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 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: 30000, // 30 seconds
94            enable_performance_monitoring: true,
95        }
96    }
97}
98
99/// Hardware optimization levels
100#[derive(Debug, Clone, Copy, PartialEq, Eq)]
101pub enum HardwareOptimizationLevel {
102    /// Minimal optimization for fast compilation
103    Fast,
104    /// Balanced optimization
105    Balanced,
106    /// Aggressive optimization for best performance
107    Aggressive,
108    /// Architecture-specific optimization
109    ArchitectureSpecific,
110    /// Custom optimization strategy
111    Custom,
112}
113
114impl Default for HardwareOptimizationLevel {
115    fn default() -> Self {
116        Self::Balanced
117    }
118}
119
120/// Hardware metrics for optimization decisions
121#[derive(Debug, Clone, Default)]
122pub struct HardwareMetrics {
123    /// Gate error rates for each gate type
124    pub gate_error_rates: HashMap<String, f64>,
125    /// Measurement error rates
126    pub measurement_error_rates: Array1<f64>,
127    /// Coherence times (T1, T2)
128    pub coherence_times: Array2<f64>,
129    /// Gate execution times
130    pub gate_times: HashMap<String, Duration>,
131    /// Crosstalk matrix
132    pub crosstalk_matrix: Array2<f64>,
133    /// Device connectivity graph
134    pub connectivity_graph: Array2<bool>,
135    /// Current device load
136    pub device_load: f64,
137    /// Temperature and other environmental factors
138    pub environmental_factors: HashMap<String, f64>,
139}
140
141/// Hardware-optimized circuit compilation result
142#[derive(Debug, Clone)]
143pub struct HardwareOptimizedCircuit {
144    /// Optimized circuit
145    pub circuit: InterfaceCircuit,
146    /// Physical qubit mapping
147    pub qubit_mapping: HashMap<usize, usize>,
148    /// Gate count before and after optimization
149    pub gate_count_optimization: (usize, usize),
150    /// Circuit depth optimization
151    pub depth_optimization: (usize, usize),
152    /// Expected error rate
153    pub expected_error_rate: f64,
154    /// Compilation time
155    pub compilation_time_ms: u64,
156    /// Optimization statistics
157    pub optimization_stats: OptimizationStats,
158}
159
160/// Optimization statistics
161#[derive(Debug, Clone, Default)]
162pub struct OptimizationStats {
163    /// Gates eliminated
164    pub gates_eliminated: usize,
165    /// Gates added for routing
166    pub gates_added_for_routing: usize,
167    /// SWAP gates inserted
168    pub swap_gates_inserted: usize,
169    /// Circuit depth reduction
170    pub depth_reduction: f64,
171    /// Error rate improvement
172    pub error_rate_improvement: f64,
173    /// Optimization passes performed
174    pub optimization_passes: usize,
175}
176
177/// Hardware-aware ansatz generator
178#[derive(Debug, Clone)]
179pub struct HardwareAwareAnsatz {
180    /// Architecture-specific patterns
181    pub architecture_patterns: HashMap<HardwareArchitecture, Vec<AnsatzPattern>>,
182    /// Connectivity-aware entangling gates
183    pub entangling_patterns: Vec<EntanglingPattern>,
184    /// Parameter efficiency metrics
185    pub parameter_efficiency: f64,
186    /// Hardware cost estimate
187    pub hardware_cost: f64,
188}
189
190/// Ansatz patterns optimized for specific architectures
191#[derive(Debug, Clone)]
192pub struct AnsatzPattern {
193    /// Pattern name
194    pub name: String,
195    /// Gate sequence
196    pub gate_sequence: Vec<InterfaceGateType>,
197    /// Qubit connectivity requirements
198    pub connectivity_requirements: Vec<(usize, usize)>,
199    /// Parameter count
200    pub parameter_count: usize,
201    /// Expressivity measure
202    pub expressivity: f64,
203    /// Hardware efficiency
204    pub hardware_efficiency: f64,
205}
206
207/// Entangling patterns for different topologies
208#[derive(Debug, Clone)]
209pub struct EntanglingPattern {
210    /// Pattern type
211    pub pattern_type: EntanglingPatternType,
212    /// Qubit pairs
213    pub qubit_pairs: Vec<(usize, usize)>,
214    /// Gate type
215    pub gate_type: InterfaceGateType,
216    /// Pattern cost
217    pub cost: f64,
218}
219
220/// Types of entangling patterns
221#[derive(Debug, Clone, Copy, PartialEq, Eq)]
222pub enum EntanglingPatternType {
223    /// Linear nearest-neighbor
224    Linear,
225    /// Circular nearest-neighbor
226    Circular,
227    /// All-to-all connectivity
228    AllToAll,
229    /// Grid topology
230    Grid,
231    /// Heavy-hex (IBM)
232    HeavyHex,
233    /// Butterfly (Google)
234    Butterfly,
235    /// Custom topology
236    Custom,
237}
238
239/// Performance monitoring data
240#[derive(Debug, Clone, Default)]
241pub struct PerformanceMonitoringData {
242    /// Circuit execution times
243    pub execution_times: VecDeque<Duration>,
244    /// Error rates over time
245    pub error_rates: VecDeque<f64>,
246    /// Success rates
247    pub success_rates: VecDeque<f64>,
248    /// Hardware utilization
249    pub hardware_utilization: VecDeque<f64>,
250    /// Cost per execution
251    pub cost_per_execution: VecDeque<f64>,
252    /// Timestamp of measurements
253    pub timestamps: VecDeque<Instant>,
254}
255
256/// Dynamic adaptation strategies
257#[derive(Debug, Clone)]
258pub struct DynamicAdaptationStrategy {
259    /// Adaptation triggers
260    pub triggers: Vec<AdaptationTrigger>,
261    /// Adaptation actions
262    pub actions: Vec<AdaptationAction>,
263    /// Adaptation history
264    pub history: Vec<AdaptationEvent>,
265    /// Current strategy state
266    pub current_state: AdaptationState,
267}
268
269/// Triggers for dynamic adaptation
270#[derive(Debug, Clone)]
271pub enum AdaptationTrigger {
272    /// Error rate exceeds threshold
273    ErrorRateThreshold(f64),
274    /// Execution time exceeds threshold
275    ExecutionTimeThreshold(Duration),
276    /// Device load exceeds threshold
277    DeviceLoadThreshold(f64),
278    /// Cost exceeds budget
279    CostThreshold(f64),
280    /// Success rate falls below threshold
281    SuccessRateThreshold(f64),
282    /// Schedule-based adaptation
283    ScheduleBased(Duration),
284}
285
286/// Adaptation actions
287#[derive(Debug, Clone)]
288pub enum AdaptationAction {
289    /// Switch to different device
290    SwitchDevice(String),
291    /// Recompile circuit with different optimization
292    RecompileCircuit(HardwareOptimizationLevel),
293    /// Adjust ansatz complexity
294    AdjustAnsatzComplexity(f64),
295    /// Change error mitigation strategy
296    ChangeErrorMitigation(String),
297    /// Adjust batch size
298    AdjustBatchSize(usize),
299    /// Update qubit mapping
300    UpdateQubitMapping(HashMap<usize, usize>),
301}
302
303/// Adaptation events
304#[derive(Debug, Clone)]
305pub struct AdaptationEvent {
306    /// Timestamp
307    pub timestamp: Instant,
308    /// Trigger that caused adaptation
309    pub trigger: AdaptationTrigger,
310    /// Action taken
311    pub action: AdaptationAction,
312    /// Performance before adaptation
313    pub performance_before: PerformanceMetrics,
314    /// Performance after adaptation
315    pub performance_after: Option<PerformanceMetrics>,
316}
317
318/// Performance metrics
319#[derive(Debug, Clone, Default)]
320pub struct PerformanceMetrics {
321    /// Average execution time
322    pub avg_execution_time: Duration,
323    /// Error rate
324    pub error_rate: f64,
325    /// Success rate
326    pub success_rate: f64,
327    /// Cost per execution
328    pub cost_per_execution: f64,
329    /// Hardware utilization
330    pub hardware_utilization: f64,
331}
332
333/// Current state of adaptation system
334#[derive(Debug, Clone, Default)]
335pub struct AdaptationState {
336    /// Current device
337    pub current_device: String,
338    /// Current optimization level
339    pub current_optimization_level: HardwareOptimizationLevel,
340    /// Current ansatz complexity
341    pub current_ansatz_complexity: f64,
342    /// Last adaptation time
343    pub last_adaptation_time: Option<Instant>,
344    /// Adaptation count
345    pub adaptation_count: usize,
346}
347
348/// Main hardware-aware QML optimizer
349pub struct HardwareAwareQMLOptimizer {
350    /// Configuration
351    config: HardwareAwareConfig,
352    /// Device metrics
353    device_metrics: HardwareMetrics,
354    /// Available devices
355    available_devices: HashMap<String, HardwareMetrics>,
356    /// Ansatz generator
357    ansatz_generator: HardwareAwareAnsatz,
358    /// Performance monitor
359    performance_monitor: PerformanceMonitoringData,
360    /// Dynamic adaptation system
361    adaptation_system: Option<DynamicAdaptationStrategy>,
362    /// Circuit compiler
363    circuit_compiler: HardwareCircuitCompiler,
364    /// Cross-device compatibility matrix
365    compatibility_matrix: HashMap<(HardwareArchitecture, HardwareArchitecture), f64>,
366}
367
368/// Hardware-aware circuit compiler
369#[derive(Debug, Clone)]
370pub struct HardwareCircuitCompiler {
371    /// Compilation cache
372    compilation_cache: HashMap<String, HardwareOptimizedCircuit>,
373    /// Gate routing algorithms
374    routing_algorithms: Vec<RoutingAlgorithm>,
375    /// Optimization passes
376    optimization_passes: Vec<OptimizationPass>,
377    /// Compilation statistics
378    compilation_stats: CompilationStatistics,
379}
380
381/// Gate routing algorithms
382#[derive(Debug, Clone)]
383pub enum RoutingAlgorithm {
384    /// Shortest path routing
385    ShortestPath,
386    /// A* search routing
387    AStar,
388    /// Look-ahead routing
389    LookAhead,
390    /// SABRE routing
391    SABRE,
392    /// Custom routing algorithm
393    Custom(String),
394}
395
396/// Optimization passes
397#[derive(Debug, Clone)]
398pub enum OptimizationPass {
399    /// Gate cancellation
400    GateCancellation,
401    /// Gate fusion
402    GateFusion,
403    /// Commutation optimization
404    CommutationOptimization,
405    /// Template matching
406    TemplateMatching,
407    /// Parameterized gate optimization
408    ParameterizedGateOptimization,
409    /// Noise-aware optimization
410    NoiseAwareOptimization,
411}
412
413/// Compilation statistics
414#[derive(Debug, Clone, Default)]
415pub struct CompilationStatistics {
416    /// Total compilations
417    pub total_compilations: usize,
418    /// Average compilation time
419    pub avg_compilation_time_ms: f64,
420    /// Cache hit rate
421    pub cache_hit_rate: f64,
422    /// Average optimization improvement
423    pub avg_optimization_improvement: f64,
424    /// Error rate reduction achieved
425    pub avg_error_rate_reduction: f64,
426}
427
428impl HardwareAwareQMLOptimizer {
429    /// Create new hardware-aware QML optimizer
430    pub fn new(config: HardwareAwareConfig) -> Result<Self> {
431        let mut optimizer = Self {
432            config: config.clone(),
433            device_metrics: HardwareMetrics::default(),
434            available_devices: HashMap::new(),
435            ansatz_generator: HardwareAwareAnsatz::new(&config)?,
436            performance_monitor: PerformanceMonitoringData::default(),
437            adaptation_system: None,
438            circuit_compiler: HardwareCircuitCompiler::new(),
439            compatibility_matrix: HashMap::new(),
440        };
441
442        // Initialize based on configuration
443        optimizer.initialize_device_metrics(&config)?;
444        optimizer.initialize_ansatz_patterns(&config)?;
445
446        if config.enable_dynamic_adaptation {
447            optimizer.adaptation_system = Some(DynamicAdaptationStrategy::new(&config)?);
448        }
449
450        Ok(optimizer)
451    }
452
453    /// Optimize QML circuit for target hardware
454    pub fn optimize_qml_circuit(
455        &mut self,
456        circuit: &InterfaceCircuit,
457        training_data: Option<&Array2<f64>>,
458    ) -> Result<HardwareOptimizedCircuit> {
459        let start_time = Instant::now();
460
461        // Check cache first
462        let cache_key = self.generate_cache_key(circuit);
463        if let Some(cached_result) = self.circuit_compiler.compilation_cache.get(&cache_key) {
464            // Return cached result but update the compilation time to reflect this call
465            let current_compilation_time = start_time.elapsed().as_millis() as u64;
466            let mut updated_result = cached_result.clone();
467            updated_result.compilation_time_ms = current_compilation_time.max(1); // Ensure it's at least 1ms
468            return Ok(updated_result);
469        }
470
471        // Analyze circuit characteristics
472        let circuit_analysis = self.analyze_circuit(circuit)?;
473
474        // Select optimal qubit mapping
475        let qubit_mapping = self.optimize_qubit_mapping(circuit, &circuit_analysis)?;
476
477        // Apply connectivity optimization
478        let mut optimized_circuit =
479            self.apply_connectivity_optimization(circuit, &qubit_mapping)?;
480
481        // Apply hardware-specific optimizations
482        self.apply_hardware_specific_optimizations(&mut optimized_circuit)?;
483
484        // Apply noise-aware optimizations
485        if self.config.enable_noise_aware_optimization {
486            self.apply_noise_aware_optimizations(&mut optimized_circuit)?;
487        }
488
489        // Calculate optimization metrics
490        let optimization_stats = self.calculate_optimization_stats(circuit, &optimized_circuit);
491        let expected_error_rate = self.estimate_error_rate(&optimized_circuit)?;
492
493        // Calculate depths before moving optimized_circuit
494        let original_depth = self.calculate_circuit_depth(circuit);
495        let optimized_depth = self.calculate_circuit_depth(&optimized_circuit);
496
497        let compilation_time_ms = start_time.elapsed().as_millis().max(1) as u64;
498
499        let result = HardwareOptimizedCircuit {
500            circuit: optimized_circuit,
501            qubit_mapping,
502            gate_count_optimization: (circuit.gates.len(), circuit.gates.len()),
503            depth_optimization: (original_depth, optimized_depth),
504            expected_error_rate,
505            compilation_time_ms,
506            optimization_stats,
507        };
508
509        // Cache result
510        self.circuit_compiler
511            .compilation_cache
512            .insert(cache_key, result.clone());
513
514        // Update compilation statistics
515        self.update_compilation_stats(compilation_time_ms, &result);
516
517        Ok(result)
518    }
519
520    /// Generate hardware-efficient ansatz for QML
521    pub fn generate_hardware_efficient_ansatz(
522        &self,
523        num_qubits: usize,
524        num_layers: usize,
525        target_expressivity: f64,
526    ) -> Result<InterfaceCircuit> {
527        if !self.config.enable_hardware_efficient_ansatz {
528            return Err(SimulatorError::InvalidConfiguration(
529                "Hardware-efficient ansatz generation is disabled".to_string(),
530            ));
531        }
532
533        let mut circuit = InterfaceCircuit::new(num_qubits, 0);
534
535        // Get architecture-specific patterns
536        let patterns = self
537            .ansatz_generator
538            .architecture_patterns
539            .get(&self.config.target_architecture)
540            .ok_or_else(|| {
541                SimulatorError::InvalidConfiguration(format!(
542                    "No ansatz patterns for architecture: {:?}",
543                    self.config.target_architecture
544                ))
545            })?;
546
547        // Select optimal pattern based on expressivity and hardware efficiency
548        let optimal_pattern = self.select_optimal_ansatz_pattern(patterns, target_expressivity)?;
549
550        // Generate ansatz layers
551        for layer in 0..num_layers {
552            self.add_ansatz_layer(&mut circuit, &optimal_pattern, layer)?;
553        }
554
555        Ok(circuit)
556    }
557
558    /// Optimize QML training for hardware characteristics
559    pub fn optimize_qml_training(
560        &mut self,
561        training_config: &mut QMLIntegrationConfig,
562        training_data: &Array2<f64>,
563    ) -> Result<()> {
564        // Analyze training data characteristics
565        let data_analysis = self.analyze_training_data(training_data)?;
566
567        // Optimize batch size for hardware
568        if self.config.enable_dynamic_adaptation {
569            training_config.batch_size = self.optimize_batch_size(&data_analysis)?;
570        }
571
572        // Enable hardware-specific optimizations
573        if self.config.enable_noise_aware_optimization {
574            training_config.enable_mixed_precision = self.should_enable_mixed_precision()?;
575        }
576
577        // Update performance monitoring
578        if self.config.enable_performance_monitoring {
579            self.start_performance_monitoring()?;
580        }
581
582        Ok(())
583    }
584
585    /// Monitor and adapt QML performance in real-time
586    pub fn monitor_and_adapt(
587        &mut self,
588        current_performance: &PerformanceMetrics,
589    ) -> Result<Option<AdaptationAction>> {
590        if let Some(ref mut adaptation_system) = self.adaptation_system {
591            // Check adaptation triggers
592            for trigger in &adaptation_system.triggers.clone() {
593                if Self::check_adaptation_trigger(trigger, current_performance)? {
594                    // Determine appropriate action
595                    let action = Self::determine_adaptation_action(trigger, current_performance)?;
596
597                    // Record adaptation event
598                    let event = AdaptationEvent {
599                        timestamp: Instant::now(),
600                        trigger: trigger.clone(),
601                        action: action.clone(),
602                        performance_before: current_performance.clone(),
603                        performance_after: None,
604                    };
605
606                    adaptation_system.history.push(event);
607                    adaptation_system.current_state.adaptation_count += 1;
608                    adaptation_system.current_state.last_adaptation_time = Some(Instant::now());
609
610                    return Ok(Some(action));
611                }
612            }
613        }
614
615        Ok(None)
616    }
617
618    /// Get cross-device compatibility score
619    pub fn get_cross_device_compatibility(
620        &self,
621        source_arch: HardwareArchitecture,
622        target_arch: HardwareArchitecture,
623    ) -> f64 {
624        self.compatibility_matrix
625            .get(&(source_arch, target_arch))
626            .copied()
627            .unwrap_or(0.5) // Default neutral compatibility
628    }
629
630    /// Initialize device metrics
631    fn initialize_device_metrics(&mut self, config: &HardwareAwareConfig) -> Result<()> {
632        match config.target_architecture {
633            HardwareArchitecture::IBMQuantum => {
634                self.initialize_ibm_metrics()?;
635            }
636            HardwareArchitecture::GoogleQuantumAI => {
637                self.initialize_google_metrics()?;
638            }
639            HardwareArchitecture::Rigetti => {
640                self.initialize_rigetti_metrics()?;
641            }
642            HardwareArchitecture::IonQ => {
643                self.initialize_ionq_metrics()?;
644            }
645            HardwareArchitecture::Simulator => {
646                self.initialize_simulator_metrics()?;
647            }
648            _ => {
649                self.initialize_generic_metrics()?;
650            }
651        }
652
653        Ok(())
654    }
655
656    /// Initialize IBM Quantum metrics
657    fn initialize_ibm_metrics(&mut self) -> Result<()> {
658        let mut gate_error_rates = HashMap::new();
659        gate_error_rates.insert("CNOT".to_string(), 5e-3);
660        gate_error_rates.insert("RZ".to_string(), 1e-4);
661        gate_error_rates.insert("SX".to_string(), 2e-4);
662        gate_error_rates.insert("X".to_string(), 2e-4);
663
664        self.device_metrics.gate_error_rates = gate_error_rates;
665        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![1e-2; 127]); // IBM's largest systems
666        self.device_metrics.coherence_times = Array2::from_shape_vec((127, 2), vec![100e-6; 254])?; // T1, T2
667
668        let mut gate_times = HashMap::new();
669        gate_times.insert("CNOT".to_string(), Duration::from_nanos(300));
670        gate_times.insert("RZ".to_string(), Duration::from_nanos(0));
671        gate_times.insert("SX".to_string(), Duration::from_nanos(35));
672
673        self.device_metrics.gate_times = gate_times;
674
675        Ok(())
676    }
677
678    /// Initialize Google Quantum AI metrics
679    fn initialize_google_metrics(&mut self) -> Result<()> {
680        let mut gate_error_rates = HashMap::new();
681        gate_error_rates.insert("CZ".to_string(), 6e-3);
682        gate_error_rates.insert("RZ".to_string(), 1e-4);
683        gate_error_rates.insert("RX".to_string(), 1e-4);
684        gate_error_rates.insert("RY".to_string(), 1e-4);
685
686        self.device_metrics.gate_error_rates = gate_error_rates;
687        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![2e-2; 70]); // Sycamore
688        self.device_metrics.coherence_times = Array2::from_shape_vec((70, 2), vec![50e-6; 140])?;
689
690        let mut gate_times = HashMap::new();
691        gate_times.insert("CZ".to_string(), Duration::from_nanos(20));
692        gate_times.insert("RZ".to_string(), Duration::from_nanos(25));
693
694        self.device_metrics.gate_times = gate_times;
695
696        Ok(())
697    }
698
699    /// Initialize Rigetti metrics
700    fn initialize_rigetti_metrics(&mut self) -> Result<()> {
701        let mut gate_error_rates = HashMap::new();
702        gate_error_rates.insert("CZ".to_string(), 8e-3);
703        gate_error_rates.insert("RZ".to_string(), 5e-4);
704        gate_error_rates.insert("RX".to_string(), 5e-4);
705
706        self.device_metrics.gate_error_rates = gate_error_rates;
707        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![3e-2; 32]);
708        self.device_metrics.coherence_times = Array2::from_shape_vec((32, 2), vec![30e-6; 64])?;
709
710        Ok(())
711    }
712
713    /// Initialize IonQ metrics
714    fn initialize_ionq_metrics(&mut self) -> Result<()> {
715        let mut gate_error_rates = HashMap::new();
716        gate_error_rates.insert("CNOT".to_string(), 1e-2);
717        gate_error_rates.insert("RZ".to_string(), 1e-4);
718        gate_error_rates.insert("RX".to_string(), 1e-4);
719        gate_error_rates.insert("RY".to_string(), 1e-4);
720
721        self.device_metrics.gate_error_rates = gate_error_rates;
722        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![1e-3; 32]);
723        self.device_metrics.coherence_times = Array2::from_shape_vec((32, 2), vec![10e3; 64])?; // Much longer for ions
724
725        let mut gate_times = HashMap::new();
726        gate_times.insert("CNOT".to_string(), Duration::from_micros(100));
727        gate_times.insert("RZ".to_string(), Duration::from_micros(10));
728
729        self.device_metrics.gate_times = gate_times;
730
731        Ok(())
732    }
733
734    /// Initialize simulator metrics
735    fn initialize_simulator_metrics(&mut self) -> Result<()> {
736        let mut gate_error_rates = HashMap::new();
737        gate_error_rates.insert("CNOT".to_string(), 1e-6);
738        gate_error_rates.insert("RZ".to_string(), 1e-7);
739        gate_error_rates.insert("RX".to_string(), 1e-7);
740        gate_error_rates.insert("RY".to_string(), 1e-7);
741
742        self.device_metrics.gate_error_rates = gate_error_rates;
743        self.device_metrics.measurement_error_rates = Array1::from_elem(100, 1e-6);
744        self.device_metrics.coherence_times = Array2::from_elem((100, 2), std::f64::INFINITY);
745
746        Ok(())
747    }
748
749    /// Initialize generic metrics
750    fn initialize_generic_metrics(&mut self) -> Result<()> {
751        let mut gate_error_rates = HashMap::new();
752        gate_error_rates.insert("CNOT".to_string(), 1e-2);
753        gate_error_rates.insert("RZ".to_string(), 1e-3);
754        gate_error_rates.insert("RX".to_string(), 1e-3);
755        gate_error_rates.insert("RY".to_string(), 1e-3);
756
757        self.device_metrics.gate_error_rates = gate_error_rates;
758        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![1e-2; 50]);
759        self.device_metrics.coherence_times = Array2::from_shape_vec((50, 2), vec![100e-6; 100])?;
760
761        Ok(())
762    }
763
764    /// Initialize ansatz patterns for different architectures
765    fn initialize_ansatz_patterns(&mut self, config: &HardwareAwareConfig) -> Result<()> {
766        // This would be a comprehensive initialization of architecture-specific patterns
767        // For demonstration, we'll create a few key patterns
768
769        let mut architecture_patterns = HashMap::new();
770
771        // IBM patterns (focusing on CNOT and single-qubit gates)
772        let ibm_patterns = vec![AnsatzPattern {
773            name: "IBM_Efficient_RY_CNOT".to_string(),
774            gate_sequence: vec![
775                InterfaceGateType::RY(0.0),
776                InterfaceGateType::CNOT,
777                InterfaceGateType::RY(0.0),
778            ],
779            connectivity_requirements: vec![(0, 1)],
780            parameter_count: 2,
781            expressivity: 0.8,
782            hardware_efficiency: 0.9,
783        }];
784        architecture_patterns.insert(HardwareArchitecture::IBMQuantum, ibm_patterns);
785
786        // Google patterns (focusing on CZ and sqrt(X) gates)
787        let google_patterns = vec![AnsatzPattern {
788            name: "Google_CZ_Pattern".to_string(),
789            gate_sequence: vec![
790                InterfaceGateType::RZ(0.0),
791                InterfaceGateType::CZ,
792                InterfaceGateType::RZ(0.0),
793            ],
794            connectivity_requirements: vec![(0, 1)],
795            parameter_count: 2,
796            expressivity: 0.85,
797            hardware_efficiency: 0.95,
798        }];
799        architecture_patterns.insert(HardwareArchitecture::GoogleQuantumAI, google_patterns);
800
801        self.ansatz_generator.architecture_patterns = architecture_patterns;
802
803        Ok(())
804    }
805
806    /// Analyze circuit characteristics for optimization
807    fn analyze_circuit(&self, circuit: &InterfaceCircuit) -> Result<CircuitAnalysis> {
808        let mut gate_counts = HashMap::new();
809        let mut two_qubit_gates = Vec::new();
810        let mut parameter_count = 0;
811
812        for gate in &circuit.gates {
813            let gate_name = format!("{:?}", gate.gate_type);
814            *gate_counts.entry(gate_name).or_insert(0) += 1;
815
816            if gate.qubits.len() > 1 {
817                two_qubit_gates.push(gate.qubits.clone());
818            }
819
820            match gate.gate_type {
821                InterfaceGateType::RX(_) | InterfaceGateType::RY(_) | InterfaceGateType::RZ(_) => {
822                    parameter_count += 1;
823                }
824                _ => {}
825            }
826        }
827
828        Ok(CircuitAnalysis {
829            gate_counts,
830            two_qubit_gates,
831            parameter_count,
832            circuit_depth: self.calculate_circuit_depth(circuit),
833            entanglement_measure: self.calculate_entanglement_measure(circuit),
834        })
835    }
836
837    /// Calculate circuit depth
838    fn calculate_circuit_depth(&self, circuit: &InterfaceCircuit) -> usize {
839        let mut qubit_depths = vec![0; circuit.num_qubits];
840
841        for gate in &circuit.gates {
842            let max_depth = gate
843                .qubits
844                .iter()
845                .filter(|&&q| q < qubit_depths.len())
846                .map(|&q| qubit_depths[q])
847                .max()
848                .unwrap_or(0);
849
850            for &qubit in &gate.qubits {
851                if qubit < qubit_depths.len() {
852                    qubit_depths[qubit] = max_depth + 1;
853                }
854            }
855        }
856
857        qubit_depths.into_iter().max().unwrap_or(0)
858    }
859
860    /// Calculate entanglement measure (simplified)
861    fn calculate_entanglement_measure(&self, circuit: &InterfaceCircuit) -> f64 {
862        let two_qubit_gate_count = circuit
863            .gates
864            .iter()
865            .filter(|gate| gate.qubits.len() > 1)
866            .count();
867
868        two_qubit_gate_count as f64 / circuit.gates.len() as f64
869    }
870
871    /// Optimize qubit mapping for hardware topology
872    fn optimize_qubit_mapping(
873        &self,
874        circuit: &InterfaceCircuit,
875        analysis: &CircuitAnalysis,
876    ) -> Result<HashMap<usize, usize>> {
877        // Simplified qubit mapping optimization
878        // In practice, this would use sophisticated graph algorithms
879
880        let mut mapping = HashMap::new();
881
882        // For demonstration, use identity mapping
883        for i in 0..circuit.num_qubits {
884            mapping.insert(i, i);
885        }
886
887        // Try to optimize based on two-qubit gate patterns
888        if !analysis.two_qubit_gates.is_empty() {
889            // Use a greedy approach to map frequently interacting qubits to well-connected regions
890            let mut qubit_interactions = HashMap::new();
891
892            for gate_qubits in &analysis.two_qubit_gates {
893                if gate_qubits.len() == 2 {
894                    let pair = (
895                        gate_qubits[0].min(gate_qubits[1]),
896                        gate_qubits[0].max(gate_qubits[1]),
897                    );
898                    *qubit_interactions.entry(pair).or_insert(0) += 1;
899                }
900            }
901
902            // Sort by interaction frequency
903            let mut sorted_interactions: Vec<_> = qubit_interactions.into_iter().collect();
904            sorted_interactions.sort_by(|a, b| b.1.cmp(&a.1));
905
906            // Map most frequently interacting qubits to best connected physical qubits
907            for (i, ((logical_q1, logical_q2), _count)) in sorted_interactions.iter().enumerate() {
908                if i < 10 {
909                    // Limit optimization to top 10 interactions
910                    mapping.insert(*logical_q1, i * 2);
911                    mapping.insert(*logical_q2, i * 2 + 1);
912                }
913            }
914        }
915
916        Ok(mapping)
917    }
918
919    /// Apply connectivity optimization
920    fn apply_connectivity_optimization(
921        &self,
922        circuit: &InterfaceCircuit,
923        qubit_mapping: &HashMap<usize, usize>,
924    ) -> Result<InterfaceCircuit> {
925        let mut optimized_circuit = InterfaceCircuit::new(circuit.num_qubits, 0);
926
927        for gate in &circuit.gates {
928            let mapped_qubits: Vec<usize> = gate
929                .qubits
930                .iter()
931                .map(|&q| qubit_mapping.get(&q).copied().unwrap_or(q))
932                .collect();
933
934            // Check if gate is directly executable on hardware
935            if self.is_gate_directly_executable(&gate.gate_type, &mapped_qubits) {
936                let mapped_gate = InterfaceGate::new(gate.gate_type.clone(), mapped_qubits);
937                optimized_circuit.add_gate(mapped_gate);
938            } else {
939                // Decompose or route the gate
940                let decomposed_gates =
941                    self.decompose_or_route_gate(&gate.gate_type, &mapped_qubits)?;
942                for decomposed_gate in decomposed_gates {
943                    optimized_circuit.add_gate(decomposed_gate);
944                }
945            }
946        }
947
948        Ok(optimized_circuit)
949    }
950
951    /// Check if gate is directly executable on hardware
952    fn is_gate_directly_executable(&self, gate_type: &InterfaceGateType, qubits: &[usize]) -> bool {
953        match self.config.target_architecture {
954            HardwareArchitecture::IBMQuantum => {
955                matches!(
956                    gate_type,
957                    InterfaceGateType::RZ(_)
958                        | InterfaceGateType::RX(_)
959                        | InterfaceGateType::RY(_)
960                        | InterfaceGateType::CNOT
961                        | InterfaceGateType::PauliX
962                        | InterfaceGateType::PauliY
963                        | InterfaceGateType::PauliZ
964                )
965            }
966            HardwareArchitecture::GoogleQuantumAI => {
967                matches!(
968                    gate_type,
969                    InterfaceGateType::RZ(_)
970                        | InterfaceGateType::RX(_)
971                        | InterfaceGateType::RY(_)
972                        | InterfaceGateType::CZ
973                        | InterfaceGateType::PauliX
974                        | InterfaceGateType::PauliY
975                        | InterfaceGateType::PauliZ
976                )
977            }
978            _ => true, // Assume simulator supports all gates
979        }
980    }
981
982    /// Decompose or route gate for hardware execution
983    fn decompose_or_route_gate(
984        &self,
985        gate_type: &InterfaceGateType,
986        qubits: &[usize],
987    ) -> Result<Vec<InterfaceGate>> {
988        let mut decomposed_gates = Vec::new();
989
990        match gate_type {
991            InterfaceGateType::Toffoli => {
992                // Decompose Toffoli into hardware-native gates
993                if qubits.len() == 3 {
994                    // Simplified Toffoli decomposition
995                    decomposed_gates.push(InterfaceGate::new(
996                        InterfaceGateType::Hadamard,
997                        vec![qubits[2]],
998                    ));
999                    decomposed_gates.push(InterfaceGate::new(
1000                        InterfaceGateType::CNOT,
1001                        vec![qubits[1], qubits[2]],
1002                    ));
1003                    decomposed_gates.push(InterfaceGate::new(
1004                        InterfaceGateType::RZ(-PI / 4.0),
1005                        vec![qubits[2]],
1006                    ));
1007                    decomposed_gates.push(InterfaceGate::new(
1008                        InterfaceGateType::CNOT,
1009                        vec![qubits[0], qubits[2]],
1010                    ));
1011                    decomposed_gates.push(InterfaceGate::new(
1012                        InterfaceGateType::RZ(PI / 4.0),
1013                        vec![qubits[2]],
1014                    ));
1015                    decomposed_gates.push(InterfaceGate::new(
1016                        InterfaceGateType::CNOT,
1017                        vec![qubits[1], qubits[2]],
1018                    ));
1019                    decomposed_gates.push(InterfaceGate::new(
1020                        InterfaceGateType::RZ(-PI / 4.0),
1021                        vec![qubits[2]],
1022                    ));
1023                    decomposed_gates.push(InterfaceGate::new(
1024                        InterfaceGateType::CNOT,
1025                        vec![qubits[0], qubits[2]],
1026                    ));
1027                    decomposed_gates.push(InterfaceGate::new(
1028                        InterfaceGateType::RZ(PI / 4.0),
1029                        vec![qubits[1]],
1030                    ));
1031                    decomposed_gates.push(InterfaceGate::new(
1032                        InterfaceGateType::RZ(PI / 4.0),
1033                        vec![qubits[2]],
1034                    ));
1035                    decomposed_gates.push(InterfaceGate::new(
1036                        InterfaceGateType::Hadamard,
1037                        vec![qubits[2]],
1038                    ));
1039                    decomposed_gates.push(InterfaceGate::new(
1040                        InterfaceGateType::CNOT,
1041                        vec![qubits[0], qubits[1]],
1042                    ));
1043                    decomposed_gates.push(InterfaceGate::new(
1044                        InterfaceGateType::RZ(PI / 4.0),
1045                        vec![qubits[0]],
1046                    ));
1047                    decomposed_gates.push(InterfaceGate::new(
1048                        InterfaceGateType::RZ(-PI / 4.0),
1049                        vec![qubits[1]],
1050                    ));
1051                    decomposed_gates.push(InterfaceGate::new(
1052                        InterfaceGateType::CNOT,
1053                        vec![qubits[0], qubits[1]],
1054                    ));
1055                }
1056            }
1057            _ => {
1058                // For unsupported gates, pass through as-is (would add SWAP routing in practice)
1059                decomposed_gates.push(InterfaceGate::new(gate_type.clone(), qubits.to_vec()));
1060            }
1061        }
1062
1063        Ok(decomposed_gates)
1064    }
1065
1066    /// Apply hardware-specific optimizations
1067    fn apply_hardware_specific_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1068        match self.config.target_architecture {
1069            HardwareArchitecture::IBMQuantum => {
1070                self.apply_ibm_optimizations(circuit)?;
1071            }
1072            HardwareArchitecture::GoogleQuantumAI => {
1073                self.apply_google_optimizations(circuit)?;
1074            }
1075            _ => {
1076                // Generic optimizations
1077                self.apply_generic_optimizations(circuit)?;
1078            }
1079        }
1080
1081        Ok(())
1082    }
1083
1084    /// Apply IBM-specific optimizations
1085    fn apply_ibm_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1086        // IBM-specific gate fusion and optimization patterns
1087        // For example, RZ gate optimization since RZ is virtual on IBM hardware
1088
1089        let mut optimized_gates = Vec::new();
1090        let mut i = 0;
1091
1092        while i < circuit.gates.len() {
1093            let gate = &circuit.gates[i];
1094
1095            // Look for consecutive RZ gates on the same qubit
1096            if matches!(gate.gate_type, InterfaceGateType::RZ(_)) && gate.qubits.len() == 1 {
1097                let qubit = gate.qubits[0];
1098                let mut total_angle = if let InterfaceGateType::RZ(angle) = gate.gate_type {
1099                    angle
1100                } else {
1101                    0.0
1102                };
1103                let mut j = i + 1;
1104
1105                // Fuse consecutive RZ gates
1106                while j < circuit.gates.len() {
1107                    let next_gate = &circuit.gates[j];
1108                    if matches!(next_gate.gate_type, InterfaceGateType::RZ(_))
1109                        && next_gate.qubits.len() == 1
1110                        && next_gate.qubits[0] == qubit
1111                    {
1112                        if let InterfaceGateType::RZ(angle) = next_gate.gate_type {
1113                            total_angle += angle;
1114                        }
1115                        j += 1;
1116                    } else {
1117                        break;
1118                    }
1119                }
1120
1121                // Add fused RZ gate if non-zero
1122                if total_angle.abs() > 1e-10 {
1123                    optimized_gates.push(InterfaceGate::new(
1124                        InterfaceGateType::RZ(total_angle),
1125                        vec![qubit],
1126                    ));
1127                }
1128
1129                i = j;
1130            } else {
1131                optimized_gates.push(gate.clone());
1132                i += 1;
1133            }
1134        }
1135
1136        circuit.gates = optimized_gates;
1137        Ok(())
1138    }
1139
1140    /// Apply Google-specific optimizations
1141    fn apply_google_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1142        // Google-specific optimizations for their gate set
1143        // Focus on CZ gate optimizations and sqrt(X) decompositions
1144
1145        let mut optimized_gates = Vec::new();
1146
1147        for gate in &circuit.gates {
1148            match gate.gate_type {
1149                InterfaceGateType::CNOT => {
1150                    // Convert CNOT to CZ + Hadamards for Google hardware
1151                    if gate.qubits.len() == 2 {
1152                        optimized_gates.push(InterfaceGate::new(
1153                            InterfaceGateType::Hadamard,
1154                            vec![gate.qubits[1]],
1155                        ));
1156                        optimized_gates.push(InterfaceGate::new(
1157                            InterfaceGateType::CZ,
1158                            gate.qubits.clone(),
1159                        ));
1160                        optimized_gates.push(InterfaceGate::new(
1161                            InterfaceGateType::Hadamard,
1162                            vec![gate.qubits[1]],
1163                        ));
1164                    }
1165                }
1166                _ => {
1167                    optimized_gates.push(gate.clone());
1168                }
1169            }
1170        }
1171
1172        circuit.gates = optimized_gates;
1173        Ok(())
1174    }
1175
1176    /// Apply generic optimizations
1177    fn apply_generic_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1178        // Generic gate cancellation and commutation optimizations
1179        let mut optimized_gates = Vec::new();
1180        let mut i = 0;
1181
1182        while i < circuit.gates.len() {
1183            let gate = &circuit.gates[i];
1184
1185            // Look for gate cancellations (e.g., X followed by X)
1186            if i + 1 < circuit.gates.len() {
1187                let next_gate = &circuit.gates[i + 1];
1188
1189                if self.gates_cancel(gate, next_gate) {
1190                    // Skip both gates
1191                    i += 2;
1192                    continue;
1193                }
1194            }
1195
1196            optimized_gates.push(gate.clone());
1197            i += 1;
1198        }
1199
1200        circuit.gates = optimized_gates;
1201        Ok(())
1202    }
1203
1204    /// Check if two gates cancel each other
1205    fn gates_cancel(&self, gate1: &InterfaceGate, gate2: &InterfaceGate) -> bool {
1206        // Check if gates are on the same qubits and are inverses
1207        if gate1.qubits != gate2.qubits {
1208            return false;
1209        }
1210
1211        match (&gate1.gate_type, &gate2.gate_type) {
1212            (InterfaceGateType::PauliX, InterfaceGateType::PauliX) => true,
1213            (InterfaceGateType::PauliY, InterfaceGateType::PauliY) => true,
1214            (InterfaceGateType::PauliZ, InterfaceGateType::PauliZ) => true,
1215            (InterfaceGateType::Hadamard, InterfaceGateType::Hadamard) => true,
1216            (InterfaceGateType::CNOT, InterfaceGateType::CNOT) => true,
1217            (InterfaceGateType::CZ, InterfaceGateType::CZ) => true,
1218            _ => false,
1219        }
1220    }
1221
1222    /// Apply noise-aware optimizations
1223    fn apply_noise_aware_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1224        // Optimize circuit based on device noise characteristics
1225
1226        // Prioritize gates with lower error rates
1227        let mut gate_priorities = HashMap::new();
1228        for (gate_name, error_rate) in &self.device_metrics.gate_error_rates {
1229            gate_priorities.insert(gate_name.clone(), 1.0 / (1.0 + error_rate));
1230        }
1231
1232        // Minimize high-error operations where possible
1233        let mut optimized_gates = Vec::new();
1234
1235        for gate in &circuit.gates {
1236            let gate_name = format!("{:?}", gate.gate_type);
1237            let error_rate = self
1238                .device_metrics
1239                .gate_error_rates
1240                .get(&gate_name)
1241                .unwrap_or(&1e-2);
1242
1243            // If error rate is too high, try to decompose into lower-error gates
1244            if *error_rate > 1e-2 {
1245                // Attempt decomposition (simplified)
1246                match gate.gate_type {
1247                    InterfaceGateType::Toffoli => {
1248                        // Decompose Toffoli as it typically has high error rates
1249                        let decomposed =
1250                            self.decompose_or_route_gate(&gate.gate_type, &gate.qubits)?;
1251                        optimized_gates.extend(decomposed);
1252                    }
1253                    _ => {
1254                        optimized_gates.push(gate.clone());
1255                    }
1256                }
1257            } else {
1258                optimized_gates.push(gate.clone());
1259            }
1260        }
1261
1262        circuit.gates = optimized_gates;
1263        Ok(())
1264    }
1265
1266    /// Calculate optimization statistics
1267    fn calculate_optimization_stats(
1268        &self,
1269        original: &InterfaceCircuit,
1270        optimized: &InterfaceCircuit,
1271    ) -> OptimizationStats {
1272        OptimizationStats {
1273            gates_eliminated: original.gates.len().saturating_sub(optimized.gates.len()),
1274            gates_added_for_routing: optimized.gates.len().saturating_sub(original.gates.len()),
1275            swap_gates_inserted: optimized
1276                .gates
1277                .iter()
1278                .filter(|gate| matches!(gate.gate_type, InterfaceGateType::SWAP))
1279                .count(),
1280            depth_reduction: (self.calculate_circuit_depth(original) as f64
1281                - self.calculate_circuit_depth(optimized) as f64)
1282                / self.calculate_circuit_depth(original) as f64,
1283            error_rate_improvement: {
1284                let original_error = self.estimate_error_rate(original).unwrap_or(1e-2);
1285                let optimized_error = self.estimate_error_rate(optimized).unwrap_or(1e-2);
1286                (original_error - optimized_error) / original_error
1287            },
1288            optimization_passes: 1,
1289        }
1290    }
1291
1292    /// Estimate error rate for optimized circuit
1293    fn estimate_error_rate(&self, circuit: &InterfaceCircuit) -> Result<f64> {
1294        let mut total_error = 0.0;
1295
1296        for gate in &circuit.gates {
1297            let gate_name = format!("{:?}", gate.gate_type);
1298            let gate_error = self
1299                .device_metrics
1300                .gate_error_rates
1301                .get(&gate_name)
1302                .unwrap_or(&1e-3);
1303            total_error += gate_error;
1304        }
1305
1306        // Add measurement errors
1307        let measurement_error = self
1308            .device_metrics
1309            .measurement_error_rates
1310            .mean()
1311            .unwrap_or(1e-2);
1312        total_error += measurement_error * circuit.num_qubits as f64;
1313
1314        Ok(total_error)
1315    }
1316
1317    // Additional helper methods and implementations would go here...
1318    // Due to length constraints, I'm including the key methods above
1319
1320    /// Generate cache key for circuit
1321    fn generate_cache_key(&self, circuit: &InterfaceCircuit) -> String {
1322        format!(
1323            "{}_{}_{}_{:?}",
1324            circuit.num_qubits,
1325            circuit.gates.len(),
1326            self.calculate_circuit_depth(circuit),
1327            self.config.target_architecture
1328        )
1329    }
1330
1331    /// Update compilation statistics
1332    fn update_compilation_stats(
1333        &mut self,
1334        compilation_time_ms: u64,
1335        result: &HardwareOptimizedCircuit,
1336    ) {
1337        self.circuit_compiler.compilation_stats.total_compilations += 1;
1338
1339        let total_time = self
1340            .circuit_compiler
1341            .compilation_stats
1342            .avg_compilation_time_ms
1343            * (self.circuit_compiler.compilation_stats.total_compilations - 1) as f64
1344            + compilation_time_ms as f64;
1345
1346        self.circuit_compiler
1347            .compilation_stats
1348            .avg_compilation_time_ms =
1349            total_time / self.circuit_compiler.compilation_stats.total_compilations as f64;
1350    }
1351
1352    /// Select optimal ansatz pattern based on multiple criteria
1353    fn select_optimal_ansatz_pattern<'a>(
1354        &self,
1355        patterns: &'a [AnsatzPattern],
1356        target_expressivity: f64,
1357    ) -> Result<&'a AnsatzPattern> {
1358        // Score patterns based on multiple criteria
1359        let scored_patterns: Vec<_> = patterns.iter()
1360            .filter(|p| p.expressivity >= target_expressivity * 0.95) // Allow 5% tolerance
1361            .map(|pattern| {
1362                let connectivity_score = self.calculate_connectivity_compatibility(pattern);
1363                let gate_fidelity_score = self.calculate_gate_fidelity_score(pattern);
1364                let depth_efficiency_score = 1.0 / (pattern.gate_sequence.len() as f64 + 1.0);
1365
1366                // Weighted combination of scores
1367                let total_score =
1368                    pattern.hardware_efficiency * 0.4 +
1369                    connectivity_score * 0.3 +
1370                    gate_fidelity_score * 0.2 +
1371                    depth_efficiency_score * 0.1;
1372
1373                (pattern, total_score)
1374            })
1375            .collect();
1376
1377        scored_patterns
1378            .iter()
1379            .max_by(|a, b| a.1.partial_cmp(&b.1).unwrap())
1380            .map(|(pattern, _)| *pattern)
1381            .ok_or_else(|| {
1382                SimulatorError::InvalidConfiguration("No suitable ansatz pattern found".to_string())
1383            })
1384    }
1385
1386    fn add_ansatz_layer(
1387        &self,
1388        circuit: &mut InterfaceCircuit,
1389        pattern: &AnsatzPattern,
1390        layer: usize,
1391    ) -> Result<()> {
1392        // Add ansatz layer based on pattern with hardware-aware qubit mapping
1393        let mut qubit_pairs_used = HashSet::new();
1394
1395        for (i, gate_type) in pattern.gate_sequence.iter().enumerate() {
1396            match gate_type {
1397                // Single-qubit gates
1398                InterfaceGateType::Hadamard
1399                | InterfaceGateType::PauliX
1400                | InterfaceGateType::PauliY
1401                | InterfaceGateType::PauliZ
1402                | InterfaceGateType::RX(_)
1403                | InterfaceGateType::RY(_)
1404                | InterfaceGateType::RZ(_) => {
1405                    let qubit = (layer + i) % circuit.num_qubits;
1406                    circuit.add_gate(InterfaceGate::new(gate_type.clone(), vec![qubit]));
1407                }
1408                // Two-qubit gates - use hardware-aware mapping
1409                InterfaceGateType::CNOT | InterfaceGateType::CZ | InterfaceGateType::CPhase(_) => {
1410                    let control = (layer + i) % circuit.num_qubits;
1411                    let target = (control + 1) % circuit.num_qubits;
1412
1413                    // Check if this qubit pair is available based on connectivity
1414                    let pair = if control < target {
1415                        (control, target)
1416                    } else {
1417                        (target, control)
1418                    };
1419
1420                    if !qubit_pairs_used.contains(&pair)
1421                        && self.is_qubit_pair_connected(control, target)
1422                    {
1423                        circuit
1424                            .add_gate(InterfaceGate::new(gate_type.clone(), vec![control, target]));
1425                        qubit_pairs_used.insert(pair);
1426                    } else {
1427                        // Find alternative connected pair
1428                        if let Some((alt_control, alt_target)) = self
1429                            .find_available_connected_pair(circuit.num_qubits, &qubit_pairs_used)
1430                        {
1431                            circuit.add_gate(InterfaceGate::new(
1432                                gate_type.clone(),
1433                                vec![alt_control, alt_target],
1434                            ));
1435                            qubit_pairs_used
1436                                .insert((alt_control.min(alt_target), alt_control.max(alt_target)));
1437                        }
1438                    }
1439                }
1440                _ => {
1441                    // Default single-qubit mapping for other gates
1442                    let qubit = (layer + i) % circuit.num_qubits;
1443                    circuit.add_gate(InterfaceGate::new(gate_type.clone(), vec![qubit]));
1444                }
1445            }
1446        }
1447        Ok(())
1448    }
1449
1450    fn analyze_training_data(&self, _data: &Array2<f64>) -> Result<TrainingDataAnalysis> {
1451        Ok(TrainingDataAnalysis::default())
1452    }
1453
1454    fn optimize_batch_size(&self, _analysis: &TrainingDataAnalysis) -> Result<usize> {
1455        Ok(32) // Default batch size
1456    }
1457
1458    fn should_enable_mixed_precision(&self) -> Result<bool> {
1459        Ok(true) // Enable for noise-aware optimization
1460    }
1461
1462    fn start_performance_monitoring(&mut self) -> Result<()> {
1463        // Initialize performance monitoring
1464        self.performance_monitor
1465            .timestamps
1466            .push_back(Instant::now());
1467        Ok(())
1468    }
1469
1470    pub fn check_adaptation_trigger(
1471        trigger: &AdaptationTrigger,
1472        performance: &PerformanceMetrics,
1473    ) -> Result<bool> {
1474        match trigger {
1475            AdaptationTrigger::ErrorRateThreshold(threshold) => {
1476                Ok(performance.error_rate > *threshold)
1477            }
1478            AdaptationTrigger::ExecutionTimeThreshold(threshold) => {
1479                Ok(performance.avg_execution_time > *threshold)
1480            }
1481            _ => Ok(false), // Simplified for demonstration
1482        }
1483    }
1484
1485    fn determine_adaptation_action(
1486        trigger: &AdaptationTrigger,
1487        _performance: &PerformanceMetrics,
1488    ) -> Result<AdaptationAction> {
1489        match trigger {
1490            AdaptationTrigger::ErrorRateThreshold(_) => Ok(AdaptationAction::RecompileCircuit(
1491                HardwareOptimizationLevel::Aggressive,
1492            )),
1493            AdaptationTrigger::ExecutionTimeThreshold(_) => {
1494                Ok(AdaptationAction::AdjustBatchSize(16))
1495            }
1496            _ => Ok(AdaptationAction::RecompileCircuit(
1497                HardwareOptimizationLevel::Balanced,
1498            )),
1499        }
1500    }
1501
1502    /// Calculate how well a pattern matches device connectivity
1503    fn calculate_connectivity_compatibility(&self, pattern: &AnsatzPattern) -> f64 {
1504        let mut compatibility_score = 0.0;
1505        let total_connections = pattern.gate_sequence.len();
1506
1507        if total_connections == 0 {
1508            return 1.0;
1509        }
1510
1511        // Check each gate in the pattern against device connectivity
1512        for gate_type in &pattern.gate_sequence {
1513            let gate_compatibility = match gate_type {
1514                InterfaceGateType::CNOT | InterfaceGateType::CZ | InterfaceGateType::CPhase(_) => {
1515                    // Two-qubit gates need good connectivity
1516                    if self.device_metrics.connectivity_graph.len() > 2 {
1517                        0.8 // Assume good connectivity for now
1518                    } else {
1519                        0.3
1520                    }
1521                }
1522                _ => 1.0, // Single-qubit gates are always compatible
1523            };
1524            compatibility_score += gate_compatibility;
1525        }
1526
1527        compatibility_score / total_connections as f64
1528    }
1529
1530    /// Calculate gate fidelity score for a pattern
1531    fn calculate_gate_fidelity_score(&self, pattern: &AnsatzPattern) -> f64 {
1532        let mut total_fidelity = 0.0;
1533        let total_gates = pattern.gate_sequence.len();
1534
1535        if total_gates == 0 {
1536            return 1.0;
1537        }
1538
1539        for gate_type in &pattern.gate_sequence {
1540            let gate_name = format!("{:?}", gate_type);
1541            let error_rate = self
1542                .device_metrics
1543                .gate_error_rates
1544                .get(&gate_name)
1545                .unwrap_or(&1e-3);
1546            let fidelity = 1.0 - error_rate;
1547            total_fidelity += fidelity;
1548        }
1549
1550        total_fidelity / total_gates as f64
1551    }
1552
1553    /// Check if two qubits are connected in the device topology
1554    fn is_qubit_pair_connected(&self, qubit1: usize, qubit2: usize) -> bool {
1555        // Check connectivity graph for direct connection
1556        if let Some(&is_connected) = self.device_metrics.connectivity_graph.get((qubit1, qubit2)) {
1557            is_connected
1558        } else {
1559            // If no connectivity info, assume linear connectivity for adjacent qubits
1560            (qubit1 as i32 - qubit2 as i32).abs() == 1
1561        }
1562    }
1563
1564    /// Find an available connected qubit pair
1565    fn find_available_connected_pair(
1566        &self,
1567        num_qubits: usize,
1568        used_pairs: &HashSet<(usize, usize)>,
1569    ) -> Option<(usize, usize)> {
1570        for i in 0..num_qubits {
1571            for j in (i + 1)..num_qubits {
1572                let pair = (i, j);
1573                if !used_pairs.contains(&pair) && self.is_qubit_pair_connected(i, j) {
1574                    return Some((i, j));
1575                }
1576            }
1577        }
1578        None
1579    }
1580
1581    /// Analyze circuit (public version)
1582    pub fn analyze_circuit_public(&self, circuit: &InterfaceCircuit) -> Result<CircuitAnalysis> {
1583        self.analyze_circuit(circuit)
1584    }
1585
1586    /// Optimize qubit mapping (public version)
1587    pub fn optimize_qubit_mapping_public(
1588        &self,
1589        circuit: &InterfaceCircuit,
1590        analysis: &CircuitAnalysis,
1591    ) -> Result<HashMap<usize, usize>> {
1592        self.optimize_qubit_mapping(circuit, analysis)
1593    }
1594
1595    /// Check if gate is directly executable (public version)
1596    pub fn is_gate_directly_executable_public(
1597        &self,
1598        gate_type: &InterfaceGateType,
1599        qubits: &[usize],
1600    ) -> bool {
1601        self.is_gate_directly_executable(gate_type, qubits)
1602    }
1603
1604    /// Decompose or route gate (public version)
1605    pub fn decompose_or_route_gate_public(
1606        &self,
1607        gate_type: &InterfaceGateType,
1608        qubits: &[usize],
1609    ) -> Result<Vec<InterfaceGate>> {
1610        self.decompose_or_route_gate(gate_type, qubits)
1611    }
1612
1613    /// Apply IBM optimizations (public version)
1614    pub fn apply_ibm_optimizations_public(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1615        self.apply_ibm_optimizations(circuit)
1616    }
1617
1618    /// Check if gates cancel (public version)
1619    pub fn gates_cancel_public(&self, gate1: &InterfaceGate, gate2: &InterfaceGate) -> bool {
1620        self.gates_cancel(gate1, gate2)
1621    }
1622
1623    /// Estimate error rate (public version)
1624    pub fn estimate_error_rate_public(&self, circuit: &InterfaceCircuit) -> Result<f64> {
1625        self.estimate_error_rate(circuit)
1626    }
1627
1628    /// Start performance monitoring (public version)
1629    pub fn start_performance_monitoring_public(&mut self) -> Result<()> {
1630        self.start_performance_monitoring()
1631    }
1632
1633    /// Get performance monitor reference
1634    pub fn get_performance_monitor(&self) -> &PerformanceMonitoringData {
1635        &self.performance_monitor
1636    }
1637}
1638
1639/// Circuit analysis result
1640#[derive(Debug, Clone, Default)]
1641pub struct CircuitAnalysis {
1642    pub gate_counts: HashMap<String, usize>,
1643    pub two_qubit_gates: Vec<Vec<usize>>,
1644    pub parameter_count: usize,
1645    pub circuit_depth: usize,
1646    pub entanglement_measure: f64,
1647}
1648
1649/// Training data analysis
1650#[derive(Debug, Clone, Default)]
1651pub struct TrainingDataAnalysis {
1652    pub data_size: usize,
1653    pub feature_dimension: usize,
1654    pub complexity_measure: f64,
1655}
1656
1657impl HardwareAwareAnsatz {
1658    fn new(_config: &HardwareAwareConfig) -> Result<Self> {
1659        Ok(Self {
1660            architecture_patterns: HashMap::new(),
1661            entangling_patterns: Vec::new(),
1662            parameter_efficiency: 0.8,
1663            hardware_cost: 1.0,
1664        })
1665    }
1666}
1667
1668impl HardwareCircuitCompiler {
1669    fn new() -> Self {
1670        Self {
1671            compilation_cache: HashMap::new(),
1672            routing_algorithms: vec![RoutingAlgorithm::ShortestPath],
1673            optimization_passes: vec![OptimizationPass::GateCancellation],
1674            compilation_stats: CompilationStatistics::default(),
1675        }
1676    }
1677}
1678
1679impl DynamicAdaptationStrategy {
1680    fn new(_config: &HardwareAwareConfig) -> Result<Self> {
1681        Ok(Self {
1682            triggers: vec![
1683                AdaptationTrigger::ErrorRateThreshold(0.1),
1684                AdaptationTrigger::ExecutionTimeThreshold(Duration::from_secs(10)),
1685            ],
1686            actions: Vec::new(),
1687            history: Vec::new(),
1688            current_state: AdaptationState::default(),
1689        })
1690    }
1691}
1692
1693/// Benchmark function for hardware-aware QML optimization
1694pub fn benchmark_hardware_aware_qml() -> Result<()> {
1695    println!("Benchmarking Hardware-Aware QML Optimization...");
1696
1697    let config = HardwareAwareConfig {
1698        target_architecture: HardwareArchitecture::IBMQuantum,
1699        ..Default::default()
1700    };
1701
1702    let mut optimizer = HardwareAwareQMLOptimizer::new(config)?;
1703
1704    // Create test circuit
1705    let mut circuit = InterfaceCircuit::new(4, 0);
1706    circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![0]));
1707    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![0, 1]));
1708    circuit.add_gate(InterfaceGate::new(InterfaceGateType::RY(0.5), vec![2]));
1709    circuit.add_gate(InterfaceGate::new(
1710        InterfaceGateType::Toffoli,
1711        vec![0, 1, 2],
1712    ));
1713
1714    let start_time = Instant::now();
1715
1716    // Optimize circuit for hardware
1717    let optimized_result = optimizer.optimize_qml_circuit(&circuit, None)?;
1718
1719    let duration = start_time.elapsed();
1720
1721    println!("✅ Hardware-Aware QML Optimization Results:");
1722    println!("   Original Gates: {}", circuit.gates.len());
1723    println!(
1724        "   Optimized Gates: {}",
1725        optimized_result.circuit.gates.len()
1726    );
1727    println!(
1728        "   Gate Count Optimization: {:?}",
1729        optimized_result.gate_count_optimization
1730    );
1731    println!(
1732        "   Depth Optimization: {:?}",
1733        optimized_result.depth_optimization
1734    );
1735    println!(
1736        "   Expected Error Rate: {:.6}",
1737        optimized_result.expected_error_rate
1738    );
1739    println!(
1740        "   Gates Eliminated: {}",
1741        optimized_result.optimization_stats.gates_eliminated
1742    );
1743    println!(
1744        "   SWAP Gates Added: {}",
1745        optimized_result.optimization_stats.swap_gates_inserted
1746    );
1747    println!(
1748        "   Compilation Time: {}ms",
1749        optimized_result.compilation_time_ms
1750    );
1751    println!("   Total Optimization Time: {:.2}ms", duration.as_millis());
1752
1753    // Test hardware-efficient ansatz generation
1754    let ansatz_circuit = optimizer.generate_hardware_efficient_ansatz(4, 3, 0.8)?;
1755    println!("   Generated Ansatz Gates: {}", ansatz_circuit.gates.len());
1756
1757    Ok(())
1758}
1759
1760#[cfg(test)]
1761mod tests {
1762    use super::*;
1763
1764    #[test]
1765    fn test_hardware_aware_optimizer_creation() {
1766        let config = HardwareAwareConfig::default();
1767        let optimizer = HardwareAwareQMLOptimizer::new(config);
1768        assert!(optimizer.is_ok());
1769    }
1770
1771    #[test]
1772    fn test_circuit_analysis() {
1773        let config = HardwareAwareConfig::default();
1774        let optimizer = HardwareAwareQMLOptimizer::new(config).unwrap();
1775
1776        let mut circuit = InterfaceCircuit::new(2, 0);
1777        circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![0]));
1778        circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![0, 1]));
1779
1780        let analysis = optimizer.analyze_circuit(&circuit);
1781        assert!(analysis.is_ok());
1782
1783        let analysis = analysis.unwrap();
1784        assert_eq!(analysis.two_qubit_gates.len(), 1);
1785        assert!(analysis.gate_counts.contains_key("Hadamard"));
1786    }
1787
1788    #[test]
1789    fn test_qubit_mapping_optimization() {
1790        let config = HardwareAwareConfig::default();
1791        let optimizer = HardwareAwareQMLOptimizer::new(config).unwrap();
1792
1793        let circuit = InterfaceCircuit::new(4, 0);
1794        let analysis = optimizer.analyze_circuit(&circuit).unwrap();
1795        let mapping = optimizer.optimize_qubit_mapping(&circuit, &analysis);
1796
1797        assert!(mapping.is_ok());
1798        let mapping = mapping.unwrap();
1799        assert_eq!(mapping.len(), 4);
1800    }
1801
1802    #[test]
1803    fn test_hardware_specific_optimizations() {
1804        let config = HardwareAwareConfig {
1805            target_architecture: HardwareArchitecture::IBMQuantum,
1806            ..Default::default()
1807        };
1808        let optimizer = HardwareAwareQMLOptimizer::new(config).unwrap();
1809
1810        let mut circuit = InterfaceCircuit::new(2, 0);
1811        circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(0.1), vec![0]));
1812        circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(0.2), vec![0]));
1813
1814        let original_gates = circuit.gates.len();
1815        optimizer.apply_ibm_optimizations(&mut circuit).unwrap();
1816
1817        // Should fuse consecutive RZ gates
1818        assert!(circuit.gates.len() <= original_gates);
1819    }
1820
1821    #[test]
1822    fn test_gate_cancellation() {
1823        let config = HardwareAwareConfig::default();
1824        let optimizer = HardwareAwareQMLOptimizer::new(config).unwrap();
1825
1826        let gate1 = InterfaceGate::new(InterfaceGateType::PauliX, vec![0]);
1827        let gate2 = InterfaceGate::new(InterfaceGateType::PauliX, vec![0]);
1828
1829        assert!(optimizer.gates_cancel(&gate1, &gate2));
1830
1831        let gate3 = InterfaceGate::new(InterfaceGateType::PauliY, vec![0]);
1832        assert!(!optimizer.gates_cancel(&gate1, &gate3));
1833    }
1834
1835    #[test]
1836    fn test_error_rate_estimation() {
1837        let config = HardwareAwareConfig::default();
1838        let optimizer = HardwareAwareQMLOptimizer::new(config).unwrap();
1839
1840        let mut circuit = InterfaceCircuit::new(2, 0);
1841        circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![0, 1]));
1842
1843        let error_rate = optimizer.estimate_error_rate(&circuit);
1844        assert!(error_rate.is_ok());
1845        assert!(error_rate.unwrap() > 0.0);
1846    }
1847
1848    #[test]
1849    fn test_cross_device_compatibility() {
1850        let config = HardwareAwareConfig::default();
1851        let optimizer = HardwareAwareQMLOptimizer::new(config).unwrap();
1852
1853        let compatibility = optimizer.get_cross_device_compatibility(
1854            HardwareArchitecture::IBMQuantum,
1855            HardwareArchitecture::GoogleQuantumAI,
1856        );
1857
1858        assert!(compatibility >= 0.0 && compatibility <= 1.0);
1859    }
1860}
quantrs2_sim/hardware_aware_qml.rs

quantrs2_sim/
hardware_aware_qml.rs
