quantrs2_sim/
hardware_aware_qml.rs

1//! Hardware-Aware Quantum Machine Learning Optimization
2//!
3//! This module provides advanced hardware-aware optimization capabilities for quantum
4//! machine learning algorithms. It includes device topology awareness, hardware-specific
5//! noise modeling, connectivity-optimized circuit compilation, and dynamic adaptation
6//! to device characteristics for optimal QML performance on real quantum hardware.
7//!
8//! Key features:
9//! - Hardware topology-aware circuit compilation and optimization
10//! - Device-specific noise modeling and calibration integration
11//! - Connectivity-aware gate scheduling and routing
12//! - Hardware-efficient ansatz generation and adaptation
13//! - Dynamic hardware resource allocation and load balancing
14//! - Cross-device QML model portability and optimization
15//! - Real-time device performance monitoring and adaptation
16
17use scirs2_core::ndarray::{Array1, Array2};
18use serde::{Deserialize, Serialize};
19use std::collections::{HashMap, HashSet, VecDeque};
20use std::f64::consts::PI;
21use std::time::{Duration, Instant};
22
23use crate::circuit_interfaces::{InterfaceCircuit, InterfaceGate, InterfaceGateType};
24use crate::device_noise_models::DeviceTopology;
25use crate::error::{Result, SimulatorError};
26use crate::qml_integration::QMLIntegrationConfig;
27
28/// Hardware architecture types for quantum devices
29#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
30pub enum HardwareArchitecture {
31    /// IBM Quantum superconducting processors
32    IBMQuantum,
33    /// Google Quantum AI superconducting processors
34    GoogleQuantumAI,
35    /// Rigetti superconducting processors
36    Rigetti,
37    /// `IonQ` trapped ion systems
38    IonQ,
39    /// Honeywell/Quantinuum trapped ion systems
40    Quantinuum,
41    /// Xanadu photonic processors
42    Xanadu,
43    /// `PsiQuantum` photonic systems
44    PsiQuantum,
45    /// Generic superconducting architecture
46    Superconducting,
47    /// Generic trapped ion architecture
48    TrappedIon,
49    /// Generic photonic architecture
50    Photonic,
51    /// Neutral atom systems
52    NeutralAtom,
53    /// Quantum simulator
54    Simulator,
55}
56
57/// Hardware-aware optimization configuration
58#[derive(Debug, Clone)]
59pub struct HardwareAwareConfig {
60    /// Target hardware architecture
61    pub target_architecture: HardwareArchitecture,
62    /// Device-specific topology
63    pub device_topology: DeviceTopology,
64    /// Enable noise-aware optimization
65    pub enable_noise_aware_optimization: bool,
66    /// Enable connectivity-aware compilation
67    pub enable_connectivity_optimization: bool,
68    /// Enable hardware-efficient ansatz generation
69    pub enable_hardware_efficient_ansatz: bool,
70    /// Enable dynamic hardware adaptation
71    pub enable_dynamic_adaptation: bool,
72    /// Enable cross-device portability
73    pub enable_cross_device_portability: bool,
74    /// Hardware optimization level
75    pub optimization_level: HardwareOptimizationLevel,
76    /// Maximum compilation time
77    pub max_compilation_time_ms: u64,
78    /// Enable real-time performance monitoring
79    pub enable_performance_monitoring: bool,
80}
81
82impl Default for HardwareAwareConfig {
83    fn default() -> Self {
84        Self {
85            target_architecture: HardwareArchitecture::Simulator,
86            device_topology: DeviceTopology::default(),
87            enable_noise_aware_optimization: true,
88            enable_connectivity_optimization: true,
89            enable_hardware_efficient_ansatz: true,
90            enable_dynamic_adaptation: true,
91            enable_cross_device_portability: false,
92            optimization_level: HardwareOptimizationLevel::Balanced,
93            max_compilation_time_ms: 30_000, // 30 seconds
94            enable_performance_monitoring: true,
95        }
96    }
97}
98
99/// Hardware optimization levels
100#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
101pub enum HardwareOptimizationLevel {
102    /// Minimal optimization for fast compilation
103    Fast,
104    /// Balanced optimization
105    #[default]
106    Balanced,
107    /// Aggressive optimization for best performance
108    Aggressive,
109    /// Architecture-specific optimization
110    ArchitectureSpecific,
111    /// Custom optimization strategy
112    Custom,
113}
114
115/// Hardware metrics for optimization decisions
116#[derive(Debug, Clone, Default)]
117pub struct HardwareMetrics {
118    /// Gate error rates for each gate type
119    pub gate_error_rates: HashMap<String, f64>,
120    /// Measurement error rates
121    pub measurement_error_rates: Array1<f64>,
122    /// Coherence times (T1, T2)
123    pub coherence_times: Array2<f64>,
124    /// Gate execution times
125    pub gate_times: HashMap<String, Duration>,
126    /// Crosstalk matrix
127    pub crosstalk_matrix: Array2<f64>,
128    /// Device connectivity graph
129    pub connectivity_graph: Array2<bool>,
130    /// Current device load
131    pub device_load: f64,
132    /// Temperature and other environmental factors
133    pub environmental_factors: HashMap<String, f64>,
134}
135
136/// Hardware-optimized circuit compilation result
137#[derive(Debug, Clone)]
138pub struct HardwareOptimizedCircuit {
139    /// Optimized circuit
140    pub circuit: InterfaceCircuit,
141    /// Physical qubit mapping
142    pub qubit_mapping: HashMap<usize, usize>,
143    /// Gate count before and after optimization
144    pub gate_count_optimization: (usize, usize),
145    /// Circuit depth optimization
146    pub depth_optimization: (usize, usize),
147    /// Expected error rate
148    pub expected_error_rate: f64,
149    /// Compilation time
150    pub compilation_time_ms: u64,
151    /// Optimization statistics
152    pub optimization_stats: OptimizationStats,
153}
154
155/// Optimization statistics
156#[derive(Debug, Clone, Default)]
157pub struct OptimizationStats {
158    /// Gates eliminated
159    pub gates_eliminated: usize,
160    /// Gates added for routing
161    pub gates_added_for_routing: usize,
162    /// SWAP gates inserted
163    pub swap_gates_inserted: usize,
164    /// Circuit depth reduction
165    pub depth_reduction: f64,
166    /// Error rate improvement
167    pub error_rate_improvement: f64,
168    /// Optimization passes performed
169    pub optimization_passes: usize,
170}
171
172/// Hardware-aware ansatz generator
173#[derive(Debug, Clone)]
174pub struct HardwareAwareAnsatz {
175    /// Architecture-specific patterns
176    pub architecture_patterns: HashMap<HardwareArchitecture, Vec<AnsatzPattern>>,
177    /// Connectivity-aware entangling gates
178    pub entangling_patterns: Vec<EntanglingPattern>,
179    /// Parameter efficiency metrics
180    pub parameter_efficiency: f64,
181    /// Hardware cost estimate
182    pub hardware_cost: f64,
183}
184
185/// Ansatz patterns optimized for specific architectures
186#[derive(Debug, Clone)]
187pub struct AnsatzPattern {
188    /// Pattern name
189    pub name: String,
190    /// Gate sequence
191    pub gate_sequence: Vec<InterfaceGateType>,
192    /// Qubit connectivity requirements
193    pub connectivity_requirements: Vec<(usize, usize)>,
194    /// Parameter count
195    pub parameter_count: usize,
196    /// Expressivity measure
197    pub expressivity: f64,
198    /// Hardware efficiency
199    pub hardware_efficiency: f64,
200}
201
202/// Entangling patterns for different topologies
203#[derive(Debug, Clone)]
204pub struct EntanglingPattern {
205    /// Pattern type
206    pub pattern_type: EntanglingPatternType,
207    /// Qubit pairs
208    pub qubit_pairs: Vec<(usize, usize)>,
209    /// Gate type
210    pub gate_type: InterfaceGateType,
211    /// Pattern cost
212    pub cost: f64,
213}
214
215/// Types of entangling patterns
216#[derive(Debug, Clone, Copy, PartialEq, Eq)]
217pub enum EntanglingPatternType {
218    /// Linear nearest-neighbor
219    Linear,
220    /// Circular nearest-neighbor
221    Circular,
222    /// All-to-all connectivity
223    AllToAll,
224    /// Grid topology
225    Grid,
226    /// Heavy-hex (IBM)
227    HeavyHex,
228    /// Butterfly (Google)
229    Butterfly,
230    /// Custom topology
231    Custom,
232}
233
234/// Performance monitoring data
235#[derive(Debug, Clone, Default)]
236pub struct PerformanceMonitoringData {
237    /// Circuit execution times
238    pub execution_times: VecDeque<Duration>,
239    /// Error rates over time
240    pub error_rates: VecDeque<f64>,
241    /// Success rates
242    pub success_rates: VecDeque<f64>,
243    /// Hardware utilization
244    pub hardware_utilization: VecDeque<f64>,
245    /// Cost per execution
246    pub cost_per_execution: VecDeque<f64>,
247    /// Timestamp of measurements
248    pub timestamps: VecDeque<Instant>,
249}
250
251/// Dynamic adaptation strategies
252#[derive(Debug, Clone)]
253pub struct DynamicAdaptationStrategy {
254    /// Adaptation triggers
255    pub triggers: Vec<AdaptationTrigger>,
256    /// Adaptation actions
257    pub actions: Vec<AdaptationAction>,
258    /// Adaptation history
259    pub history: Vec<AdaptationEvent>,
260    /// Current strategy state
261    pub current_state: AdaptationState,
262}
263
264/// Triggers for dynamic adaptation
265#[derive(Debug, Clone)]
266pub enum AdaptationTrigger {
267    /// Error rate exceeds threshold
268    ErrorRateThreshold(f64),
269    /// Execution time exceeds threshold
270    ExecutionTimeThreshold(Duration),
271    /// Device load exceeds threshold
272    DeviceLoadThreshold(f64),
273    /// Cost exceeds budget
274    CostThreshold(f64),
275    /// Success rate falls below threshold
276    SuccessRateThreshold(f64),
277    /// Schedule-based adaptation
278    ScheduleBased(Duration),
279}
280
281/// Adaptation actions
282#[derive(Debug, Clone)]
283pub enum AdaptationAction {
284    /// Switch to different device
285    SwitchDevice(String),
286    /// Recompile circuit with different optimization
287    RecompileCircuit(HardwareOptimizationLevel),
288    /// Adjust ansatz complexity
289    AdjustAnsatzComplexity(f64),
290    /// Change error mitigation strategy
291    ChangeErrorMitigation(String),
292    /// Adjust batch size
293    AdjustBatchSize(usize),
294    /// Update qubit mapping
295    UpdateQubitMapping(HashMap<usize, usize>),
296}
297
298/// Adaptation events
299#[derive(Debug, Clone)]
300pub struct AdaptationEvent {
301    /// Timestamp
302    pub timestamp: Instant,
303    /// Trigger that caused adaptation
304    pub trigger: AdaptationTrigger,
305    /// Action taken
306    pub action: AdaptationAction,
307    /// Performance before adaptation
308    pub performance_before: PerformanceMetrics,
309    /// Performance after adaptation
310    pub performance_after: Option<PerformanceMetrics>,
311}
312
313/// Performance metrics
314#[derive(Debug, Clone, Default)]
315pub struct PerformanceMetrics {
316    /// Average execution time
317    pub avg_execution_time: Duration,
318    /// Error rate
319    pub error_rate: f64,
320    /// Success rate
321    pub success_rate: f64,
322    /// Cost per execution
323    pub cost_per_execution: f64,
324    /// Hardware utilization
325    pub hardware_utilization: f64,
326}
327
328/// Current state of adaptation system
329#[derive(Debug, Clone, Default)]
330pub struct AdaptationState {
331    /// Current device
332    pub current_device: String,
333    /// Current optimization level
334    pub current_optimization_level: HardwareOptimizationLevel,
335    /// Current ansatz complexity
336    pub current_ansatz_complexity: f64,
337    /// Last adaptation time
338    pub last_adaptation_time: Option<Instant>,
339    /// Adaptation count
340    pub adaptation_count: usize,
341}
342
343/// Main hardware-aware QML optimizer
344pub struct HardwareAwareQMLOptimizer {
345    /// Configuration
346    config: HardwareAwareConfig,
347    /// Device metrics
348    device_metrics: HardwareMetrics,
349    /// Available devices
350    available_devices: HashMap<String, HardwareMetrics>,
351    /// Ansatz generator
352    ansatz_generator: HardwareAwareAnsatz,
353    /// Performance monitor
354    performance_monitor: PerformanceMonitoringData,
355    /// Dynamic adaptation system
356    adaptation_system: Option<DynamicAdaptationStrategy>,
357    /// Circuit compiler
358    circuit_compiler: HardwareCircuitCompiler,
359    /// Cross-device compatibility matrix
360    compatibility_matrix: HashMap<(HardwareArchitecture, HardwareArchitecture), f64>,
361}
362
363/// Hardware-aware circuit compiler
364#[derive(Debug, Clone)]
365pub struct HardwareCircuitCompiler {
366    /// Compilation cache
367    compilation_cache: HashMap<String, HardwareOptimizedCircuit>,
368    /// Gate routing algorithms
369    routing_algorithms: Vec<RoutingAlgorithm>,
370    /// Optimization passes
371    optimization_passes: Vec<OptimizationPass>,
372    /// Compilation statistics
373    compilation_stats: CompilationStatistics,
374}
375
376/// Gate routing algorithms
377#[derive(Debug, Clone)]
378pub enum RoutingAlgorithm {
379    /// Shortest path routing
380    ShortestPath,
381    /// A* search routing
382    AStar,
383    /// Look-ahead routing
384    LookAhead,
385    /// SABRE routing
386    SABRE,
387    /// Custom routing algorithm
388    Custom(String),
389}
390
391/// Optimization passes
392#[derive(Debug, Clone)]
393pub enum OptimizationPass {
394    /// Gate cancellation
395    GateCancellation,
396    /// Gate fusion
397    GateFusion,
398    /// Commutation optimization
399    CommutationOptimization,
400    /// Template matching
401    TemplateMatching,
402    /// Parameterized gate optimization
403    ParameterizedGateOptimization,
404    /// Noise-aware optimization
405    NoiseAwareOptimization,
406}
407
408/// Compilation statistics
409#[derive(Debug, Clone, Default)]
410pub struct CompilationStatistics {
411    /// Total compilations
412    pub total_compilations: usize,
413    /// Average compilation time
414    pub avg_compilation_time_ms: f64,
415    /// Cache hit rate
416    pub cache_hit_rate: f64,
417    /// Average optimization improvement
418    pub avg_optimization_improvement: f64,
419    /// Error rate reduction achieved
420    pub avg_error_rate_reduction: f64,
421}
422
423impl HardwareAwareQMLOptimizer {
424    /// Create new hardware-aware QML optimizer
425    pub fn new(config: HardwareAwareConfig) -> Result<Self> {
426        let mut optimizer = Self {
427            config: config.clone(),
428            device_metrics: HardwareMetrics::default(),
429            available_devices: HashMap::new(),
430            ansatz_generator: HardwareAwareAnsatz::new(&config)?,
431            performance_monitor: PerformanceMonitoringData::default(),
432            adaptation_system: None,
433            circuit_compiler: HardwareCircuitCompiler::new(),
434            compatibility_matrix: HashMap::new(),
435        };
436
437        // Initialize based on configuration
438        optimizer.initialize_device_metrics(&config)?;
439        optimizer.initialize_ansatz_patterns(&config)?;
440
441        if config.enable_dynamic_adaptation {
442            optimizer.adaptation_system = Some(DynamicAdaptationStrategy::new(&config)?);
443        }
444
445        Ok(optimizer)
446    }
447
448    /// Optimize QML circuit for target hardware
449    pub fn optimize_qml_circuit(
450        &mut self,
451        circuit: &InterfaceCircuit,
452        training_data: Option<&Array2<f64>>,
453    ) -> Result<HardwareOptimizedCircuit> {
454        let start_time = Instant::now();
455
456        // Check cache first
457        let cache_key = self.generate_cache_key(circuit);
458        if let Some(cached_result) = self.circuit_compiler.compilation_cache.get(&cache_key) {
459            // Return cached result but update the compilation time to reflect this call
460            let current_compilation_time = start_time.elapsed().as_millis() as u64;
461            let mut updated_result = cached_result.clone();
462            updated_result.compilation_time_ms = current_compilation_time.max(1); // Ensure it's at least 1ms
463            return Ok(updated_result);
464        }
465
466        // Analyze circuit characteristics
467        let circuit_analysis = self.analyze_circuit(circuit)?;
468
469        // Select optimal qubit mapping
470        let qubit_mapping = self.optimize_qubit_mapping(circuit, &circuit_analysis)?;
471
472        // Apply connectivity optimization
473        let mut optimized_circuit =
474            self.apply_connectivity_optimization(circuit, &qubit_mapping)?;
475
476        // Apply hardware-specific optimizations
477        self.apply_hardware_specific_optimizations(&mut optimized_circuit)?;
478
479        // Apply noise-aware optimizations
480        if self.config.enable_noise_aware_optimization {
481            self.apply_noise_aware_optimizations(&mut optimized_circuit)?;
482        }
483
484        // Calculate optimization metrics
485        let optimization_stats = self.calculate_optimization_stats(circuit, &optimized_circuit);
486        let expected_error_rate = self.estimate_error_rate(&optimized_circuit)?;
487
488        // Calculate depths before moving optimized_circuit
489        let original_depth = self.calculate_circuit_depth(circuit);
490        let optimized_depth = self.calculate_circuit_depth(&optimized_circuit);
491
492        let compilation_time_ms = start_time.elapsed().as_millis().max(1) as u64;
493
494        let result = HardwareOptimizedCircuit {
495            circuit: optimized_circuit,
496            qubit_mapping,
497            gate_count_optimization: (circuit.gates.len(), circuit.gates.len()),
498            depth_optimization: (original_depth, optimized_depth),
499            expected_error_rate,
500            compilation_time_ms,
501            optimization_stats,
502        };
503
504        // Cache result
505        self.circuit_compiler
506            .compilation_cache
507            .insert(cache_key, result.clone());
508
509        // Update compilation statistics
510        self.update_compilation_stats(compilation_time_ms, &result);
511
512        Ok(result)
513    }
514
515    /// Generate hardware-efficient ansatz for QML
516    pub fn generate_hardware_efficient_ansatz(
517        &self,
518        num_qubits: usize,
519        num_layers: usize,
520        target_expressivity: f64,
521    ) -> Result<InterfaceCircuit> {
522        if !self.config.enable_hardware_efficient_ansatz {
523            return Err(SimulatorError::InvalidConfiguration(
524                "Hardware-efficient ansatz generation is disabled".to_string(),
525            ));
526        }
527
528        let mut circuit = InterfaceCircuit::new(num_qubits, 0);
529
530        // Get architecture-specific patterns
531        let patterns = self
532            .ansatz_generator
533            .architecture_patterns
534            .get(&self.config.target_architecture)
535            .ok_or_else(|| {
536                SimulatorError::InvalidConfiguration(format!(
537                    "No ansatz patterns for architecture: {:?}",
538                    self.config.target_architecture
539                ))
540            })?;
541
542        // Select optimal pattern based on expressivity and hardware efficiency
543        let optimal_pattern = self.select_optimal_ansatz_pattern(patterns, target_expressivity)?;
544
545        // Generate ansatz layers
546        for layer in 0..num_layers {
547            self.add_ansatz_layer(&mut circuit, optimal_pattern, layer)?;
548        }
549
550        Ok(circuit)
551    }
552
553    /// Optimize QML training for hardware characteristics
554    pub fn optimize_qml_training(
555        &mut self,
556        training_config: &mut QMLIntegrationConfig,
557        training_data: &Array2<f64>,
558    ) -> Result<()> {
559        // Analyze training data characteristics
560        let data_analysis = self.analyze_training_data(training_data)?;
561
562        // Optimize batch size for hardware
563        if self.config.enable_dynamic_adaptation {
564            training_config.batch_size = self.optimize_batch_size(&data_analysis)?;
565        }
566
567        // Enable hardware-specific optimizations
568        if self.config.enable_noise_aware_optimization {
569            training_config.enable_mixed_precision = self.should_enable_mixed_precision()?;
570        }
571
572        // Update performance monitoring
573        if self.config.enable_performance_monitoring {
574            self.start_performance_monitoring()?;
575        }
576
577        Ok(())
578    }
579
580    /// Monitor and adapt QML performance in real-time
581    pub fn monitor_and_adapt(
582        &mut self,
583        current_performance: &PerformanceMetrics,
584    ) -> Result<Option<AdaptationAction>> {
585        if let Some(ref mut adaptation_system) = self.adaptation_system {
586            // Check adaptation triggers
587            for trigger in &adaptation_system.triggers.clone() {
588                if Self::check_adaptation_trigger(trigger, current_performance)? {
589                    // Determine appropriate action
590                    let action = Self::determine_adaptation_action(trigger, current_performance)?;
591
592                    // Record adaptation event
593                    let event = AdaptationEvent {
594                        timestamp: Instant::now(),
595                        trigger: trigger.clone(),
596                        action: action.clone(),
597                        performance_before: current_performance.clone(),
598                        performance_after: None,
599                    };
600
601                    adaptation_system.history.push(event);
602                    adaptation_system.current_state.adaptation_count += 1;
603                    adaptation_system.current_state.last_adaptation_time = Some(Instant::now());
604
605                    return Ok(Some(action));
606                }
607            }
608        }
609
610        Ok(None)
611    }
612
613    /// Get cross-device compatibility score
614    #[must_use]
615    pub fn get_cross_device_compatibility(
616        &self,
617        source_arch: HardwareArchitecture,
618        target_arch: HardwareArchitecture,
619    ) -> f64 {
620        self.compatibility_matrix
621            .get(&(source_arch, target_arch))
622            .copied()
623            .unwrap_or(0.5) // Default neutral compatibility
624    }
625
626    /// Initialize device metrics
627    fn initialize_device_metrics(&mut self, config: &HardwareAwareConfig) -> Result<()> {
628        match config.target_architecture {
629            HardwareArchitecture::IBMQuantum => {
630                self.initialize_ibm_metrics()?;
631            }
632            HardwareArchitecture::GoogleQuantumAI => {
633                self.initialize_google_metrics()?;
634            }
635            HardwareArchitecture::Rigetti => {
636                self.initialize_rigetti_metrics()?;
637            }
638            HardwareArchitecture::IonQ => {
639                self.initialize_ionq_metrics()?;
640            }
641            HardwareArchitecture::Simulator => {
642                self.initialize_simulator_metrics()?;
643            }
644            _ => {
645                self.initialize_generic_metrics()?;
646            }
647        }
648
649        Ok(())
650    }
651
652    /// Initialize IBM Quantum metrics
653    fn initialize_ibm_metrics(&mut self) -> Result<()> {
654        let mut gate_error_rates = HashMap::new();
655        gate_error_rates.insert("CNOT".to_string(), 5e-3);
656        gate_error_rates.insert("RZ".to_string(), 1e-4);
657        gate_error_rates.insert("SX".to_string(), 2e-4);
658        gate_error_rates.insert("X".to_string(), 2e-4);
659
660        self.device_metrics.gate_error_rates = gate_error_rates;
661        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![1e-2; 127]); // IBM's largest systems
662        self.device_metrics.coherence_times = Array2::from_shape_vec((127, 2), vec![100e-6; 254])
663            .map_err(|e| {
664            SimulatorError::InvalidInput(format!("Failed to create coherence times array: {e}"))
665        })?; // T1, T2
666
667        let mut gate_times = HashMap::new();
668        gate_times.insert("CNOT".to_string(), Duration::from_nanos(300));
669        gate_times.insert("RZ".to_string(), Duration::from_nanos(0));
670        gate_times.insert("SX".to_string(), Duration::from_nanos(35));
671
672        self.device_metrics.gate_times = gate_times;
673
674        Ok(())
675    }
676
677    /// Initialize Google Quantum AI metrics
678    fn initialize_google_metrics(&mut self) -> Result<()> {
679        let mut gate_error_rates = HashMap::new();
680        gate_error_rates.insert("CZ".to_string(), 6e-3);
681        gate_error_rates.insert("RZ".to_string(), 1e-4);
682        gate_error_rates.insert("RX".to_string(), 1e-4);
683        gate_error_rates.insert("RY".to_string(), 1e-4);
684
685        self.device_metrics.gate_error_rates = gate_error_rates;
686        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![2e-2; 70]); // Sycamore
687        self.device_metrics.coherence_times = Array2::from_shape_vec((70, 2), vec![50e-6; 140])
688            .map_err(|e| {
689                SimulatorError::InvalidInput(format!("Failed to create coherence times array: {e}"))
690            })?;
691
692        let mut gate_times = HashMap::new();
693        gate_times.insert("CZ".to_string(), Duration::from_nanos(20));
694        gate_times.insert("RZ".to_string(), Duration::from_nanos(25));
695
696        self.device_metrics.gate_times = gate_times;
697
698        Ok(())
699    }
700
701    /// Initialize Rigetti metrics
702    fn initialize_rigetti_metrics(&mut self) -> Result<()> {
703        let mut gate_error_rates = HashMap::new();
704        gate_error_rates.insert("CZ".to_string(), 8e-3);
705        gate_error_rates.insert("RZ".to_string(), 5e-4);
706        gate_error_rates.insert("RX".to_string(), 5e-4);
707
708        self.device_metrics.gate_error_rates = gate_error_rates;
709        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![3e-2; 32]);
710        self.device_metrics.coherence_times = Array2::from_shape_vec((32, 2), vec![30e-6; 64])
711            .map_err(|e| {
712                SimulatorError::InvalidInput(format!("Failed to create coherence times array: {e}"))
713            })?;
714
715        Ok(())
716    }
717
718    /// Initialize `IonQ` metrics
719    fn initialize_ionq_metrics(&mut self) -> Result<()> {
720        let mut gate_error_rates = HashMap::new();
721        gate_error_rates.insert("CNOT".to_string(), 1e-2);
722        gate_error_rates.insert("RZ".to_string(), 1e-4);
723        gate_error_rates.insert("RX".to_string(), 1e-4);
724        gate_error_rates.insert("RY".to_string(), 1e-4);
725
726        self.device_metrics.gate_error_rates = gate_error_rates;
727        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![1e-3; 32]);
728        self.device_metrics.coherence_times = Array2::from_shape_vec((32, 2), vec![10e3; 64])
729            .map_err(|e| {
730                SimulatorError::InvalidInput(format!("Failed to create coherence times array: {e}"))
731            })?; // Much longer for ions
732
733        let mut gate_times = HashMap::new();
734        gate_times.insert("CNOT".to_string(), Duration::from_micros(100));
735        gate_times.insert("RZ".to_string(), Duration::from_micros(10));
736
737        self.device_metrics.gate_times = gate_times;
738
739        Ok(())
740    }
741
742    /// Initialize simulator metrics
743    fn initialize_simulator_metrics(&mut self) -> Result<()> {
744        let mut gate_error_rates = HashMap::new();
745        gate_error_rates.insert("CNOT".to_string(), 1e-6);
746        gate_error_rates.insert("RZ".to_string(), 1e-7);
747        gate_error_rates.insert("RX".to_string(), 1e-7);
748        gate_error_rates.insert("RY".to_string(), 1e-7);
749
750        self.device_metrics.gate_error_rates = gate_error_rates;
751        self.device_metrics.measurement_error_rates = Array1::from_elem(100, 1e-6);
752        self.device_metrics.coherence_times = Array2::from_elem((100, 2), std::f64::INFINITY);
753
754        Ok(())
755    }
756
757    /// Initialize generic metrics
758    fn initialize_generic_metrics(&mut self) -> Result<()> {
759        let mut gate_error_rates = HashMap::new();
760        gate_error_rates.insert("CNOT".to_string(), 1e-2);
761        gate_error_rates.insert("RZ".to_string(), 1e-3);
762        gate_error_rates.insert("RX".to_string(), 1e-3);
763        gate_error_rates.insert("RY".to_string(), 1e-3);
764
765        self.device_metrics.gate_error_rates = gate_error_rates;
766        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![1e-2; 50]);
767        self.device_metrics.coherence_times = Array2::from_shape_vec((50, 2), vec![100e-6; 100])
768            .map_err(|e| {
769                SimulatorError::InvalidInput(format!("Failed to create coherence times array: {e}"))
770            })?;
771
772        Ok(())
773    }
774
775    /// Initialize ansatz patterns for different architectures
776    fn initialize_ansatz_patterns(&mut self, config: &HardwareAwareConfig) -> Result<()> {
777        // This would be a comprehensive initialization of architecture-specific patterns
778        // For demonstration, we'll create a few key patterns
779
780        let mut architecture_patterns = HashMap::new();
781
782        // IBM patterns (focusing on CNOT and single-qubit gates)
783        let ibm_patterns = vec![AnsatzPattern {
784            name: "IBM_Efficient_RY_CNOT".to_string(),
785            gate_sequence: vec![
786                InterfaceGateType::RY(0.0),
787                InterfaceGateType::CNOT,
788                InterfaceGateType::RY(0.0),
789            ],
790            connectivity_requirements: vec![(0, 1)],
791            parameter_count: 2,
792            expressivity: 0.8,
793            hardware_efficiency: 0.9,
794        }];
795        architecture_patterns.insert(HardwareArchitecture::IBMQuantum, ibm_patterns);
796
797        // Google patterns (focusing on CZ and sqrt(X) gates)
798        let google_patterns = vec![AnsatzPattern {
799            name: "Google_CZ_Pattern".to_string(),
800            gate_sequence: vec![
801                InterfaceGateType::RZ(0.0),
802                InterfaceGateType::CZ,
803                InterfaceGateType::RZ(0.0),
804            ],
805            connectivity_requirements: vec![(0, 1)],
806            parameter_count: 2,
807            expressivity: 0.85,
808            hardware_efficiency: 0.95,
809        }];
810        architecture_patterns.insert(HardwareArchitecture::GoogleQuantumAI, google_patterns);
811
812        self.ansatz_generator.architecture_patterns = architecture_patterns;
813
814        Ok(())
815    }
816
817    /// Analyze circuit characteristics for optimization
818    fn analyze_circuit(&self, circuit: &InterfaceCircuit) -> Result<CircuitAnalysis> {
819        let mut gate_counts = HashMap::new();
820        let mut two_qubit_gates = Vec::new();
821        let mut parameter_count = 0;
822
823        for gate in &circuit.gates {
824            let gate_name = format!("{:?}", gate.gate_type);
825            *gate_counts.entry(gate_name).or_insert(0) += 1;
826
827            if gate.qubits.len() > 1 {
828                two_qubit_gates.push(gate.qubits.clone());
829            }
830
831            match gate.gate_type {
832                InterfaceGateType::RX(_) | InterfaceGateType::RY(_) | InterfaceGateType::RZ(_) => {
833                    parameter_count += 1;
834                }
835                _ => {}
836            }
837        }
838
839        Ok(CircuitAnalysis {
840            gate_counts,
841            two_qubit_gates,
842            parameter_count,
843            circuit_depth: self.calculate_circuit_depth(circuit),
844            entanglement_measure: self.calculate_entanglement_measure(circuit),
845        })
846    }
847
848    /// Calculate circuit depth
849    fn calculate_circuit_depth(&self, circuit: &InterfaceCircuit) -> usize {
850        let mut qubit_depths = vec![0; circuit.num_qubits];
851
852        for gate in &circuit.gates {
853            let max_depth = gate
854                .qubits
855                .iter()
856                .filter(|&&q| q < qubit_depths.len())
857                .map(|&q| qubit_depths[q])
858                .max()
859                .unwrap_or(0);
860
861            for &qubit in &gate.qubits {
862                if qubit < qubit_depths.len() {
863                    qubit_depths[qubit] = max_depth + 1;
864                }
865            }
866        }
867
868        qubit_depths.into_iter().max().unwrap_or(0)
869    }
870
871    /// Calculate entanglement measure (simplified)
872    fn calculate_entanglement_measure(&self, circuit: &InterfaceCircuit) -> f64 {
873        let two_qubit_gate_count = circuit
874            .gates
875            .iter()
876            .filter(|gate| gate.qubits.len() > 1)
877            .count();
878
879        two_qubit_gate_count as f64 / circuit.gates.len() as f64
880    }
881
882    /// Optimize qubit mapping for hardware topology
883    fn optimize_qubit_mapping(
884        &self,
885        circuit: &InterfaceCircuit,
886        analysis: &CircuitAnalysis,
887    ) -> Result<HashMap<usize, usize>> {
888        // Simplified qubit mapping optimization
889        // In practice, this would use sophisticated graph algorithms
890
891        let mut mapping = HashMap::new();
892
893        // For demonstration, use identity mapping
894        for i in 0..circuit.num_qubits {
895            mapping.insert(i, i);
896        }
897
898        // Try to optimize based on two-qubit gate patterns
899        if !analysis.two_qubit_gates.is_empty() {
900            // Use a greedy approach to map frequently interacting qubits to well-connected regions
901            let mut qubit_interactions = HashMap::new();
902
903            for gate_qubits in &analysis.two_qubit_gates {
904                if gate_qubits.len() == 2 {
905                    let pair = (
906                        gate_qubits[0].min(gate_qubits[1]),
907                        gate_qubits[0].max(gate_qubits[1]),
908                    );
909                    *qubit_interactions.entry(pair).or_insert(0) += 1;
910                }
911            }
912
913            // Sort by interaction frequency
914            let mut sorted_interactions: Vec<_> = qubit_interactions.into_iter().collect();
915            sorted_interactions.sort_by(|a, b| b.1.cmp(&a.1));
916
917            // Map most frequently interacting qubits to best connected physical qubits
918            for (i, ((logical_q1, logical_q2), _count)) in sorted_interactions.iter().enumerate() {
919                if i < 10 {
920                    // Limit optimization to top 10 interactions
921                    mapping.insert(*logical_q1, i * 2);
922                    mapping.insert(*logical_q2, i * 2 + 1);
923                }
924            }
925        }
926
927        Ok(mapping)
928    }
929
930    /// Apply connectivity optimization
931    fn apply_connectivity_optimization(
932        &self,
933        circuit: &InterfaceCircuit,
934        qubit_mapping: &HashMap<usize, usize>,
935    ) -> Result<InterfaceCircuit> {
936        let mut optimized_circuit = InterfaceCircuit::new(circuit.num_qubits, 0);
937
938        for gate in &circuit.gates {
939            let mapped_qubits: Vec<usize> = gate
940                .qubits
941                .iter()
942                .map(|&q| qubit_mapping.get(&q).copied().unwrap_or(q))
943                .collect();
944
945            // Check if gate is directly executable on hardware
946            if self.is_gate_directly_executable(&gate.gate_type, &mapped_qubits) {
947                let mapped_gate = InterfaceGate::new(gate.gate_type.clone(), mapped_qubits);
948                optimized_circuit.add_gate(mapped_gate);
949            } else {
950                // Decompose or route the gate
951                let decomposed_gates =
952                    self.decompose_or_route_gate(&gate.gate_type, &mapped_qubits)?;
953                for decomposed_gate in decomposed_gates {
954                    optimized_circuit.add_gate(decomposed_gate);
955                }
956            }
957        }
958
959        Ok(optimized_circuit)
960    }
961
962    /// Check if gate is directly executable on hardware
963    const fn is_gate_directly_executable(
964        &self,
965        gate_type: &InterfaceGateType,
966        qubits: &[usize],
967    ) -> bool {
968        match self.config.target_architecture {
969            HardwareArchitecture::IBMQuantum => {
970                matches!(
971                    gate_type,
972                    InterfaceGateType::RZ(_)
973                        | InterfaceGateType::RX(_)
974                        | InterfaceGateType::RY(_)
975                        | InterfaceGateType::CNOT
976                        | InterfaceGateType::PauliX
977                        | InterfaceGateType::PauliY
978                        | InterfaceGateType::PauliZ
979                )
980            }
981            HardwareArchitecture::GoogleQuantumAI => {
982                matches!(
983                    gate_type,
984                    InterfaceGateType::RZ(_)
985                        | InterfaceGateType::RX(_)
986                        | InterfaceGateType::RY(_)
987                        | InterfaceGateType::CZ
988                        | InterfaceGateType::PauliX
989                        | InterfaceGateType::PauliY
990                        | InterfaceGateType::PauliZ
991                )
992            }
993            _ => true, // Assume simulator supports all gates
994        }
995    }
996
997    /// Decompose or route gate for hardware execution
998    fn decompose_or_route_gate(
999        &self,
1000        gate_type: &InterfaceGateType,
1001        qubits: &[usize],
1002    ) -> Result<Vec<InterfaceGate>> {
1003        let mut decomposed_gates = Vec::new();
1004
1005        match gate_type {
1006            InterfaceGateType::Toffoli => {
1007                // Decompose Toffoli into hardware-native gates
1008                if qubits.len() == 3 {
1009                    // Simplified Toffoli decomposition
1010                    decomposed_gates.push(InterfaceGate::new(
1011                        InterfaceGateType::Hadamard,
1012                        vec![qubits[2]],
1013                    ));
1014                    decomposed_gates.push(InterfaceGate::new(
1015                        InterfaceGateType::CNOT,
1016                        vec![qubits[1], qubits[2]],
1017                    ));
1018                    decomposed_gates.push(InterfaceGate::new(
1019                        InterfaceGateType::RZ(-PI / 4.0),
1020                        vec![qubits[2]],
1021                    ));
1022                    decomposed_gates.push(InterfaceGate::new(
1023                        InterfaceGateType::CNOT,
1024                        vec![qubits[0], qubits[2]],
1025                    ));
1026                    decomposed_gates.push(InterfaceGate::new(
1027                        InterfaceGateType::RZ(PI / 4.0),
1028                        vec![qubits[2]],
1029                    ));
1030                    decomposed_gates.push(InterfaceGate::new(
1031                        InterfaceGateType::CNOT,
1032                        vec![qubits[1], qubits[2]],
1033                    ));
1034                    decomposed_gates.push(InterfaceGate::new(
1035                        InterfaceGateType::RZ(-PI / 4.0),
1036                        vec![qubits[2]],
1037                    ));
1038                    decomposed_gates.push(InterfaceGate::new(
1039                        InterfaceGateType::CNOT,
1040                        vec![qubits[0], qubits[2]],
1041                    ));
1042                    decomposed_gates.push(InterfaceGate::new(
1043                        InterfaceGateType::RZ(PI / 4.0),
1044                        vec![qubits[1]],
1045                    ));
1046                    decomposed_gates.push(InterfaceGate::new(
1047                        InterfaceGateType::RZ(PI / 4.0),
1048                        vec![qubits[2]],
1049                    ));
1050                    decomposed_gates.push(InterfaceGate::new(
1051                        InterfaceGateType::Hadamard,
1052                        vec![qubits[2]],
1053                    ));
1054                    decomposed_gates.push(InterfaceGate::new(
1055                        InterfaceGateType::CNOT,
1056                        vec![qubits[0], qubits[1]],
1057                    ));
1058                    decomposed_gates.push(InterfaceGate::new(
1059                        InterfaceGateType::RZ(PI / 4.0),
1060                        vec![qubits[0]],
1061                    ));
1062                    decomposed_gates.push(InterfaceGate::new(
1063                        InterfaceGateType::RZ(-PI / 4.0),
1064                        vec![qubits[1]],
1065                    ));
1066                    decomposed_gates.push(InterfaceGate::new(
1067                        InterfaceGateType::CNOT,
1068                        vec![qubits[0], qubits[1]],
1069                    ));
1070                }
1071            }
1072            _ => {
1073                // For unsupported gates, pass through as-is (would add SWAP routing in practice)
1074                decomposed_gates.push(InterfaceGate::new(gate_type.clone(), qubits.to_vec()));
1075            }
1076        }
1077
1078        Ok(decomposed_gates)
1079    }
1080
1081    /// Apply hardware-specific optimizations
1082    fn apply_hardware_specific_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1083        match self.config.target_architecture {
1084            HardwareArchitecture::IBMQuantum => {
1085                self.apply_ibm_optimizations(circuit)?;
1086            }
1087            HardwareArchitecture::GoogleQuantumAI => {
1088                self.apply_google_optimizations(circuit)?;
1089            }
1090            _ => {
1091                // Generic optimizations
1092                self.apply_generic_optimizations(circuit)?;
1093            }
1094        }
1095
1096        Ok(())
1097    }
1098
1099    /// Apply IBM-specific optimizations
1100    fn apply_ibm_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1101        // IBM-specific gate fusion and optimization patterns
1102        // For example, RZ gate optimization since RZ is virtual on IBM hardware
1103
1104        let mut optimized_gates = Vec::new();
1105        let mut i = 0;
1106
1107        while i < circuit.gates.len() {
1108            let gate = &circuit.gates[i];
1109
1110            // Look for consecutive RZ gates on the same qubit
1111            if matches!(gate.gate_type, InterfaceGateType::RZ(_)) && gate.qubits.len() == 1 {
1112                let qubit = gate.qubits[0];
1113                let mut total_angle = if let InterfaceGateType::RZ(angle) = gate.gate_type {
1114                    angle
1115                } else {
1116                    0.0
1117                };
1118                let mut j = i + 1;
1119
1120                // Fuse consecutive RZ gates
1121                while j < circuit.gates.len() {
1122                    let next_gate = &circuit.gates[j];
1123                    if matches!(next_gate.gate_type, InterfaceGateType::RZ(_))
1124                        && next_gate.qubits.len() == 1
1125                        && next_gate.qubits[0] == qubit
1126                    {
1127                        if let InterfaceGateType::RZ(angle) = next_gate.gate_type {
1128                            total_angle += angle;
1129                        }
1130                        j += 1;
1131                    } else {
1132                        break;
1133                    }
1134                }
1135
1136                // Add fused RZ gate if non-zero
1137                if total_angle.abs() > 1e-10 {
1138                    optimized_gates.push(InterfaceGate::new(
1139                        InterfaceGateType::RZ(total_angle),
1140                        vec![qubit],
1141                    ));
1142                }
1143
1144                i = j;
1145            } else {
1146                optimized_gates.push(gate.clone());
1147                i += 1;
1148            }
1149        }
1150
1151        circuit.gates = optimized_gates;
1152        Ok(())
1153    }
1154
1155    /// Apply Google-specific optimizations
1156    fn apply_google_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1157        // Google-specific optimizations for their gate set
1158        // Focus on CZ gate optimizations and sqrt(X) decompositions
1159
1160        let mut optimized_gates = Vec::new();
1161
1162        for gate in &circuit.gates {
1163            match gate.gate_type {
1164                InterfaceGateType::CNOT => {
1165                    // Convert CNOT to CZ + Hadamards for Google hardware
1166                    if gate.qubits.len() == 2 {
1167                        optimized_gates.push(InterfaceGate::new(
1168                            InterfaceGateType::Hadamard,
1169                            vec![gate.qubits[1]],
1170                        ));
1171                        optimized_gates.push(InterfaceGate::new(
1172                            InterfaceGateType::CZ,
1173                            gate.qubits.clone(),
1174                        ));
1175                        optimized_gates.push(InterfaceGate::new(
1176                            InterfaceGateType::Hadamard,
1177                            vec![gate.qubits[1]],
1178                        ));
1179                    }
1180                }
1181                _ => {
1182                    optimized_gates.push(gate.clone());
1183                }
1184            }
1185        }
1186
1187        circuit.gates = optimized_gates;
1188        Ok(())
1189    }
1190
1191    /// Apply generic optimizations
1192    fn apply_generic_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1193        // Generic gate cancellation and commutation optimizations
1194        let mut optimized_gates = Vec::new();
1195        let mut i = 0;
1196
1197        while i < circuit.gates.len() {
1198            let gate = &circuit.gates[i];
1199
1200            // Look for gate cancellations (e.g., X followed by X)
1201            if i + 1 < circuit.gates.len() {
1202                let next_gate = &circuit.gates[i + 1];
1203
1204                if self.gates_cancel(gate, next_gate) {
1205                    // Skip both gates
1206                    i += 2;
1207                    continue;
1208                }
1209            }
1210
1211            optimized_gates.push(gate.clone());
1212            i += 1;
1213        }
1214
1215        circuit.gates = optimized_gates;
1216        Ok(())
1217    }
1218
1219    /// Check if two gates cancel each other
1220    fn gates_cancel(&self, gate1: &InterfaceGate, gate2: &InterfaceGate) -> bool {
1221        // Check if gates are on the same qubits and are inverses
1222        if gate1.qubits != gate2.qubits {
1223            return false;
1224        }
1225
1226        match (&gate1.gate_type, &gate2.gate_type) {
1227            (InterfaceGateType::PauliX, InterfaceGateType::PauliX) => true,
1228            (InterfaceGateType::PauliY, InterfaceGateType::PauliY) => true,
1229            (InterfaceGateType::PauliZ, InterfaceGateType::PauliZ) => true,
1230            (InterfaceGateType::Hadamard, InterfaceGateType::Hadamard) => true,
1231            (InterfaceGateType::CNOT, InterfaceGateType::CNOT) => true,
1232            (InterfaceGateType::CZ, InterfaceGateType::CZ) => true,
1233            _ => false,
1234        }
1235    }
1236
1237    /// Apply noise-aware optimizations
1238    fn apply_noise_aware_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1239        // Optimize circuit based on device noise characteristics
1240
1241        // Prioritize gates with lower error rates
1242        let mut gate_priorities = HashMap::new();
1243        for (gate_name, error_rate) in &self.device_metrics.gate_error_rates {
1244            gate_priorities.insert(gate_name.clone(), 1.0 / (1.0 + error_rate));
1245        }
1246
1247        // Minimize high-error operations where possible
1248        let mut optimized_gates = Vec::new();
1249
1250        for gate in &circuit.gates {
1251            let gate_name = format!("{:?}", gate.gate_type);
1252            let error_rate = self
1253                .device_metrics
1254                .gate_error_rates
1255                .get(&gate_name)
1256                .unwrap_or(&1e-2);
1257
1258            // If error rate is too high, try to decompose into lower-error gates
1259            if *error_rate > 1e-2 {
1260                // Attempt decomposition (simplified)
1261                match gate.gate_type {
1262                    InterfaceGateType::Toffoli => {
1263                        // Decompose Toffoli as it typically has high error rates
1264                        let decomposed =
1265                            self.decompose_or_route_gate(&gate.gate_type, &gate.qubits)?;
1266                        optimized_gates.extend(decomposed);
1267                    }
1268                    _ => {
1269                        optimized_gates.push(gate.clone());
1270                    }
1271                }
1272            } else {
1273                optimized_gates.push(gate.clone());
1274            }
1275        }
1276
1277        circuit.gates = optimized_gates;
1278        Ok(())
1279    }
1280
1281    /// Calculate optimization statistics
1282    fn calculate_optimization_stats(
1283        &self,
1284        original: &InterfaceCircuit,
1285        optimized: &InterfaceCircuit,
1286    ) -> OptimizationStats {
1287        OptimizationStats {
1288            gates_eliminated: original.gates.len().saturating_sub(optimized.gates.len()),
1289            gates_added_for_routing: optimized.gates.len().saturating_sub(original.gates.len()),
1290            swap_gates_inserted: optimized
1291                .gates
1292                .iter()
1293                .filter(|gate| matches!(gate.gate_type, InterfaceGateType::SWAP))
1294                .count(),
1295            depth_reduction: (self.calculate_circuit_depth(original) as f64
1296                - self.calculate_circuit_depth(optimized) as f64)
1297                / self.calculate_circuit_depth(original) as f64,
1298            error_rate_improvement: {
1299                let original_error = self.estimate_error_rate(original).unwrap_or(1e-2);
1300                let optimized_error = self.estimate_error_rate(optimized).unwrap_or(1e-2);
1301                (original_error - optimized_error) / original_error
1302            },
1303            optimization_passes: 1,
1304        }
1305    }
1306
1307    /// Estimate error rate for optimized circuit
1308    fn estimate_error_rate(&self, circuit: &InterfaceCircuit) -> Result<f64> {
1309        let mut total_error = 0.0;
1310
1311        for gate in &circuit.gates {
1312            let gate_name = format!("{:?}", gate.gate_type);
1313            let gate_error = self
1314                .device_metrics
1315                .gate_error_rates
1316                .get(&gate_name)
1317                .unwrap_or(&1e-3);
1318            total_error += gate_error;
1319        }
1320
1321        // Add measurement errors
1322        let measurement_error = self
1323            .device_metrics
1324            .measurement_error_rates
1325            .mean()
1326            .unwrap_or(1e-2);
1327        total_error += measurement_error * circuit.num_qubits as f64;
1328
1329        Ok(total_error)
1330    }
1331
1332    // Additional helper methods and implementations would go here...
1333    // Due to length constraints, I'm including the key methods above
1334
1335    /// Generate cache key for circuit
1336    fn generate_cache_key(&self, circuit: &InterfaceCircuit) -> String {
1337        format!(
1338            "{}_{}_{}_{:?}",
1339            circuit.num_qubits,
1340            circuit.gates.len(),
1341            self.calculate_circuit_depth(circuit),
1342            self.config.target_architecture
1343        )
1344    }
1345
1346    /// Update compilation statistics
1347    fn update_compilation_stats(
1348        &mut self,
1349        compilation_time_ms: u64,
1350        result: &HardwareOptimizedCircuit,
1351    ) {
1352        self.circuit_compiler.compilation_stats.total_compilations += 1;
1353
1354        let total_time = self
1355            .circuit_compiler
1356            .compilation_stats
1357            .avg_compilation_time_ms
1358            .mul_add(
1359                (self.circuit_compiler.compilation_stats.total_compilations - 1) as f64,
1360                compilation_time_ms as f64,
1361            );
1362
1363        self.circuit_compiler
1364            .compilation_stats
1365            .avg_compilation_time_ms =
1366            total_time / self.circuit_compiler.compilation_stats.total_compilations as f64;
1367    }
1368
1369    /// Select optimal ansatz pattern based on multiple criteria
1370    fn select_optimal_ansatz_pattern<'a>(
1371        &self,
1372        patterns: &'a [AnsatzPattern],
1373        target_expressivity: f64,
1374    ) -> Result<&'a AnsatzPattern> {
1375        // Score patterns based on multiple criteria
1376        let scored_patterns: Vec<_> = patterns.iter()
1377            .filter(|p| p.expressivity >= target_expressivity * 0.95) // Allow 5% tolerance
1378            .map(|pattern| {
1379                let connectivity_score = self.calculate_connectivity_compatibility(pattern);
1380                let gate_fidelity_score = self.calculate_gate_fidelity_score(pattern);
1381                let depth_efficiency_score = 1.0 / (pattern.gate_sequence.len() as f64 + 1.0);
1382
1383                // Weighted combination of scores
1384                let total_score =
1385                    gate_fidelity_score.mul_add(0.2, pattern.hardware_efficiency.mul_add(0.4, connectivity_score * 0.3)) +
1386                    depth_efficiency_score * 0.1;
1387
1388                (pattern, total_score)
1389            })
1390            .collect();
1391
1392        scored_patterns
1393            .iter()
1394            .max_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal))
1395            .map(|(pattern, _)| *pattern)
1396            .ok_or_else(|| {
1397                SimulatorError::InvalidConfiguration("No suitable ansatz pattern found".to_string())
1398            })
1399    }
1400
1401    fn add_ansatz_layer(
1402        &self,
1403        circuit: &mut InterfaceCircuit,
1404        pattern: &AnsatzPattern,
1405        layer: usize,
1406    ) -> Result<()> {
1407        // Add ansatz layer based on pattern with hardware-aware qubit mapping
1408        let mut qubit_pairs_used = HashSet::new();
1409
1410        for (i, gate_type) in pattern.gate_sequence.iter().enumerate() {
1411            match gate_type {
1412                // Single-qubit gates
1413                InterfaceGateType::Hadamard
1414                | InterfaceGateType::PauliX
1415                | InterfaceGateType::PauliY
1416                | InterfaceGateType::PauliZ
1417                | InterfaceGateType::RX(_)
1418                | InterfaceGateType::RY(_)
1419                | InterfaceGateType::RZ(_) => {
1420                    let qubit = (layer + i) % circuit.num_qubits;
1421                    circuit.add_gate(InterfaceGate::new(gate_type.clone(), vec![qubit]));
1422                }
1423                // Two-qubit gates - use hardware-aware mapping
1424                InterfaceGateType::CNOT | InterfaceGateType::CZ | InterfaceGateType::CPhase(_) => {
1425                    let control = (layer + i) % circuit.num_qubits;
1426                    let target = (control + 1) % circuit.num_qubits;
1427
1428                    // Check if this qubit pair is available based on connectivity
1429                    let pair = if control < target {
1430                        (control, target)
1431                    } else {
1432                        (target, control)
1433                    };
1434
1435                    if !qubit_pairs_used.contains(&pair)
1436                        && self.is_qubit_pair_connected(control, target)
1437                    {
1438                        circuit
1439                            .add_gate(InterfaceGate::new(gate_type.clone(), vec![control, target]));
1440                        qubit_pairs_used.insert(pair);
1441                    } else {
1442                        // Find alternative connected pair
1443                        if let Some((alt_control, alt_target)) = self
1444                            .find_available_connected_pair(circuit.num_qubits, &qubit_pairs_used)
1445                        {
1446                            circuit.add_gate(InterfaceGate::new(
1447                                gate_type.clone(),
1448                                vec![alt_control, alt_target],
1449                            ));
1450                            qubit_pairs_used
1451                                .insert((alt_control.min(alt_target), alt_control.max(alt_target)));
1452                        }
1453                    }
1454                }
1455                _ => {
1456                    // Default single-qubit mapping for other gates
1457                    let qubit = (layer + i) % circuit.num_qubits;
1458                    circuit.add_gate(InterfaceGate::new(gate_type.clone(), vec![qubit]));
1459                }
1460            }
1461        }
1462        Ok(())
1463    }
1464
1465    fn analyze_training_data(&self, _data: &Array2<f64>) -> Result<TrainingDataAnalysis> {
1466        Ok(TrainingDataAnalysis::default())
1467    }
1468
1469    const fn optimize_batch_size(&self, _analysis: &TrainingDataAnalysis) -> Result<usize> {
1470        Ok(32) // Default batch size
1471    }
1472
1473    const fn should_enable_mixed_precision(&self) -> Result<bool> {
1474        Ok(true) // Enable for noise-aware optimization
1475    }
1476
1477    fn start_performance_monitoring(&mut self) -> Result<()> {
1478        // Initialize performance monitoring
1479        self.performance_monitor
1480            .timestamps
1481            .push_back(Instant::now());
1482        Ok(())
1483    }
1484
1485    pub fn check_adaptation_trigger(
1486        trigger: &AdaptationTrigger,
1487        performance: &PerformanceMetrics,
1488    ) -> Result<bool> {
1489        match trigger {
1490            AdaptationTrigger::ErrorRateThreshold(threshold) => {
1491                Ok(performance.error_rate > *threshold)
1492            }
1493            AdaptationTrigger::ExecutionTimeThreshold(threshold) => {
1494                Ok(performance.avg_execution_time > *threshold)
1495            }
1496            _ => Ok(false), // Simplified for demonstration
1497        }
1498    }
1499
1500    const fn determine_adaptation_action(
1501        trigger: &AdaptationTrigger,
1502        _performance: &PerformanceMetrics,
1503    ) -> Result<AdaptationAction> {
1504        match trigger {
1505            AdaptationTrigger::ErrorRateThreshold(_) => Ok(AdaptationAction::RecompileCircuit(
1506                HardwareOptimizationLevel::Aggressive,
1507            )),
1508            AdaptationTrigger::ExecutionTimeThreshold(_) => {
1509                Ok(AdaptationAction::AdjustBatchSize(16))
1510            }
1511            _ => Ok(AdaptationAction::RecompileCircuit(
1512                HardwareOptimizationLevel::Balanced,
1513            )),
1514        }
1515    }
1516
1517    /// Calculate how well a pattern matches device connectivity
1518    fn calculate_connectivity_compatibility(&self, pattern: &AnsatzPattern) -> f64 {
1519        let mut compatibility_score = 0.0;
1520        let total_connections = pattern.gate_sequence.len();
1521
1522        if total_connections == 0 {
1523            return 1.0;
1524        }
1525
1526        // Check each gate in the pattern against device connectivity
1527        for gate_type in &pattern.gate_sequence {
1528            let gate_compatibility = match gate_type {
1529                InterfaceGateType::CNOT | InterfaceGateType::CZ | InterfaceGateType::CPhase(_) => {
1530                    // Two-qubit gates need good connectivity
1531                    if self.device_metrics.connectivity_graph.len() > 2 {
1532                        0.8 // Assume good connectivity for now
1533                    } else {
1534                        0.3
1535                    }
1536                }
1537                _ => 1.0, // Single-qubit gates are always compatible
1538            };
1539            compatibility_score += gate_compatibility;
1540        }
1541
1542        compatibility_score / total_connections as f64
1543    }
1544
1545    /// Calculate gate fidelity score for a pattern
1546    fn calculate_gate_fidelity_score(&self, pattern: &AnsatzPattern) -> f64 {
1547        let mut total_fidelity = 0.0;
1548        let total_gates = pattern.gate_sequence.len();
1549
1550        if total_gates == 0 {
1551            return 1.0;
1552        }
1553
1554        for gate_type in &pattern.gate_sequence {
1555            let gate_name = format!("{gate_type:?}");
1556            let error_rate = self
1557                .device_metrics
1558                .gate_error_rates
1559                .get(&gate_name)
1560                .unwrap_or(&1e-3);
1561            let fidelity = 1.0 - error_rate;
1562            total_fidelity += fidelity;
1563        }
1564
1565        total_fidelity / total_gates as f64
1566    }
1567
1568    /// Check if two qubits are connected in the device topology
1569    fn is_qubit_pair_connected(&self, qubit1: usize, qubit2: usize) -> bool {
1570        // Check connectivity graph for direct connection
1571        if let Some(&is_connected) = self.device_metrics.connectivity_graph.get((qubit1, qubit2)) {
1572            is_connected
1573        } else {
1574            // If no connectivity info, assume linear connectivity for adjacent qubits
1575            (qubit1 as i32 - qubit2 as i32).abs() == 1
1576        }
1577    }
1578
1579    /// Find an available connected qubit pair
1580    fn find_available_connected_pair(
1581        &self,
1582        num_qubits: usize,
1583        used_pairs: &HashSet<(usize, usize)>,
1584    ) -> Option<(usize, usize)> {
1585        for i in 0..num_qubits {
1586            for j in (i + 1)..num_qubits {
1587                let pair = (i, j);
1588                if !used_pairs.contains(&pair) && self.is_qubit_pair_connected(i, j) {
1589                    return Some((i, j));
1590                }
1591            }
1592        }
1593        None
1594    }
1595
1596    /// Analyze circuit (public version)
1597    pub fn analyze_circuit_public(&self, circuit: &InterfaceCircuit) -> Result<CircuitAnalysis> {
1598        self.analyze_circuit(circuit)
1599    }
1600
1601    /// Optimize qubit mapping (public version)
1602    pub fn optimize_qubit_mapping_public(
1603        &self,
1604        circuit: &InterfaceCircuit,
1605        analysis: &CircuitAnalysis,
1606    ) -> Result<HashMap<usize, usize>> {
1607        self.optimize_qubit_mapping(circuit, analysis)
1608    }
1609
1610    /// Check if gate is directly executable (public version)
1611    #[must_use]
1612    pub const fn is_gate_directly_executable_public(
1613        &self,
1614        gate_type: &InterfaceGateType,
1615        qubits: &[usize],
1616    ) -> bool {
1617        self.is_gate_directly_executable(gate_type, qubits)
1618    }
1619
1620    /// Decompose or route gate (public version)
1621    pub fn decompose_or_route_gate_public(
1622        &self,
1623        gate_type: &InterfaceGateType,
1624        qubits: &[usize],
1625    ) -> Result<Vec<InterfaceGate>> {
1626        self.decompose_or_route_gate(gate_type, qubits)
1627    }
1628
1629    /// Apply IBM optimizations (public version)
1630    pub fn apply_ibm_optimizations_public(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1631        self.apply_ibm_optimizations(circuit)
1632    }
1633
1634    /// Check if gates cancel (public version)
1635    #[must_use]
1636    pub fn gates_cancel_public(&self, gate1: &InterfaceGate, gate2: &InterfaceGate) -> bool {
1637        self.gates_cancel(gate1, gate2)
1638    }
1639
1640    /// Estimate error rate (public version)
1641    pub fn estimate_error_rate_public(&self, circuit: &InterfaceCircuit) -> Result<f64> {
1642        self.estimate_error_rate(circuit)
1643    }
1644
1645    /// Start performance monitoring (public version)
1646    pub fn start_performance_monitoring_public(&mut self) -> Result<()> {
1647        self.start_performance_monitoring()
1648    }
1649
1650    /// Get performance monitor reference
1651    #[must_use]
1652    pub const fn get_performance_monitor(&self) -> &PerformanceMonitoringData {
1653        &self.performance_monitor
1654    }
1655}
1656
1657/// Circuit analysis result
1658#[derive(Debug, Clone, Default)]
1659pub struct CircuitAnalysis {
1660    pub gate_counts: HashMap<String, usize>,
1661    pub two_qubit_gates: Vec<Vec<usize>>,
1662    pub parameter_count: usize,
1663    pub circuit_depth: usize,
1664    pub entanglement_measure: f64,
1665}
1666
1667/// Training data analysis
1668#[derive(Debug, Clone, Default)]
1669pub struct TrainingDataAnalysis {
1670    pub data_size: usize,
1671    pub feature_dimension: usize,
1672    pub complexity_measure: f64,
1673}
1674
1675impl HardwareAwareAnsatz {
1676    fn new(_config: &HardwareAwareConfig) -> Result<Self> {
1677        Ok(Self {
1678            architecture_patterns: HashMap::new(),
1679            entangling_patterns: Vec::new(),
1680            parameter_efficiency: 0.8,
1681            hardware_cost: 1.0,
1682        })
1683    }
1684}
1685
1686impl HardwareCircuitCompiler {
1687    fn new() -> Self {
1688        Self {
1689            compilation_cache: HashMap::new(),
1690            routing_algorithms: vec![RoutingAlgorithm::ShortestPath],
1691            optimization_passes: vec![OptimizationPass::GateCancellation],
1692            compilation_stats: CompilationStatistics::default(),
1693        }
1694    }
1695}
1696
1697impl DynamicAdaptationStrategy {
1698    fn new(_config: &HardwareAwareConfig) -> Result<Self> {
1699        Ok(Self {
1700            triggers: vec![
1701                AdaptationTrigger::ErrorRateThreshold(0.1),
1702                AdaptationTrigger::ExecutionTimeThreshold(Duration::from_secs(10)),
1703            ],
1704            actions: Vec::new(),
1705            history: Vec::new(),
1706            current_state: AdaptationState::default(),
1707        })
1708    }
1709}
1710
1711/// Benchmark function for hardware-aware QML optimization
1712pub fn benchmark_hardware_aware_qml() -> Result<()> {
1713    println!("Benchmarking Hardware-Aware QML Optimization...");
1714
1715    let config = HardwareAwareConfig {
1716        target_architecture: HardwareArchitecture::IBMQuantum,
1717        ..Default::default()
1718    };
1719
1720    let mut optimizer = HardwareAwareQMLOptimizer::new(config)?;
1721
1722    // Create test circuit
1723    let mut circuit = InterfaceCircuit::new(4, 0);
1724    circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![0]));
1725    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![0, 1]));
1726    circuit.add_gate(InterfaceGate::new(InterfaceGateType::RY(0.5), vec![2]));
1727    circuit.add_gate(InterfaceGate::new(
1728        InterfaceGateType::Toffoli,
1729        vec![0, 1, 2],
1730    ));
1731
1732    let start_time = Instant::now();
1733
1734    // Optimize circuit for hardware
1735    let optimized_result = optimizer.optimize_qml_circuit(&circuit, None)?;
1736
1737    let duration = start_time.elapsed();
1738
1739    println!("✅ Hardware-Aware QML Optimization Results:");
1740    println!("   Original Gates: {}", circuit.gates.len());
1741    println!(
1742        "   Optimized Gates: {}",
1743        optimized_result.circuit.gates.len()
1744    );
1745    println!(
1746        "   Gate Count Optimization: {:?}",
1747        optimized_result.gate_count_optimization
1748    );
1749    println!(
1750        "   Depth Optimization: {:?}",
1751        optimized_result.depth_optimization
1752    );
1753    println!(
1754        "   Expected Error Rate: {:.6}",
1755        optimized_result.expected_error_rate
1756    );
1757    println!(
1758        "   Gates Eliminated: {}",
1759        optimized_result.optimization_stats.gates_eliminated
1760    );
1761    println!(
1762        "   SWAP Gates Added: {}",
1763        optimized_result.optimization_stats.swap_gates_inserted
1764    );
1765    println!(
1766        "   Compilation Time: {}ms",
1767        optimized_result.compilation_time_ms
1768    );
1769    println!("   Total Optimization Time: {:.2}ms", duration.as_millis());
1770
1771    // Test hardware-efficient ansatz generation
1772    let ansatz_circuit = optimizer.generate_hardware_efficient_ansatz(4, 3, 0.8)?;
1773    println!("   Generated Ansatz Gates: {}", ansatz_circuit.gates.len());
1774
1775    Ok(())
1776}
1777
1778#[cfg(test)]
1779mod tests {
1780    use super::*;
1781
1782    #[test]
1783    fn test_hardware_aware_optimizer_creation() {
1784        let config = HardwareAwareConfig::default();
1785        let optimizer = HardwareAwareQMLOptimizer::new(config);
1786        assert!(optimizer.is_ok());
1787    }
1788
1789    #[test]
1790    fn test_circuit_analysis() {
1791        let config = HardwareAwareConfig::default();
1792        let optimizer = HardwareAwareQMLOptimizer::new(config)
1793            .expect("Optimizer creation should succeed in test");
1794
1795        let mut circuit = InterfaceCircuit::new(2, 0);
1796        circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![0]));
1797        circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![0, 1]));
1798
1799        let analysis = optimizer.analyze_circuit(&circuit);
1800        assert!(analysis.is_ok());
1801
1802        let analysis = analysis.expect("Circuit analysis should succeed in test");
1803        assert_eq!(analysis.two_qubit_gates.len(), 1);
1804        assert!(analysis.gate_counts.contains_key("Hadamard"));
1805    }
1806
1807    #[test]
1808    fn test_qubit_mapping_optimization() {
1809        let config = HardwareAwareConfig::default();
1810        let optimizer = HardwareAwareQMLOptimizer::new(config)
1811            .expect("Optimizer creation should succeed in test");
1812
1813        let circuit = InterfaceCircuit::new(4, 0);
1814        let analysis = optimizer
1815            .analyze_circuit(&circuit)
1816            .expect("Circuit analysis should succeed in test");
1817        let mapping = optimizer.optimize_qubit_mapping(&circuit, &analysis);
1818
1819        assert!(mapping.is_ok());
1820        let mapping = mapping.expect("Qubit mapping optimization should succeed in test");
1821        assert_eq!(mapping.len(), 4);
1822    }
1823
1824    #[test]
1825    fn test_hardware_specific_optimizations() {
1826        let config = HardwareAwareConfig {
1827            target_architecture: HardwareArchitecture::IBMQuantum,
1828            ..Default::default()
1829        };
1830        let optimizer = HardwareAwareQMLOptimizer::new(config)
1831            .expect("Optimizer creation should succeed in test");
1832
1833        let mut circuit = InterfaceCircuit::new(2, 0);
1834        circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(0.1), vec![0]));
1835        circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(0.2), vec![0]));
1836
1837        let original_gates = circuit.gates.len();
1838        optimizer
1839            .apply_ibm_optimizations(&mut circuit)
1840            .expect("IBM optimizations should succeed in test");
1841
1842        // Should fuse consecutive RZ gates
1843        assert!(circuit.gates.len() <= original_gates);
1844    }
1845
1846    #[test]
1847    fn test_gate_cancellation() {
1848        let config = HardwareAwareConfig::default();
1849        let optimizer = HardwareAwareQMLOptimizer::new(config)
1850            .expect("Optimizer creation should succeed in test");
1851
1852        let gate1 = InterfaceGate::new(InterfaceGateType::PauliX, vec![0]);
1853        let gate2 = InterfaceGate::new(InterfaceGateType::PauliX, vec![0]);
1854
1855        assert!(optimizer.gates_cancel(&gate1, &gate2));
1856
1857        let gate3 = InterfaceGate::new(InterfaceGateType::PauliY, vec![0]);
1858        assert!(!optimizer.gates_cancel(&gate1, &gate3));
1859    }
1860
1861    #[test]
1862    fn test_error_rate_estimation() {
1863        let config = HardwareAwareConfig::default();
1864        let optimizer = HardwareAwareQMLOptimizer::new(config)
1865            .expect("Optimizer creation should succeed in test");
1866
1867        let mut circuit = InterfaceCircuit::new(2, 0);
1868        circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![0, 1]));
1869
1870        let error_rate = optimizer.estimate_error_rate(&circuit);
1871        assert!(error_rate.is_ok());
1872        assert!(error_rate.expect("Error rate estimation should succeed in test") > 0.0);
1873    }
1874
1875    #[test]
1876    fn test_cross_device_compatibility() {
1877        let config = HardwareAwareConfig::default();
1878        let optimizer = HardwareAwareQMLOptimizer::new(config)
1879            .expect("Optimizer creation should succeed in test");
1880
1881        let compatibility = optimizer.get_cross_device_compatibility(
1882            HardwareArchitecture::IBMQuantum,
1883            HardwareArchitecture::GoogleQuantumAI,
1884        );
1885
1886        assert!((0.0..=1.0).contains(&compatibility));
1887    }
1888}
quantrs2_sim/hardware_aware_qml.rs

quantrs2_sim/
hardware_aware_qml.rs
