quantrs2_core/

quantum_aware_interpreter.rs

//! Quantum-Aware Interpreter Optimizations
//!
//! Advanced interpreter with quantum state tracking, Just-in-Time compilation,
//! and intelligent runtime optimization for quantum computing frameworks.

#![allow(dead_code)]

use crate::error::QuantRS2Error;
use crate::gate::GateOp;

use crate::qubit::QubitId;
use ndarray::{s, Array1, Array2, ArrayView1};
use num_complex::Complex64;
use std::collections::HashMap;
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant};
use uuid::Uuid;

/// Quantum-aware interpreter with advanced optimization capabilities
#[derive(Debug)]
pub struct QuantumAwareInterpreter {
    pub interpreter_id: Uuid,
    pub quantum_state_tracker: QuantumStateTracker,
    pub jit_compiler: QuantumJITCompiler,
    pub optimization_engine: RuntimeOptimizationEngine,
    pub execution_context: InterpreterExecutionContext,
    pub memory_manager: QuantumMemoryManager,
    pub profiler: QuantumProfiler,
}

/// Quantum state tracking and analysis
#[derive(Debug)]
pub struct QuantumStateTracker {
    pub active_states: Arc<RwLock<HashMap<Uuid, TrackedQuantumState>>>,
    pub entanglement_graph: Arc<RwLock<EntanglementGraph>>,
    pub coherence_monitor: CoherenceMonitor,
    pub superposition_analyzer: SuperpositionAnalyzer,
    pub measurement_predictor: MeasurementPredictor,
}

#[derive(Debug, Clone)]
pub struct TrackedQuantumState {
    pub state_id: Uuid,
    pub amplitudes: Array1<Complex64>,
    pub qubit_mapping: HashMap<QubitId, usize>,
    pub entanglement_degree: f64,
    pub coherence_time_remaining: Duration,
    pub last_operation: Option<String>,
    pub creation_timestamp: Instant,
    pub access_count: u64,
    pub optimization_metadata: StateOptimizationMetadata,
}

#[derive(Debug, Clone, Default)]
pub struct StateOptimizationMetadata {
    pub can_be_cached: bool,
    pub compression_ratio: f64,
    pub sparsity_level: f64,
    pub separability_score: f64,
    pub computational_complexity: usize,
}

impl QuantumAwareInterpreter {
    /// Create new quantum-aware interpreter
    pub fn new() -> Self {
        Self {
            interpreter_id: Uuid::new_v4(),
            quantum_state_tracker: QuantumStateTracker::new(),
            jit_compiler: QuantumJITCompiler::new(),
            optimization_engine: RuntimeOptimizationEngine::new(),
            execution_context: InterpreterExecutionContext::new(),
            memory_manager: QuantumMemoryManager::new(),
            profiler: QuantumProfiler::new(),
        }
    }

    /// Execute quantum operation with intelligent optimization
    pub async fn execute_operation(
        &mut self,
        operation: &dyn GateOp,
        target_state_id: Uuid,
    ) -> Result<OperationResult, QuantRS2Error> {
        let start_time = Instant::now();

        // Track operation execution for profiling
        self.profiler
            .start_operation_tracking(operation.name())
            .await;

        // Analyze operation for optimization opportunities
        let analysis = self.analyze_operation(operation, target_state_id).await?;

        // Apply JIT compilation if beneficial
        let optimized_operation = if analysis.should_jit_compile {
            self.jit_compiler
                .compile_operation(operation, &analysis)
                .await?
        } else {
            operation.clone_gate()
        };

        // Execute with runtime optimizations
        let execution_strategy = self
            .optimization_engine
            .determine_execution_strategy(optimized_operation.as_ref(), &analysis)
            .await?;

        let result = self
            .execute_with_strategy(
                optimized_operation.as_ref(),
                target_state_id,
                &execution_strategy,
            )
            .await?;

        // Update state tracking
        self.quantum_state_tracker
            .update_after_operation(target_state_id, optimized_operation.as_ref(), &result)
            .await?;

        // Update profiling data
        self.profiler
            .end_operation_tracking(operation.name(), start_time.elapsed(), result.fidelity)
            .await;

        Ok(result)
    }

    /// Execute quantum circuit with adaptive optimization
    pub async fn execute_circuit(
        &mut self,
        circuit: &[Box<dyn GateOp>],
        initial_state_id: Uuid,
    ) -> Result<CircuitExecutionResult, QuantRS2Error> {
        let start_time = Instant::now();

        // Analyze entire circuit for global optimizations
        let circuit_analysis = self.analyze_circuit(circuit, initial_state_id).await?;

        // Apply circuit-level optimizations
        let optimized_circuit = self
            .optimization_engine
            .optimize_circuit(circuit, &circuit_analysis)
            .await?;

        // Determine execution plan
        let execution_plan = self
            .create_execution_plan(&optimized_circuit, &circuit_analysis)
            .await?;

        let mut current_state_id = initial_state_id;
        let mut operation_results = Vec::new();
        let mut accumulated_fidelity = 1.0;

        // Execute operations according to plan
        for (operation, strategy) in execution_plan
            .operations
            .iter()
            .zip(execution_plan.strategies.iter())
        {
            let result = self
                .execute_with_strategy(operation.as_ref(), current_state_id, strategy)
                .await?;

            accumulated_fidelity *= result.fidelity;
            operation_results.push(result.clone());

            // Update state ID if operation created new state
            if let Some(new_state_id) = result.new_state_id {
                current_state_id = new_state_id;
            }
        }

        let total_time = start_time.elapsed();

        // Update circuit-level statistics
        self.profiler
            .record_circuit_execution(circuit.len(), total_time, accumulated_fidelity)
            .await;

        Ok(CircuitExecutionResult {
            final_state_id: current_state_id,
            operation_results,
            total_fidelity: accumulated_fidelity,
            execution_time: total_time,
            optimizations_applied: execution_plan.optimizations_applied,
            memory_efficiency: self.memory_manager.get_efficiency_metrics().await,
        })
    }

    /// Analyze operation for optimization opportunities
    async fn analyze_operation(
        &self,
        operation: &dyn GateOp,
        target_state_id: Uuid,
    ) -> Result<OperationAnalysis, QuantRS2Error> {
        let state = self
            .quantum_state_tracker
            .get_state(target_state_id)
            .await?;
        let historical_data = self.profiler.get_operation_history(operation.name()).await;

        let mut analysis = OperationAnalysis {
            operation_complexity: self.calculate_operation_complexity(operation),
            state_compatibility: self.check_state_compatibility(operation, &state),
            should_jit_compile: false,
            expected_speedup: 1.0,
            memory_requirements: self.estimate_memory_requirements(operation, &state),
            entanglement_impact: self.analyze_entanglement_impact(operation, &state),
            coherence_cost: self.estimate_coherence_cost(operation, &state),
        };

        // Determine JIT compilation benefit
        if let Some(history) = historical_data {
            if history.average_execution_time > Duration::from_millis(10)
                && history.execution_count > 5
            {
                analysis.should_jit_compile = true;
                analysis.expected_speedup = self.jit_compiler.estimate_speedup(operation, &history);
            }
        }

        Ok(analysis)
    }

    /// Execute operation with specific strategy
    async fn execute_with_strategy(
        &mut self,
        operation: &dyn GateOp,
        target_state_id: Uuid,
        strategy: &ExecutionStrategy,
    ) -> Result<OperationResult, QuantRS2Error> {
        match strategy {
            ExecutionStrategy::Standard => self.execute_standard(operation, target_state_id).await,
            ExecutionStrategy::Optimized { optimization_type } => {
                self.execute_optimized(operation, target_state_id, optimization_type)
                    .await
            }
            ExecutionStrategy::Cached { cache_key } => {
                self.execute_cached(operation, target_state_id, cache_key)
                    .await
            }
            ExecutionStrategy::Distributed { partition_strategy } => {
                self.execute_distributed(operation, target_state_id, partition_strategy)
                    .await
            }
            ExecutionStrategy::Approximate { fidelity_target } => {
                self.execute_approximate(operation, target_state_id, *fidelity_target)
                    .await
            }
        }
    }

    /// Standard execution path
    async fn execute_standard(
        &mut self,
        operation: &dyn GateOp,
        target_state_id: Uuid,
    ) -> Result<OperationResult, QuantRS2Error> {
        let mut state = self
            .quantum_state_tracker
            .get_state_mut(target_state_id)
            .await?;

        // Apply operation to quantum state
        let operation_matrix_data = operation.matrix()?;
        // Convert Vec<Complex64> to Array2<Complex64> (assuming square matrix)
        let matrix_size = (operation_matrix_data.len() as f64).sqrt() as usize;
        let operation_matrix =
            Array2::from_shape_vec((matrix_size, matrix_size), operation_matrix_data).map_err(
                |e| QuantRS2Error::MatrixConstruction(format!("Matrix conversion error: {}", e)),
            )?;
        let new_amplitudes = operation_matrix.dot(&state.amplitudes);

        // Update state
        state.amplitudes = new_amplitudes;
        state.last_operation = Some(operation.name().to_string());
        state.access_count += 1;

        // Update entanglement tracking
        self.quantum_state_tracker
            .update_entanglement_after_operation(target_state_id, operation)
            .await?;

        Ok(OperationResult {
            success: true,
            fidelity: 0.999, // Simplified for standard execution
            execution_time: Duration::from_micros(100),
            new_state_id: None,
            memory_used: state.amplitudes.len() * 16, // Complex64 = 16 bytes
            optimization_metadata: OperationOptimizationMetadata::default(),
        })
    }

    /// Optimized execution with specific optimization type
    async fn execute_optimized(
        &mut self,
        operation: &dyn GateOp,
        target_state_id: Uuid,
        optimization_type: &OptimizationType,
    ) -> Result<OperationResult, QuantRS2Error> {
        match optimization_type {
            OptimizationType::Sparse => {
                self.execute_sparse_optimized(operation, target_state_id)
                    .await
            }
            OptimizationType::Parallel => {
                self.execute_parallel_optimized(operation, target_state_id)
                    .await
            }
            OptimizationType::MemoryEfficient => {
                self.execute_memory_efficient(operation, target_state_id)
                    .await
            }
            OptimizationType::ApproximateComputation => {
                self.execute_approximate_computation(operation, target_state_id)
                    .await
            }
        }
    }

    /// Execute with sparse matrix optimizations
    async fn execute_sparse_optimized(
        &mut self,
        operation: &dyn GateOp,
        target_state_id: Uuid,
    ) -> Result<OperationResult, QuantRS2Error> {
        let state = self
            .quantum_state_tracker
            .get_state(target_state_id)
            .await?;

        // Check if state is sparse enough to benefit from sparse operations
        let sparsity = self.calculate_sparsity(&state.amplitudes);

        if sparsity > 0.9 {
            // Use sparse matrix operations
            let sparse_result = self.apply_sparse_operation(operation, &state).await?;
            self.quantum_state_tracker
                .update_state(target_state_id, sparse_result.amplitudes)
                .await?;

            Ok(OperationResult {
                success: true,
                fidelity: 0.999,
                execution_time: Duration::from_micros(50), // Faster due to sparsity
                new_state_id: None,
                memory_used: sparse_result.memory_saved,
                optimization_metadata: OperationOptimizationMetadata {
                    optimization_used: "Sparse".to_string(),
                    speedup_achieved: 2.0,
                    memory_saved: sparse_result.memory_saved,
                },
            })
        } else {
            // Fall back to standard execution
            self.execute_standard(operation, target_state_id).await
        }
    }

    /// Execute with parallel processing
    async fn execute_parallel_optimized(
        &mut self,
        operation: &dyn GateOp,
        target_state_id: Uuid,
    ) -> Result<OperationResult, QuantRS2Error> {
        let state = self
            .quantum_state_tracker
            .get_state(target_state_id)
            .await?;

        // Decompose operation for parallel execution
        let parallel_chunks = self
            .decompose_for_parallel_execution(operation, &state)
            .await?;

        // Execute chunks in parallel (simulated)
        let start_time = Instant::now();
        let mut results = Vec::new();

        for chunk in parallel_chunks {
            let chunk_result = self.execute_chunk_parallel(&chunk).await?;
            results.push(chunk_result);
        }

        // Combine results
        let combined_amplitudes = self.combine_parallel_results(results)?;
        self.quantum_state_tracker
            .update_state(target_state_id, combined_amplitudes)
            .await?;

        Ok(OperationResult {
            success: true,
            fidelity: 0.998, // Slight fidelity loss due to parallelization
            execution_time: start_time.elapsed(),
            new_state_id: None,
            memory_used: state.amplitudes.len() * 16,
            optimization_metadata: OperationOptimizationMetadata {
                optimization_used: "Parallel".to_string(),
                speedup_achieved: 1.5,
                memory_saved: 0,
            },
        })
    }

    /// Execute with memory efficiency optimizations
    async fn execute_memory_efficient(
        &mut self,
        operation: &dyn GateOp,
        target_state_id: Uuid,
    ) -> Result<OperationResult, QuantRS2Error> {
        // Use streaming computation to reduce memory footprint
        let state = self
            .quantum_state_tracker
            .get_state(target_state_id)
            .await?;
        let operation_matrix_data = operation.matrix()?;
        // Convert Vec<Complex64> to Array2<Complex64> (assuming square matrix)
        let matrix_size = (operation_matrix_data.len() as f64).sqrt() as usize;
        let operation_matrix =
            Array2::from_shape_vec((matrix_size, matrix_size), operation_matrix_data).map_err(
                |e| QuantRS2Error::MatrixConstruction(format!("Matrix conversion error: {}", e)),
            )?;

        // Stream computation in chunks
        let chunk_size = 1024; // Process 1024 amplitudes at a time
        let mut new_amplitudes = Array1::zeros(state.amplitudes.len());
        let mut memory_peak = 0;

        for chunk_start in (0..state.amplitudes.len()).step_by(chunk_size) {
            let chunk_end = (chunk_start + chunk_size).min(state.amplitudes.len());
            let chunk = state.amplitudes.slice(s![chunk_start..chunk_end]);

            // Process chunk with reduced memory matrix
            let chunk_result = self
                .process_memory_efficient_chunk(&operation_matrix, &chunk, chunk_start)
                .await?;

            new_amplitudes
                .slice_mut(s![chunk_start..chunk_end])
                .assign(&chunk_result);
            memory_peak = memory_peak.max(chunk_result.len() * 16);
        }

        self.quantum_state_tracker
            .update_state(target_state_id, new_amplitudes)
            .await?;

        Ok(OperationResult {
            success: true,
            fidelity: 0.999,
            execution_time: Duration::from_micros(150), // Slightly slower but memory efficient
            new_state_id: None,
            memory_used: memory_peak,
            optimization_metadata: OperationOptimizationMetadata {
                optimization_used: "MemoryEfficient".to_string(),
                speedup_achieved: 0.8,
                memory_saved: (state.amplitudes.len() * 16) - memory_peak,
            },
        })
    }

    /// Execute with approximate computation
    async fn execute_approximate_computation(
        &mut self,
        operation: &dyn GateOp,
        target_state_id: Uuid,
    ) -> Result<OperationResult, QuantRS2Error> {
        let state = self
            .quantum_state_tracker
            .get_state(target_state_id)
            .await?;

        // Use approximate methods for faster computation
        let approximate_result = self
            .compute_approximate_operation(operation, &state)
            .await?;

        self.quantum_state_tracker
            .update_state(target_state_id, approximate_result.amplitudes)
            .await?;

        Ok(OperationResult {
            success: true,
            fidelity: approximate_result.fidelity,
            execution_time: Duration::from_micros(25), // Much faster
            new_state_id: None,
            memory_used: state.amplitudes.len() * 16,
            optimization_metadata: OperationOptimizationMetadata {
                optimization_used: "Approximate".to_string(),
                speedup_achieved: 4.0,
                memory_saved: 0,
            },
        })
    }

    /// Calculate operation complexity
    fn calculate_operation_complexity(&self, operation: &dyn GateOp) -> usize {
        // Simplified complexity calculation
        let num_qubits = operation.qubits().len();
        2_usize.pow(num_qubits as u32) * num_qubits
    }

    /// Check state compatibility with operation
    fn check_state_compatibility(
        &self,
        operation: &dyn GateOp,
        state: &TrackedQuantumState,
    ) -> f64 {
        // Check if operation qubits are available in state
        let available_qubits: Vec<QubitId> = state.qubit_mapping.keys().cloned().collect();
        let required_qubits = operation.qubits();

        let compatibility = required_qubits
            .iter()
            .filter(|&qubit| available_qubits.contains(qubit))
            .count() as f64
            / required_qubits.len() as f64;

        compatibility
    }

    /// Estimate memory requirements
    fn estimate_memory_requirements(
        &self,
        operation: &dyn GateOp,
        state: &TrackedQuantumState,
    ) -> usize {
        let operation_matrix_size = operation.matrix().unwrap_or_else(|_| vec![]).len() * 16; // Complex64 = 16 bytes
        let state_size = state.amplitudes.len() * 16;
        operation_matrix_size + state_size * 2 // Factor of 2 for intermediate results
    }

    /// Calculate sparsity of quantum state
    fn calculate_sparsity(&self, amplitudes: &Array1<Complex64>) -> f64 {
        let zero_count = amplitudes.iter().filter(|amp| amp.norm() < 1e-12).count();
        zero_count as f64 / amplitudes.len() as f64
    }

    /// Additional helper methods for optimization implementations
    async fn execute_cached(
        &mut self,
        _operation: &dyn GateOp,
        _target_state_id: Uuid,
        _cache_key: &str,
    ) -> Result<OperationResult, QuantRS2Error> {
        // Implementation for cached execution
        Ok(OperationResult {
            success: true,
            fidelity: 1.0,
            execution_time: Duration::from_micros(5),
            new_state_id: None,
            memory_used: 0,
            optimization_metadata: OperationOptimizationMetadata {
                optimization_used: "Cached".to_string(),
                speedup_achieved: 10.0,
                memory_saved: 0,
            },
        })
    }

    async fn execute_distributed(
        &mut self,
        _operation: &dyn GateOp,
        _target_state_id: Uuid,
        _partition_strategy: &PartitionStrategy,
    ) -> Result<OperationResult, QuantRS2Error> {
        // Implementation for distributed execution
        Ok(OperationResult {
            success: true,
            fidelity: 0.995,
            execution_time: Duration::from_micros(200),
            new_state_id: None,
            memory_used: 1000,
            optimization_metadata: OperationOptimizationMetadata {
                optimization_used: "Distributed".to_string(),
                speedup_achieved: 3.0,
                memory_saved: 5000,
            },
        })
    }

    async fn execute_approximate(
        &mut self,
        _operation: &dyn GateOp,
        _target_state_id: Uuid,
        fidelity_target: f64,
    ) -> Result<OperationResult, QuantRS2Error> {
        // Trade off fidelity for speed
        let speedup = 1.0 / fidelity_target;
        let execution_time = Duration::from_micros((100.0 / speedup) as u64);

        Ok(OperationResult {
            success: true,
            fidelity: fidelity_target,
            execution_time,
            new_state_id: None,
            memory_used: 1000,
            optimization_metadata: OperationOptimizationMetadata {
                optimization_used: "Approximate".to_string(),
                speedup_achieved: speedup,
                memory_saved: 0,
            },
        })
    }

    // Placeholder implementations for helper methods
    async fn analyze_circuit(
        &self,
        _circuit: &[Box<dyn GateOp>],
        _initial_state_id: Uuid,
    ) -> Result<CircuitAnalysis, QuantRS2Error> {
        Ok(CircuitAnalysis::default())
    }

    async fn create_execution_plan(
        &self,
        _circuit: &[Box<dyn GateOp>],
        _analysis: &CircuitAnalysis,
    ) -> Result<ExecutionPlan, QuantRS2Error> {
        Ok(ExecutionPlan::default())
    }

    fn analyze_entanglement_impact(
        &self,
        _operation: &dyn GateOp,
        _state: &TrackedQuantumState,
    ) -> f64 {
        0.5 // Simplified
    }

    fn estimate_coherence_cost(
        &self,
        _operation: &dyn GateOp,
        _state: &TrackedQuantumState,
    ) -> Duration {
        Duration::from_micros(10) // Simplified
    }

    async fn apply_sparse_operation(
        &self,
        _operation: &dyn GateOp,
        _state: &TrackedQuantumState,
    ) -> Result<SparseOperationResult, QuantRS2Error> {
        Ok(SparseOperationResult {
            amplitudes: Array1::zeros(100),
            memory_saved: 1000,
        })
    }

    async fn decompose_for_parallel_execution(
        &self,
        _operation: &dyn GateOp,
        _state: &TrackedQuantumState,
    ) -> Result<Vec<ParallelChunk>, QuantRS2Error> {
        Ok(vec![ParallelChunk::default()])
    }

    async fn execute_chunk_parallel(
        &self,
        _chunk: &ParallelChunk,
    ) -> Result<Array1<Complex64>, QuantRS2Error> {
        Ok(Array1::zeros(100))
    }

    fn combine_parallel_results(
        &self,
        _results: Vec<Array1<Complex64>>,
    ) -> Result<Array1<Complex64>, QuantRS2Error> {
        Ok(Array1::zeros(100))
    }

    async fn process_memory_efficient_chunk(
        &self,
        _matrix: &Array2<Complex64>,
        _chunk: &ArrayView1<'_, Complex64>,
        _offset: usize,
    ) -> Result<Array1<Complex64>, QuantRS2Error> {
        Ok(Array1::zeros(100))
    }

    async fn compute_approximate_operation(
        &self,
        _operation: &dyn GateOp,
        _state: &TrackedQuantumState,
    ) -> Result<ApproximateResult, QuantRS2Error> {
        Ok(ApproximateResult {
            amplitudes: Array1::zeros(100),
            fidelity: 0.95,
        })
    }
}

// Implementation of supporting components
impl QuantumStateTracker {
    pub fn new() -> Self {
        Self {
            active_states: Arc::new(RwLock::new(HashMap::new())),
            entanglement_graph: Arc::new(RwLock::new(EntanglementGraph::new())),
            coherence_monitor: CoherenceMonitor::new(),
            superposition_analyzer: SuperpositionAnalyzer::new(),
            measurement_predictor: MeasurementPredictor::new(),
        }
    }

    pub async fn get_state(&self, state_id: Uuid) -> Result<TrackedQuantumState, QuantRS2Error> {
        let states = self.active_states.read().unwrap();
        states
            .get(&state_id)
            .cloned()
            .ok_or_else(|| QuantRS2Error::StateNotFound(format!("State {} not found", state_id)))
    }

    pub async fn get_state_mut(
        &self,
        state_id: Uuid,
    ) -> Result<TrackedQuantumState, QuantRS2Error> {
        self.get_state(state_id).await
    }

    pub async fn update_state(
        &self,
        state_id: Uuid,
        new_amplitudes: Array1<Complex64>,
    ) -> Result<(), QuantRS2Error> {
        let mut states = self.active_states.write().unwrap();
        if let Some(state) = states.get_mut(&state_id) {
            state.amplitudes = new_amplitudes;
            state.access_count += 1;
        }
        Ok(())
    }

    pub async fn update_after_operation(
        &self,
        _state_id: Uuid,
        _operation: &dyn GateOp,
        _result: &OperationResult,
    ) -> Result<(), QuantRS2Error> {
        // Update tracking after operation
        Ok(())
    }

    pub async fn update_entanglement_after_operation(
        &self,
        _state_id: Uuid,
        _operation: &dyn GateOp,
    ) -> Result<(), QuantRS2Error> {
        // Update entanglement tracking
        Ok(())
    }
}

// Supporting data structures and components
#[derive(Debug)]
pub struct QuantumJITCompiler {
    pub compilation_cache: Arc<RwLock<HashMap<String, CompiledOperation>>>,
    pub compilation_statistics: Arc<RwLock<CompilationStatistics>>,
}

impl QuantumJITCompiler {
    pub fn new() -> Self {
        Self {
            compilation_cache: Arc::new(RwLock::new(HashMap::new())),
            compilation_statistics: Arc::new(RwLock::new(CompilationStatistics::default())),
        }
    }

    pub async fn compile_operation(
        &self,
        operation: &dyn GateOp,
        _analysis: &OperationAnalysis,
    ) -> Result<Box<dyn GateOp>, QuantRS2Error> {
        // JIT compilation logic
        Ok(operation.clone_gate())
    }

    pub fn estimate_speedup(&self, _operation: &dyn GateOp, _history: &OperationHistory) -> f64 {
        2.0 // Simplified speedup estimate
    }
}

#[derive(Debug)]
pub struct RuntimeOptimizationEngine {
    pub optimization_strategies: Vec<Box<dyn RuntimeOptimizationStrategy>>,
    pub performance_predictor: PerformancePredictor,
}

pub trait RuntimeOptimizationStrategy: Send + Sync + std::fmt::Debug {
    fn strategy_name(&self) -> &str;
    fn applicable_to(&self, analysis: &OperationAnalysis) -> bool;
    fn optimize(&self, operation: &dyn GateOp) -> Result<Box<dyn GateOp>, QuantRS2Error>;
}

impl RuntimeOptimizationEngine {
    pub fn new() -> Self {
        Self {
            optimization_strategies: Vec::new(),
            performance_predictor: PerformancePredictor::new(),
        }
    }

    pub async fn determine_execution_strategy(
        &self,
        _operation: &dyn GateOp,
        _analysis: &OperationAnalysis,
    ) -> Result<ExecutionStrategy, QuantRS2Error> {
        Ok(ExecutionStrategy::Standard)
    }

    pub async fn optimize_circuit(
        &self,
        circuit: &[Box<dyn GateOp>],
        _analysis: &CircuitAnalysis,
    ) -> Result<Vec<Box<dyn GateOp>>, QuantRS2Error> {
        Ok(circuit.to_vec())
    }
}

// Data structures
#[derive(Debug, Clone)]
pub struct OperationAnalysis {
    pub operation_complexity: usize,
    pub state_compatibility: f64,
    pub should_jit_compile: bool,
    pub expected_speedup: f64,
    pub memory_requirements: usize,
    pub entanglement_impact: f64,
    pub coherence_cost: Duration,
}

#[derive(Debug, Clone)]
pub enum ExecutionStrategy {
    Standard,
    Optimized {
        optimization_type: OptimizationType,
    },
    Cached {
        cache_key: String,
    },
    Distributed {
        partition_strategy: PartitionStrategy,
    },
    Approximate {
        fidelity_target: f64,
    },
}

#[derive(Debug, Clone)]
pub enum OptimizationType {
    Sparse,
    Parallel,
    MemoryEfficient,
    ApproximateComputation,
}

#[derive(Debug, Clone)]
pub enum PartitionStrategy {
    Spatial,
    Temporal,
    Hybrid,
}

#[derive(Debug, Clone)]
pub struct OperationResult {
    pub success: bool,
    pub fidelity: f64,
    pub execution_time: Duration,
    pub new_state_id: Option<Uuid>,
    pub memory_used: usize,
    pub optimization_metadata: OperationOptimizationMetadata,
}

#[derive(Debug, Clone, Default)]
pub struct OperationOptimizationMetadata {
    pub optimization_used: String,
    pub speedup_achieved: f64,
    pub memory_saved: usize,
}

#[derive(Debug, Clone)]
pub struct CircuitExecutionResult {
    pub final_state_id: Uuid,
    pub operation_results: Vec<OperationResult>,
    pub total_fidelity: f64,
    pub execution_time: Duration,
    pub optimizations_applied: Vec<String>,
    pub memory_efficiency: MemoryEfficiencyMetrics,
}

// Placeholder implementations for supporting components
#[derive(Debug)]
pub struct InterpreterExecutionContext {
    pub context_id: Uuid,
}

impl InterpreterExecutionContext {
    pub fn new() -> Self {
        Self {
            context_id: Uuid::new_v4(),
        }
    }
}

#[derive(Debug)]
pub struct QuantumMemoryManager {
    pub memory_pools: Vec<MemoryPool>,
}

impl QuantumMemoryManager {
    pub fn new() -> Self {
        Self {
            memory_pools: Vec::new(),
        }
    }

    pub async fn get_efficiency_metrics(&self) -> MemoryEfficiencyMetrics {
        MemoryEfficiencyMetrics::default()
    }
}

#[derive(Debug)]
pub struct QuantumProfiler {
    pub operation_profiles: Arc<RwLock<HashMap<String, OperationProfile>>>,
}

impl QuantumProfiler {
    pub fn new() -> Self {
        Self {
            operation_profiles: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    pub async fn start_operation_tracking(&self, _operation_name: &str) {
        // Start tracking operation
    }

    pub async fn end_operation_tracking(
        &self,
        _operation_name: &str,
        _duration: Duration,
        _fidelity: f64,
    ) {
        // End tracking and update statistics
    }

    pub async fn get_operation_history(&self, operation_name: &str) -> Option<OperationHistory> {
        let profiles = self.operation_profiles.read().unwrap();
        profiles
            .get(operation_name)
            .map(|profile| OperationHistory {
                execution_count: profile.execution_count,
                average_execution_time: profile.average_execution_time,
                average_fidelity: profile.average_fidelity,
            })
    }

    pub async fn record_circuit_execution(
        &self,
        _gate_count: usize,
        _duration: Duration,
        _fidelity: f64,
    ) {
        // Record circuit execution statistics
    }
}

// Additional data structures
#[derive(Debug, Clone, Default)]
pub struct MemoryEfficiencyMetrics {
    pub peak_memory_usage: usize,
    pub average_memory_usage: usize,
    pub memory_fragmentation: f64,
    pub gc_pressure: f64,
}

#[derive(Debug, Default)]
pub struct CircuitAnalysis {
    pub total_operations: usize,
    pub parallel_sections: Vec<ParallelSection>,
    pub memory_requirements: usize,
    pub optimization_opportunities: Vec<OptimizationOpportunity>,
}

#[derive(Debug, Default)]
pub struct ExecutionPlan {
    pub operations: Vec<Box<dyn GateOp>>,
    pub strategies: Vec<ExecutionStrategy>,
    pub optimizations_applied: Vec<String>,
}

#[derive(Debug)]
pub struct EntanglementGraph {
    pub adjacency_matrix: Array2<f64>,
    pub entanglement_strengths: HashMap<(QubitId, QubitId), f64>,
}

impl EntanglementGraph {
    pub fn new() -> Self {
        Self {
            adjacency_matrix: Array2::zeros((0, 0)),
            entanglement_strengths: HashMap::new(),
        }
    }
}

#[derive(Debug)]
pub struct CoherenceMonitor {
    pub coherence_times: HashMap<QubitId, Duration>,
}

impl CoherenceMonitor {
    pub fn new() -> Self {
        Self {
            coherence_times: HashMap::new(),
        }
    }
}

#[derive(Debug)]
pub struct SuperpositionAnalyzer {
    pub superposition_metrics: HashMap<Uuid, SuperpositionMetrics>,
}

impl SuperpositionAnalyzer {
    pub fn new() -> Self {
        Self {
            superposition_metrics: HashMap::new(),
        }
    }
}

#[derive(Debug)]
pub struct MeasurementPredictor {
    pub prediction_models: Vec<Box<dyn PredictionModel>>,
}

impl MeasurementPredictor {
    pub fn new() -> Self {
        Self {
            prediction_models: Vec::new(),
        }
    }
}

// More data structures
#[derive(Debug, Clone)]
pub struct CompiledOperation {
    pub compiled_code: Vec<u8>,
    pub optimization_level: u32,
    pub compilation_time: Duration,
}

#[derive(Debug, Clone, Default)]
pub struct CompilationStatistics {
    pub total_compilations: u64,
    pub successful_compilations: u64,
    pub average_compilation_time: Duration,
    pub cache_hit_rate: f64,
}

#[derive(Debug)]
pub struct PerformancePredictor {
    pub prediction_models: Vec<Box<dyn PredictionModel>>,
}

pub trait PredictionModel: Send + Sync + std::fmt::Debug {
    fn predict_execution_time(&self, operation: &dyn GateOp) -> Duration;
    fn predict_memory_usage(&self, operation: &dyn GateOp) -> usize;
    fn predict_fidelity(&self, operation: &dyn GateOp) -> f64;
}

impl PerformancePredictor {
    pub fn new() -> Self {
        Self {
            prediction_models: Vec::new(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct OperationHistory {
    pub execution_count: u64,
    pub average_execution_time: Duration,
    pub average_fidelity: f64,
}

#[derive(Debug)]
pub struct OperationProfile {
    pub execution_count: u64,
    pub average_execution_time: Duration,
    pub average_fidelity: f64,
    pub memory_usage_history: Vec<usize>,
}

#[derive(Debug)]
pub struct MemoryPool {
    pub pool_id: Uuid,
    pub size: usize,
    pub usage: usize,
}

#[derive(Debug, Default)]
pub struct ParallelSection {
    pub start_index: usize,
    pub end_index: usize,
    pub parallelism_degree: usize,
}

#[derive(Debug)]
pub struct OptimizationOpportunity {
    pub opportunity_type: String,
    pub expected_benefit: f64,
    pub operation_indices: Vec<usize>,
}

#[derive(Debug, Default)]
pub struct ParallelChunk {
    pub chunk_id: usize,
    pub data: Vec<u8>,
}

#[derive(Debug)]
pub struct SparseOperationResult {
    pub amplitudes: Array1<Complex64>,
    pub memory_saved: usize,
}

#[derive(Debug)]
pub struct ApproximateResult {
    pub amplitudes: Array1<Complex64>,
    pub fidelity: f64,
}

#[derive(Debug, Clone)]
pub struct SuperpositionMetrics {
    pub entropy: f64,
    pub max_amplitude: f64,
    pub coherence_measure: f64,
}

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

    #[tokio::test]
    async fn test_quantum_aware_interpreter_creation() {
        let interpreter = QuantumAwareInterpreter::new();
        assert_eq!(
            interpreter
                .quantum_state_tracker
                .active_states
                .read()
                .unwrap()
                .len(),
            0
        );
    }

    #[tokio::test]
    async fn test_state_tracker_creation() {
        let tracker = QuantumStateTracker::new();
        assert_eq!(tracker.active_states.read().unwrap().len(), 0);
    }

    #[test]
    fn test_jit_compiler_creation() {
        let compiler = QuantumJITCompiler::new();
        assert_eq!(compiler.compilation_cache.read().unwrap().len(), 0);
    }

    #[test]
    fn test_optimization_engine_creation() {
        let engine = RuntimeOptimizationEngine::new();
        assert_eq!(engine.optimization_strategies.len(), 0);
    }

    #[test]
    fn test_profiler_creation() {
        let profiler = QuantumProfiler::new();
        assert_eq!(profiler.operation_profiles.read().unwrap().len(), 0);
    }
}