }

    /// Allocate GPU buffer
    pub fn allocate_buffer(&self, size: usize) -> QuantRS2Result<GpuBuffer> {
        let device_id = self.active_device.ok_or_else(||
            QuantRS2Error::InvalidOperation("No active device".to_string()))?;

        self.memory_pool
            .lock()
            .map_err(|e| QuantRS2Error::LockPoisoned(format!("Memory pool Mutex poisoned: {e}")))?
            .allocate(device_id, size)
    }

    /// Deallocate GPU buffer
    pub fn deallocate_buffer(&self, buffer: GpuBuffer) -> QuantRS2Result<()> {
        self.memory_pool
            .lock()
            .map_err(|e| QuantRS2Error::LockPoisoned(format!("Memory pool Mutex poisoned: {e}")))?
            .deallocate(buffer);
        Ok(())
    }
}

/// GPU-accelerated state vector simulator
#[derive(Debug)]
pub struct GpuStateVectorSimulator {
    context: Arc<GpuContext>,
    state_buffer: Option<GpuBuffer>,
    temp_buffer: Option<GpuBuffer>,
    num_qubits: usize,
    state_size: usize,
}

impl GpuStateVectorSimulator {
    /// Create a new GPU state vector simulator
    pub fn new(context: Arc<GpuContext>, num_qubits: usize) -> QuantRS2Result<Self> {
        if num_qubits > 50 {
            return Err(QuantRS2Error::UnsupportedQubits(
                num_qubits,
                "Maximum 50 qubits supported for state vector simulation".to_string()
            ));
        }

        let state_size = 1 << num_qubits;
        let buffer_size = state_size * std::mem::size_of::<Complex64>();

        // Compile required kernels
        context.compile_kernel("apply_single_qubit_gate", SINGLE_QUBIT_GATE_KERNEL)?;
        context.compile_kernel("apply_two_qubit_gate", TWO_QUBIT_GATE_KERNEL)?;
        context.compile_kernel("apply_phase_rotation", PHASE_ROTATION_KERNEL)?;
        context.compile_kernel("compute_expectation", EXPECTATION_VALUE_KERNEL)?;

        Ok(Self {
            context,
            state_buffer: None,
            temp_buffer: None,
            num_qubits,
            state_size,
        })
    }

    /// Initialize state vector on GPU
    pub fn initialize_state(&mut self, initial_state: &[Complex64]) -> QuantRS2Result<()> {
        if initial_state.len() != self.state_size {
            return Err(QuantRS2Error::InvalidInput(
                format!("Expected {} amplitudes, got {}", self.state_size, initial_state.len())
            ));
        }

        let buffer_size = self.state_size * std::mem::size_of::<Complex64>();

        // Allocate state buffer
        let mut state_buffer = self.context.allocate_buffer(buffer_size)?;
        state_buffer.copy_from_host(initial_state)?;
        self.state_buffer = Some(state_buffer);

        // Allocate temporary buffer
        let temp_buffer = self.context.allocate_buffer(buffer_size)?;
        self.temp_buffer = Some(temp_buffer);

        Ok(())
    }

    /// Apply a single-qubit gate
    pub fn apply_single_qubit_gate(&mut self, qubit: usize, gate_matrix: &[Complex64]) -> QuantRS2Result<()> {
        if gate_matrix.len() != 4 {
            return Err(QuantRS2Error::InvalidInput("Single-qubit gate must be 2x2".to_string()));
        }

        if qubit >= self.num_qubits {
            return Err(QuantRS2Error::InvalidQubitId(qubit as u32));
        }

        let state_buffer = self.state_buffer.as_ref().ok_or_else(||
            QuantRS2Error::InvalidOperation("State not initialized".to_string()))?;

        // Copy gate matrix to device (simplified)
        let gate_params = vec![
            gate_matrix[0].re, gate_matrix[0].im,
            gate_matrix[1].re, gate_matrix[1].im,
            gate_matrix[2].re, gate_matrix[2].im,
            gate_matrix[3].re, gate_matrix[3].im,
            qubit as f64,
        ];

        self.context.execute_kernel("apply_single_qubit_gate", &[state_buffer], &gate_params)?;
        Ok(())
    }

    /// Apply a two-qubit gate
    pub fn apply_two_qubit_gate(&mut self, control: usize, target: usize, gate_matrix: &[Complex64]) -> QuantRS2Result<()> {
        if gate_matrix.len() != 16 {
            return Err(QuantRS2Error::InvalidInput("Two-qubit gate must be 4x4".to_string()));
        }

        if control >= self.num_qubits || target >= self.num_qubits {
            return Err(QuantRS2Error::InvalidQubitId(control.max(target) as u32));
        }

        let state_buffer = self.state_buffer.as_ref().ok_or_else(||
            QuantRS2Error::InvalidOperation("State not initialized".to_string()))?;

        // Copy gate matrix to device (simplified)
        let mut gate_params = Vec::with_capacity(34);
        for elem in gate_matrix {
            gate_params.push(elem.re);
            gate_params.push(elem.im);
        }
        gate_params.push(control as f64);
        gate_params.push(target as f64);

        self.context.execute_kernel("apply_two_qubit_gate", &[state_buffer], &gate_params)?;
        Ok(())
    }

    /// Apply phase rotation to entire state
    pub fn apply_global_phase(&mut self, phase: f64) -> QuantRS2Result<()> {
        let state_buffer = self.state_buffer.as_ref().ok_or_else(||
            QuantRS2Error::InvalidOperation("State not initialized".to_string()))?;

        self.context.execute_kernel("apply_phase_rotation", &[state_buffer], &[phase])?;
        Ok(())
    }

    /// Compute expectation value of a Pauli operator
    pub fn expectation_value(&self, pauli_string: &str) -> QuantRS2Result<f64> {
        if pauli_string.len() != self.num_qubits {
            return Err(QuantRS2Error::InvalidInput(
                format!("Pauli string length {} must match number of qubits {}",
                        pauli_string.len(), self.num_qubits)
            ));
        }

        let state_buffer = self.state_buffer.as_ref().ok_or_else(||
            QuantRS2Error::InvalidOperation("State not initialized".to_string()))?;

        // Encode Pauli string (I=0, X=1, Y=2, Z=3)
        let mut pauli_encoding = Vec::new();
        for c in pauli_string.chars() {
            match c {
                'I' => pauli_encoding.push(0.0),
                'X' => pauli_encoding.push(1.0),
                'Y' => pauli_encoding.push(2.0),
                'Z' => pauli_encoding.push(3.0),
                _ => return Err(QuantRS2Error::InvalidInput(
                    format!("Invalid Pauli operator: {}", c)
                )),
            }
        }

        self.context.execute_kernel("compute_expectation", &[state_buffer], &pauli_encoding)?;

        // In real implementation, this would retrieve the result from GPU
        Ok(0.5) // Mock result
    }

    /// Get the current state vector
    pub fn get_state(&self) -> QuantRS2Result<Vec<Complex64>> {
        let state_buffer = self.state_buffer.as_ref().ok_or_else(||
            QuantRS2Error::InvalidOperation("State not initialized".to_string()))?;

        let mut state = vec![Complex64::new(0.0, 0.0); self.state_size];
        state_buffer.copy_to_host(&mut state)?;
        Ok(state)
    }

    /// Get probability distribution
    pub fn get_probabilities(&self) -> QuantRS2Result<Vec<f64>> {
        let state = self.get_state()?;
        Ok(state.iter().map(|amp| amp.norm_sqr()).collect())
    }
}

/// GPU-accelerated tensor network contractor
#[derive(Debug)]
pub struct GpuTensorNetworkContractor {
    context: Arc<GpuContext>,
    tensor_buffers: HashMap<usize, GpuBuffer>,
    contraction_cache: HashMap<String, Vec<Complex64>>,
}

impl GpuTensorNetworkContractor {
    /// Create a new GPU tensor network contractor
    pub fn new(context: Arc<GpuContext>) -> QuantRS2Result<Self> {
        // Compile tensor contraction kernels
        context.compile_kernel("contract_tensors", TENSOR_CONTRACTION_KERNEL)?;
        context.compile_kernel("tensor_svd", TENSOR_SVD_KERNEL)?;
        context.compile_kernel("tensor_qr", TENSOR_QR_KERNEL)?;

        Ok(Self {
            context,
            tensor_buffers: HashMap::new(),
            contraction_cache: HashMap::new(),
        })
    }

    /// Upload tensor to GPU
    pub fn upload_tensor(&mut self, tensor: &Tensor) -> QuantRS2Result<()> {
        let data_size = tensor.data.len() * std::mem::size_of::<Complex64>();
        let mut buffer = self.context.allocate_buffer(data_size)?;

        // Flatten tensor data
        let flattened: Vec<Complex64> = tensor.data.iter().cloned().collect();
        buffer.copy_from_host(&flattened)?;

        self.tensor_buffers.insert(tensor.id, buffer);
        Ok(())
    }

    /// Contract two tensors on GPU
    pub fn contract_tensors(&mut self, tensor1_id: usize, tensor2_id: usize,
                           contract_indices: &[(usize, usize)]) -> QuantRS2Result<Vec<Complex64>> {
        let buffer1 = self.tensor_buffers.get(&tensor1_id).ok_or_else(||
            QuantRS2Error::InvalidOperation(format!("Tensor {} not found on GPU", tensor1_id)))?;

        let buffer2 = self.tensor_buffers.get(&tensor2_id).ok_or_else(||
            QuantRS2Error::InvalidOperation(format!("Tensor {} not found on GPU", tensor2_id)))?;

        // Encode contraction indices
        let mut params = Vec::new();
        for (i, j) in contract_indices {
            params.push(*i as f64);
            params.push(*j as f64);
        }

        self.context.execute_kernel("contract_tensors", &[buffer1, buffer2], &params)?;

        // In real implementation, this would retrieve the result tensor
        Ok(vec![Complex64::new(1.0, 0.0)]) // Mock result
    }

    /// Perform SVD decomposition on GPU
    pub fn tensor_svd(&self, tensor_id: usize, split_index: usize) -> QuantRS2Result<(Vec<Complex64>, Vec<f64>, Vec<Complex64>)> {
        let buffer = self.tensor_buffers.get(&tensor_id).ok_or_else(||
            QuantRS2Error::InvalidOperation(format!("Tensor {} not found on GPU", tensor_id)))?;

        self.context.execute_kernel("tensor_svd", &[buffer], &[split_index as f64])?;

        // Mock SVD result
        Ok((
            vec![Complex64::new(1.0, 0.0)], // U
            vec![1.0],                      // S
            vec![Complex64::new(1.0, 0.0)], // V†
        ))
    }

    /// Optimize contraction order using GPU
    pub fn optimize_contraction_order(&self, network: &TensorNetwork) -> QuantRS2Result<Vec<(usize, usize)>> {
        // Simplified contraction order optimization
        let tensor_ids: Vec<usize> = network.tensors.keys().cloned().collect();
        let mut order = Vec::new();

        for i in 0..tensor_ids.len() - 1 {
            order.push((tensor_ids[i], tensor_ids[i + 1]));
        }

        Ok(order)
    }
}

/// Performance monitoring for GPU operations
#[derive(Debug, Clone)]
pub struct GpuPerformanceMonitor {
    operation_times: HashMap<String, Vec<f64>>,
    memory_usage: Vec<usize>,
    kernel_launches: u64,
    memory_transfers: u64,
}

impl GpuPerformanceMonitor {
    pub fn new() -> Self {
        Self {
            operation_times: HashMap::new(),
            memory_usage: Vec::new(),
            kernel_launches: 0,
            memory_transfers: 0,
        }
    }

    pub fn record_operation(&mut self, name: &str, duration_ms: f64) {
        self.operation_times.entry(name.to_string()).or_insert_with(Vec::new).push(duration_ms);
    }

    pub fn record_memory_usage(&mut self, bytes: usize) {
        self.memory_usage.push(bytes);
    }

    pub fn record_kernel_launch(&mut self) {
        self.kernel_launches += 1;
    }

    pub fn record_memory_transfer(&mut self) {
        self.memory_transfers += 1;
    }

    pub fn get_average_time(&self, operation: &str) -> Option<f64> {
        self.operation_times.get(operation).map(|times| {
            times.iter().sum::<f64>() / times.len() as f64
        })
    }

    pub fn get_peak_memory_usage(&self) -> Option<usize> {
        self.memory_usage.iter().max().cloned()
    }

    pub fn get_stats(&self) -> GpuPerformanceStats {
        GpuPerformanceStats {
            total_kernel_launches: self.kernel_launches,
            total_memory_transfers: self.memory_transfers,
            peak_memory_usage: self.get_peak_memory_usage().unwrap_or(0),
            operation_averages: self.operation_times.iter()
                .map(|(name, times)| (name.clone(), times.iter().sum::<f64>() / times.len() as f64))
                .collect(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct GpuPerformanceStats {
    pub total_kernel_launches: u64,
    pub total_memory_transfers: u64,
    pub peak_memory_usage: usize,
    pub operation_averages: HashMap<String, f64>,
}

// GPU Kernel source code (simplified CUDA-like pseudocode)
const SINGLE_QUBIT_GATE_KERNEL: &str = r#"
__global__ void apply_single_qubit_gate(Complex* state, Complex* gate, int qubit, int n_qubits) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int total_states = 1 << n_qubits;

    if (idx >= total_states / 2) return;

    int qubit_mask = 1 << qubit;
    int state_0 = (idx & ~qubit_mask) | ((idx & (qubit_mask - 1)));
    int state_1 = state_0 | qubit_mask;

    Complex amp_0 = state[state_0];
    Complex amp_1 = state[state_1];

    state[state_0] = gate[0] * amp_0 + gate[1] * amp_1;
    state[state_1] = gate[2] * amp_0 + gate[3] * amp_1;
}
"#;

const TWO_QUBIT_GATE_KERNEL: &str = r#"
__global__ void apply_two_qubit_gate(Complex* state, Complex* gate, int control, int target, int n_qubits) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int total_states = 1 << n_qubits;

    if (idx >= total_states / 4) return;

    // Implementation would handle two-qubit gate application
}
"#;

const PHASE_ROTATION_KERNEL: &str = r#"
__global__ void apply_phase_rotation(Complex* state, double phase, int size) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;

    if (idx >= size) return;

    Complex phase_factor = make_cuDoubleComplex(cos(phase), sin(phase));
    state[idx] = cuCmul(state[idx], phase_factor);
}
"#;

const EXPECTATION_VALUE_KERNEL: &str = r#"
__global__ void compute_expectation(Complex* state, double* paulis, double* result, int n_qubits) {
    // Implementation would compute expectation value of Pauli string
}
"#;

const TENSOR_CONTRACTION_KERNEL: &str = r#"
__global__ void contract_tensors(Complex* tensor1, Complex* tensor2,
                               int* indices, Complex* result,
                               int* shape1, int* shape2) {
    // Implementation would perform tensor contraction
}
"#;

const TENSOR_SVD_KERNEL: &str = r#"
__global__ void tensor_svd(Complex* tensor, Complex* U, double* S, Complex* Vt,
                          int rows, int cols) {
    // Implementation would perform SVD decomposition
}
"#;

const TENSOR_QR_KERNEL: &str = r#"
__global__ void tensor_qr(Complex* tensor, Complex* Q, Complex* R,
                         int rows, int cols) {
    // Implementation would perform QR decomposition
}
"#;

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

    #[test]
    fn test_gpu_context_creation() {
        let context = GpuContext::new(GpuBackend::CUDA);
        assert!(context.is_ok());

        let context = context.expect("CUDA context should be created successfully");
        assert!(!context.devices.is_empty());
        assert!(context.active_device.is_some());
    }

    #[test]
    fn test_gpu_buffer_allocation() {
        let context =
            GpuContext::new(GpuBackend::CUDA).expect("CUDA context should be created successfully");
        let buffer = context.allocate_buffer(1024);
        assert!(buffer.is_ok());

        let buffer = buffer.expect("Buffer should be allocated successfully");
        assert_eq!(buffer.size, 1024);
    }

    #[test]
    fn test_state_vector_simulator() {
        let context = Arc::new(
            GpuContext::new(GpuBackend::CUDA).expect("CUDA context should be created successfully"),
        );
        let mut simulator = GpuStateVectorSimulator::new(context, 3)
            .expect("State vector simulator should be created successfully");

        // Initialize with |000⟩ state
        let initial_state = 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),
            Complex64::new(0.0, 0.0), Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0), Complex64::new(0.0, 0.0),
        ];

        assert!(simulator.initialize_state(&initial_state).is_ok());

        // Apply Hadamard gate on qubit 0
        let hadamard = vec![
            Complex64::new(1.0/2.0_f64.sqrt(), 0.0), Complex64::new(1.0/2.0_f64.sqrt(), 0.0),
            Complex64::new(1.0/2.0_f64.sqrt(), 0.0), Complex64::new(-1.0/2.0_f64.sqrt(), 0.0),
        ];

        assert!(simulator.apply_single_qubit_gate(0, &hadamard).is_ok());
    }

    #[test]
    fn test_tensor_network_contractor() {
        let context = Arc::new(
            GpuContext::new(GpuBackend::CUDA).expect("CUDA context should be created successfully"),
        );
        let mut contractor = GpuTensorNetworkContractor::new(context)
            .expect("Tensor network contractor should be created successfully");

        // Create a simple tensor
        let data = scirs2_core::ndarray::Array::from_shape_vec(
            IxDyn(&[2, 2]),
            vec![
                Complex64::new(1.0, 0.0),
                Complex64::new(0.0, 0.0),
                Complex64::new(0.0, 0.0),
                Complex64::new(1.0, 0.0),
            ],
        )
        .expect("Array should be created from valid shape and data");

        let tensor = Tensor::new(0, data, vec!["i".to_string(), "j".to_string()]);
        assert!(contractor.upload_tensor(&tensor).is_ok());
    }

    #[test]
    fn test_performance_monitor() {
        let mut monitor = GpuPerformanceMonitor::new();

        monitor.record_operation("gate_application", 1.5);
        monitor.record_operation("gate_application", 2.0);
        monitor.record_memory_usage(1024);
        monitor.record_kernel_launch();

        assert_eq!(monitor.get_average_time("gate_application"), Some(1.75));
        assert_eq!(monitor.get_peak_memory_usage(), Some(1024));

        let stats = monitor.get_stats();
        assert_eq!(stats.total_kernel_launches, 1);
    }

    #[test]
    fn test_unsupported_backend() {
        let context = GpuContext::new(GpuBackend::WebGPU);
        // WebGPU might not have devices in our mock implementation
        if let Err(e) = context {
            assert!(matches!(e, QuantRS2Error::NoHardwareAvailable(_)));
        }
    }

    #[test]
    fn test_invalid_qubit_operations() {
        let context = Arc::new(
            GpuContext::new(GpuBackend::CUDA).expect("CUDA context should be created successfully"),
        );
        let mut simulator = GpuStateVectorSimulator::new(context, 2)
            .expect("State vector simulator should be created successfully");

        let initial_state = 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),
        ];

        simulator
            .initialize_state(&initial_state)
            .expect("State should be initialized successfully");

        // Try to apply gate to non-existent qubit
        let hadamard = vec![
            Complex64::new(1.0 / 2.0_f64.sqrt(), 0.0),
            Complex64::new(1.0 / 2.0_f64.sqrt(), 0.0),
            Complex64::new(1.0 / 2.0_f64.sqrt(), 0.0),
            Complex64::new(-1.0 / 2.0_f64.sqrt(), 0.0),
        ];

        let result = simulator.apply_single_qubit_gate(5, &hadamard);
        assert!(result.is_err());
        let err = result.expect_err("Expected InvalidQubitId error for qubit 5");
        assert!(matches!(err, QuantRS2Error::InvalidQubitId(_)));
    }

    #[test]
    fn test_expectation_value_calculation() {
        let context = Arc::new(
            GpuContext::new(GpuBackend::CUDA).expect("CUDA context should be created successfully"),
        );
        let simulator = GpuStateVectorSimulator::new(context, 3)
            .expect("State vector simulator should be created successfully");

        // Test invalid Pauli string
        let result = simulator.expectation_value("XYZ");
        assert!(result.is_ok());

        // Test invalid Pauli string length
        let result = simulator.expectation_value("XY");
        assert!(result.is_err());

        // Test invalid Pauli operator
        let result = simulator.expectation_value("ABC");
        assert!(result.is_err());
    }
}