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: 30000, // 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    pub fn get_cross_device_compatibility(
615        &self,
616        source_arch: HardwareArchitecture,
617        target_arch: HardwareArchitecture,
618    ) -> f64 {
619        self.compatibility_matrix
620            .get(&(source_arch, target_arch))
621            .copied()
622            .unwrap_or(0.5) // Default neutral compatibility
623    }
624
625    /// Initialize device metrics
626    fn initialize_device_metrics(&mut self, config: &HardwareAwareConfig) -> Result<()> {
627        match config.target_architecture {
628            HardwareArchitecture::IBMQuantum => {
629                self.initialize_ibm_metrics()?;
630            }
631            HardwareArchitecture::GoogleQuantumAI => {
632                self.initialize_google_metrics()?;
633            }
634            HardwareArchitecture::Rigetti => {
635                self.initialize_rigetti_metrics()?;
636            }
637            HardwareArchitecture::IonQ => {
638                self.initialize_ionq_metrics()?;
639            }
640            HardwareArchitecture::Simulator => {
641                self.initialize_simulator_metrics()?;
642            }
643            _ => {
644                self.initialize_generic_metrics()?;
645            }
646        }
647
648        Ok(())
649    }
650
651    /// Initialize IBM Quantum metrics
652    fn initialize_ibm_metrics(&mut self) -> Result<()> {
653        let mut gate_error_rates = HashMap::new();
654        gate_error_rates.insert("CNOT".to_string(), 5e-3);
655        gate_error_rates.insert("RZ".to_string(), 1e-4);
656        gate_error_rates.insert("SX".to_string(), 2e-4);
657        gate_error_rates.insert("X".to_string(), 2e-4);
658
659        self.device_metrics.gate_error_rates = gate_error_rates;
660        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![1e-2; 127]); // IBM's largest systems
661        self.device_metrics.coherence_times = Array2::from_shape_vec((127, 2), vec![100e-6; 254])?; // T1, T2
662
663        let mut gate_times = HashMap::new();
664        gate_times.insert("CNOT".to_string(), Duration::from_nanos(300));
665        gate_times.insert("RZ".to_string(), Duration::from_nanos(0));
666        gate_times.insert("SX".to_string(), Duration::from_nanos(35));
667
668        self.device_metrics.gate_times = gate_times;
669
670        Ok(())
671    }
672
673    /// Initialize Google Quantum AI metrics
674    fn initialize_google_metrics(&mut self) -> Result<()> {
675        let mut gate_error_rates = HashMap::new();
676        gate_error_rates.insert("CZ".to_string(), 6e-3);
677        gate_error_rates.insert("RZ".to_string(), 1e-4);
678        gate_error_rates.insert("RX".to_string(), 1e-4);
679        gate_error_rates.insert("RY".to_string(), 1e-4);
680
681        self.device_metrics.gate_error_rates = gate_error_rates;
682        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![2e-2; 70]); // Sycamore
683        self.device_metrics.coherence_times = Array2::from_shape_vec((70, 2), vec![50e-6; 140])?;
684
685        let mut gate_times = HashMap::new();
686        gate_times.insert("CZ".to_string(), Duration::from_nanos(20));
687        gate_times.insert("RZ".to_string(), Duration::from_nanos(25));
688
689        self.device_metrics.gate_times = gate_times;
690
691        Ok(())
692    }
693
694    /// Initialize Rigetti metrics
695    fn initialize_rigetti_metrics(&mut self) -> Result<()> {
696        let mut gate_error_rates = HashMap::new();
697        gate_error_rates.insert("CZ".to_string(), 8e-3);
698        gate_error_rates.insert("RZ".to_string(), 5e-4);
699        gate_error_rates.insert("RX".to_string(), 5e-4);
700
701        self.device_metrics.gate_error_rates = gate_error_rates;
702        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![3e-2; 32]);
703        self.device_metrics.coherence_times = Array2::from_shape_vec((32, 2), vec![30e-6; 64])?;
704
705        Ok(())
706    }
707
708    /// Initialize IonQ metrics
709    fn initialize_ionq_metrics(&mut self) -> Result<()> {
710        let mut gate_error_rates = HashMap::new();
711        gate_error_rates.insert("CNOT".to_string(), 1e-2);
712        gate_error_rates.insert("RZ".to_string(), 1e-4);
713        gate_error_rates.insert("RX".to_string(), 1e-4);
714        gate_error_rates.insert("RY".to_string(), 1e-4);
715
716        self.device_metrics.gate_error_rates = gate_error_rates;
717        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![1e-3; 32]);
718        self.device_metrics.coherence_times = Array2::from_shape_vec((32, 2), vec![10e3; 64])?; // Much longer for ions
719
720        let mut gate_times = HashMap::new();
721        gate_times.insert("CNOT".to_string(), Duration::from_micros(100));
722        gate_times.insert("RZ".to_string(), Duration::from_micros(10));
723
724        self.device_metrics.gate_times = gate_times;
725
726        Ok(())
727    }
728
729    /// Initialize simulator metrics
730    fn initialize_simulator_metrics(&mut self) -> Result<()> {
731        let mut gate_error_rates = HashMap::new();
732        gate_error_rates.insert("CNOT".to_string(), 1e-6);
733        gate_error_rates.insert("RZ".to_string(), 1e-7);
734        gate_error_rates.insert("RX".to_string(), 1e-7);
735        gate_error_rates.insert("RY".to_string(), 1e-7);
736
737        self.device_metrics.gate_error_rates = gate_error_rates;
738        self.device_metrics.measurement_error_rates = Array1::from_elem(100, 1e-6);
739        self.device_metrics.coherence_times = Array2::from_elem((100, 2), std::f64::INFINITY);
740
741        Ok(())
742    }
743
744    /// Initialize generic metrics
745    fn initialize_generic_metrics(&mut self) -> Result<()> {
746        let mut gate_error_rates = HashMap::new();
747        gate_error_rates.insert("CNOT".to_string(), 1e-2);
748        gate_error_rates.insert("RZ".to_string(), 1e-3);
749        gate_error_rates.insert("RX".to_string(), 1e-3);
750        gate_error_rates.insert("RY".to_string(), 1e-3);
751
752        self.device_metrics.gate_error_rates = gate_error_rates;
753        self.device_metrics.measurement_error_rates = Array1::from_vec(vec![1e-2; 50]);
754        self.device_metrics.coherence_times = Array2::from_shape_vec((50, 2), vec![100e-6; 100])?;
755
756        Ok(())
757    }
758
759    /// Initialize ansatz patterns for different architectures
760    fn initialize_ansatz_patterns(&mut self, config: &HardwareAwareConfig) -> Result<()> {
761        // This would be a comprehensive initialization of architecture-specific patterns
762        // For demonstration, we'll create a few key patterns
763
764        let mut architecture_patterns = HashMap::new();
765
766        // IBM patterns (focusing on CNOT and single-qubit gates)
767        let ibm_patterns = vec![AnsatzPattern {
768            name: "IBM_Efficient_RY_CNOT".to_string(),
769            gate_sequence: vec![
770                InterfaceGateType::RY(0.0),
771                InterfaceGateType::CNOT,
772                InterfaceGateType::RY(0.0),
773            ],
774            connectivity_requirements: vec![(0, 1)],
775            parameter_count: 2,
776            expressivity: 0.8,
777            hardware_efficiency: 0.9,
778        }];
779        architecture_patterns.insert(HardwareArchitecture::IBMQuantum, ibm_patterns);
780
781        // Google patterns (focusing on CZ and sqrt(X) gates)
782        let google_patterns = vec![AnsatzPattern {
783            name: "Google_CZ_Pattern".to_string(),
784            gate_sequence: vec![
785                InterfaceGateType::RZ(0.0),
786                InterfaceGateType::CZ,
787                InterfaceGateType::RZ(0.0),
788            ],
789            connectivity_requirements: vec![(0, 1)],
790            parameter_count: 2,
791            expressivity: 0.85,
792            hardware_efficiency: 0.95,
793        }];
794        architecture_patterns.insert(HardwareArchitecture::GoogleQuantumAI, google_patterns);
795
796        self.ansatz_generator.architecture_patterns = architecture_patterns;
797
798        Ok(())
799    }
800
801    /// Analyze circuit characteristics for optimization
802    fn analyze_circuit(&self, circuit: &InterfaceCircuit) -> Result<CircuitAnalysis> {
803        let mut gate_counts = HashMap::new();
804        let mut two_qubit_gates = Vec::new();
805        let mut parameter_count = 0;
806
807        for gate in &circuit.gates {
808            let gate_name = format!("{:?}", gate.gate_type);
809            *gate_counts.entry(gate_name).or_insert(0) += 1;
810
811            if gate.qubits.len() > 1 {
812                two_qubit_gates.push(gate.qubits.clone());
813            }
814
815            match gate.gate_type {
816                InterfaceGateType::RX(_) | InterfaceGateType::RY(_) | InterfaceGateType::RZ(_) => {
817                    parameter_count += 1;
818                }
819                _ => {}
820            }
821        }
822
823        Ok(CircuitAnalysis {
824            gate_counts,
825            two_qubit_gates,
826            parameter_count,
827            circuit_depth: self.calculate_circuit_depth(circuit),
828            entanglement_measure: self.calculate_entanglement_measure(circuit),
829        })
830    }
831
832    /// Calculate circuit depth
833    fn calculate_circuit_depth(&self, circuit: &InterfaceCircuit) -> usize {
834        let mut qubit_depths = vec![0; circuit.num_qubits];
835
836        for gate in &circuit.gates {
837            let max_depth = gate
838                .qubits
839                .iter()
840                .filter(|&&q| q < qubit_depths.len())
841                .map(|&q| qubit_depths[q])
842                .max()
843                .unwrap_or(0);
844
845            for &qubit in &gate.qubits {
846                if qubit < qubit_depths.len() {
847                    qubit_depths[qubit] = max_depth + 1;
848                }
849            }
850        }
851
852        qubit_depths.into_iter().max().unwrap_or(0)
853    }
854
855    /// Calculate entanglement measure (simplified)
856    fn calculate_entanglement_measure(&self, circuit: &InterfaceCircuit) -> f64 {
857        let two_qubit_gate_count = circuit
858            .gates
859            .iter()
860            .filter(|gate| gate.qubits.len() > 1)
861            .count();
862
863        two_qubit_gate_count as f64 / circuit.gates.len() as f64
864    }
865
866    /// Optimize qubit mapping for hardware topology
867    fn optimize_qubit_mapping(
868        &self,
869        circuit: &InterfaceCircuit,
870        analysis: &CircuitAnalysis,
871    ) -> Result<HashMap<usize, usize>> {
872        // Simplified qubit mapping optimization
873        // In practice, this would use sophisticated graph algorithms
874
875        let mut mapping = HashMap::new();
876
877        // For demonstration, use identity mapping
878        for i in 0..circuit.num_qubits {
879            mapping.insert(i, i);
880        }
881
882        // Try to optimize based on two-qubit gate patterns
883        if !analysis.two_qubit_gates.is_empty() {
884            // Use a greedy approach to map frequently interacting qubits to well-connected regions
885            let mut qubit_interactions = HashMap::new();
886
887            for gate_qubits in &analysis.two_qubit_gates {
888                if gate_qubits.len() == 2 {
889                    let pair = (
890                        gate_qubits[0].min(gate_qubits[1]),
891                        gate_qubits[0].max(gate_qubits[1]),
892                    );
893                    *qubit_interactions.entry(pair).or_insert(0) += 1;
894                }
895            }
896
897            // Sort by interaction frequency
898            let mut sorted_interactions: Vec<_> = qubit_interactions.into_iter().collect();
899            sorted_interactions.sort_by(|a, b| b.1.cmp(&a.1));
900
901            // Map most frequently interacting qubits to best connected physical qubits
902            for (i, ((logical_q1, logical_q2), _count)) in sorted_interactions.iter().enumerate() {
903                if i < 10 {
904                    // Limit optimization to top 10 interactions
905                    mapping.insert(*logical_q1, i * 2);
906                    mapping.insert(*logical_q2, i * 2 + 1);
907                }
908            }
909        }
910
911        Ok(mapping)
912    }
913
914    /// Apply connectivity optimization
915    fn apply_connectivity_optimization(
916        &self,
917        circuit: &InterfaceCircuit,
918        qubit_mapping: &HashMap<usize, usize>,
919    ) -> Result<InterfaceCircuit> {
920        let mut optimized_circuit = InterfaceCircuit::new(circuit.num_qubits, 0);
921
922        for gate in &circuit.gates {
923            let mapped_qubits: Vec<usize> = gate
924                .qubits
925                .iter()
926                .map(|&q| qubit_mapping.get(&q).copied().unwrap_or(q))
927                .collect();
928
929            // Check if gate is directly executable on hardware
930            if self.is_gate_directly_executable(&gate.gate_type, &mapped_qubits) {
931                let mapped_gate = InterfaceGate::new(gate.gate_type.clone(), mapped_qubits);
932                optimized_circuit.add_gate(mapped_gate);
933            } else {
934                // Decompose or route the gate
935                let decomposed_gates =
936                    self.decompose_or_route_gate(&gate.gate_type, &mapped_qubits)?;
937                for decomposed_gate in decomposed_gates {
938                    optimized_circuit.add_gate(decomposed_gate);
939                }
940            }
941        }
942
943        Ok(optimized_circuit)
944    }
945
946    /// Check if gate is directly executable on hardware
947    const fn is_gate_directly_executable(
948        &self,
949        gate_type: &InterfaceGateType,
950        qubits: &[usize],
951    ) -> bool {
952        match self.config.target_architecture {
953            HardwareArchitecture::IBMQuantum => {
954                matches!(
955                    gate_type,
956                    InterfaceGateType::RZ(_)
957                        | InterfaceGateType::RX(_)
958                        | InterfaceGateType::RY(_)
959                        | InterfaceGateType::CNOT
960                        | InterfaceGateType::PauliX
961                        | InterfaceGateType::PauliY
962                        | InterfaceGateType::PauliZ
963                )
964            }
965            HardwareArchitecture::GoogleQuantumAI => {
966                matches!(
967                    gate_type,
968                    InterfaceGateType::RZ(_)
969                        | InterfaceGateType::RX(_)
970                        | InterfaceGateType::RY(_)
971                        | InterfaceGateType::CZ
972                        | InterfaceGateType::PauliX
973                        | InterfaceGateType::PauliY
974                        | InterfaceGateType::PauliZ
975                )
976            }
977            _ => true, // Assume simulator supports all gates
978        }
979    }
980
981    /// Decompose or route gate for hardware execution
982    fn decompose_or_route_gate(
983        &self,
984        gate_type: &InterfaceGateType,
985        qubits: &[usize],
986    ) -> Result<Vec<InterfaceGate>> {
987        let mut decomposed_gates = Vec::new();
988
989        match gate_type {
990            InterfaceGateType::Toffoli => {
991                // Decompose Toffoli into hardware-native gates
992                if qubits.len() == 3 {
993                    // Simplified Toffoli decomposition
994                    decomposed_gates.push(InterfaceGate::new(
995                        InterfaceGateType::Hadamard,
996                        vec![qubits[2]],
997                    ));
998                    decomposed_gates.push(InterfaceGate::new(
999                        InterfaceGateType::CNOT,
1000                        vec![qubits[1], qubits[2]],
1001                    ));
1002                    decomposed_gates.push(InterfaceGate::new(
1003                        InterfaceGateType::RZ(-PI / 4.0),
1004                        vec![qubits[2]],
1005                    ));
1006                    decomposed_gates.push(InterfaceGate::new(
1007                        InterfaceGateType::CNOT,
1008                        vec![qubits[0], qubits[2]],
1009                    ));
1010                    decomposed_gates.push(InterfaceGate::new(
1011                        InterfaceGateType::RZ(PI / 4.0),
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[1]],
1029                    ));
1030                    decomposed_gates.push(InterfaceGate::new(
1031                        InterfaceGateType::RZ(PI / 4.0),
1032                        vec![qubits[2]],
1033                    ));
1034                    decomposed_gates.push(InterfaceGate::new(
1035                        InterfaceGateType::Hadamard,
1036                        vec![qubits[2]],
1037                    ));
1038                    decomposed_gates.push(InterfaceGate::new(
1039                        InterfaceGateType::CNOT,
1040                        vec![qubits[0], qubits[1]],
1041                    ));
1042                    decomposed_gates.push(InterfaceGate::new(
1043                        InterfaceGateType::RZ(PI / 4.0),
1044                        vec![qubits[0]],
1045                    ));
1046                    decomposed_gates.push(InterfaceGate::new(
1047                        InterfaceGateType::RZ(-PI / 4.0),
1048                        vec![qubits[1]],
1049                    ));
1050                    decomposed_gates.push(InterfaceGate::new(
1051                        InterfaceGateType::CNOT,
1052                        vec![qubits[0], qubits[1]],
1053                    ));
1054                }
1055            }
1056            _ => {
1057                // For unsupported gates, pass through as-is (would add SWAP routing in practice)
1058                decomposed_gates.push(InterfaceGate::new(gate_type.clone(), qubits.to_vec()));
1059            }
1060        }
1061
1062        Ok(decomposed_gates)
1063    }
1064
1065    /// Apply hardware-specific optimizations
1066    fn apply_hardware_specific_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1067        match self.config.target_architecture {
1068            HardwareArchitecture::IBMQuantum => {
1069                self.apply_ibm_optimizations(circuit)?;
1070            }
1071            HardwareArchitecture::GoogleQuantumAI => {
1072                self.apply_google_optimizations(circuit)?;
1073            }
1074            _ => {
1075                // Generic optimizations
1076                self.apply_generic_optimizations(circuit)?;
1077            }
1078        }
1079
1080        Ok(())
1081    }
1082
1083    /// Apply IBM-specific optimizations
1084    fn apply_ibm_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1085        // IBM-specific gate fusion and optimization patterns
1086        // For example, RZ gate optimization since RZ is virtual on IBM hardware
1087
1088        let mut optimized_gates = Vec::new();
1089        let mut i = 0;
1090
1091        while i < circuit.gates.len() {
1092            let gate = &circuit.gates[i];
1093
1094            // Look for consecutive RZ gates on the same qubit
1095            if matches!(gate.gate_type, InterfaceGateType::RZ(_)) && gate.qubits.len() == 1 {
1096                let qubit = gate.qubits[0];
1097                let mut total_angle = if let InterfaceGateType::RZ(angle) = gate.gate_type {
1098                    angle
1099                } else {
1100                    0.0
1101                };
1102                let mut j = i + 1;
1103
1104                // Fuse consecutive RZ gates
1105                while j < circuit.gates.len() {
1106                    let next_gate = &circuit.gates[j];
1107                    if matches!(next_gate.gate_type, InterfaceGateType::RZ(_))
1108                        && next_gate.qubits.len() == 1
1109                        && next_gate.qubits[0] == qubit
1110                    {
1111                        if let InterfaceGateType::RZ(angle) = next_gate.gate_type {
1112                            total_angle += angle;
1113                        }
1114                        j += 1;
1115                    } else {
1116                        break;
1117                    }
1118                }
1119
1120                // Add fused RZ gate if non-zero
1121                if total_angle.abs() > 1e-10 {
1122                    optimized_gates.push(InterfaceGate::new(
1123                        InterfaceGateType::RZ(total_angle),
1124                        vec![qubit],
1125                    ));
1126                }
1127
1128                i = j;
1129            } else {
1130                optimized_gates.push(gate.clone());
1131                i += 1;
1132            }
1133        }
1134
1135        circuit.gates = optimized_gates;
1136        Ok(())
1137    }
1138
1139    /// Apply Google-specific optimizations
1140    fn apply_google_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1141        // Google-specific optimizations for their gate set
1142        // Focus on CZ gate optimizations and sqrt(X) decompositions
1143
1144        let mut optimized_gates = Vec::new();
1145
1146        for gate in &circuit.gates {
1147            match gate.gate_type {
1148                InterfaceGateType::CNOT => {
1149                    // Convert CNOT to CZ + Hadamards for Google hardware
1150                    if gate.qubits.len() == 2 {
1151                        optimized_gates.push(InterfaceGate::new(
1152                            InterfaceGateType::Hadamard,
1153                            vec![gate.qubits[1]],
1154                        ));
1155                        optimized_gates.push(InterfaceGate::new(
1156                            InterfaceGateType::CZ,
1157                            gate.qubits.clone(),
1158                        ));
1159                        optimized_gates.push(InterfaceGate::new(
1160                            InterfaceGateType::Hadamard,
1161                            vec![gate.qubits[1]],
1162                        ));
1163                    }
1164                }
1165                _ => {
1166                    optimized_gates.push(gate.clone());
1167                }
1168            }
1169        }
1170
1171        circuit.gates = optimized_gates;
1172        Ok(())
1173    }
1174
1175    /// Apply generic optimizations
1176    fn apply_generic_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1177        // Generic gate cancellation and commutation optimizations
1178        let mut optimized_gates = Vec::new();
1179        let mut i = 0;
1180
1181        while i < circuit.gates.len() {
1182            let gate = &circuit.gates[i];
1183
1184            // Look for gate cancellations (e.g., X followed by X)
1185            if i + 1 < circuit.gates.len() {
1186                let next_gate = &circuit.gates[i + 1];
1187
1188                if self.gates_cancel(gate, next_gate) {
1189                    // Skip both gates
1190                    i += 2;
1191                    continue;
1192                }
1193            }
1194
1195            optimized_gates.push(gate.clone());
1196            i += 1;
1197        }
1198
1199        circuit.gates = optimized_gates;
1200        Ok(())
1201    }
1202
1203    /// Check if two gates cancel each other
1204    fn gates_cancel(&self, gate1: &InterfaceGate, gate2: &InterfaceGate) -> bool {
1205        // Check if gates are on the same qubits and are inverses
1206        if gate1.qubits != gate2.qubits {
1207            return false;
1208        }
1209
1210        match (&gate1.gate_type, &gate2.gate_type) {
1211            (InterfaceGateType::PauliX, InterfaceGateType::PauliX) => true,
1212            (InterfaceGateType::PauliY, InterfaceGateType::PauliY) => true,
1213            (InterfaceGateType::PauliZ, InterfaceGateType::PauliZ) => true,
1214            (InterfaceGateType::Hadamard, InterfaceGateType::Hadamard) => true,
1215            (InterfaceGateType::CNOT, InterfaceGateType::CNOT) => true,
1216            (InterfaceGateType::CZ, InterfaceGateType::CZ) => true,
1217            _ => false,
1218        }
1219    }
1220
1221    /// Apply noise-aware optimizations
1222    fn apply_noise_aware_optimizations(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1223        // Optimize circuit based on device noise characteristics
1224
1225        // Prioritize gates with lower error rates
1226        let mut gate_priorities = HashMap::new();
1227        for (gate_name, error_rate) in &self.device_metrics.gate_error_rates {
1228            gate_priorities.insert(gate_name.clone(), 1.0 / (1.0 + error_rate));
1229        }
1230
1231        // Minimize high-error operations where possible
1232        let mut optimized_gates = Vec::new();
1233
1234        for gate in &circuit.gates {
1235            let gate_name = format!("{:?}", gate.gate_type);
1236            let error_rate = self
1237                .device_metrics
1238                .gate_error_rates
1239                .get(&gate_name)
1240                .unwrap_or(&1e-2);
1241
1242            // If error rate is too high, try to decompose into lower-error gates
1243            if *error_rate > 1e-2 {
1244                // Attempt decomposition (simplified)
1245                match gate.gate_type {
1246                    InterfaceGateType::Toffoli => {
1247                        // Decompose Toffoli as it typically has high error rates
1248                        let decomposed =
1249                            self.decompose_or_route_gate(&gate.gate_type, &gate.qubits)?;
1250                        optimized_gates.extend(decomposed);
1251                    }
1252                    _ => {
1253                        optimized_gates.push(gate.clone());
1254                    }
1255                }
1256            } else {
1257                optimized_gates.push(gate.clone());
1258            }
1259        }
1260
1261        circuit.gates = optimized_gates;
1262        Ok(())
1263    }
1264
1265    /// Calculate optimization statistics
1266    fn calculate_optimization_stats(
1267        &self,
1268        original: &InterfaceCircuit,
1269        optimized: &InterfaceCircuit,
1270    ) -> OptimizationStats {
1271        OptimizationStats {
1272            gates_eliminated: original.gates.len().saturating_sub(optimized.gates.len()),
1273            gates_added_for_routing: optimized.gates.len().saturating_sub(original.gates.len()),
1274            swap_gates_inserted: optimized
1275                .gates
1276                .iter()
1277                .filter(|gate| matches!(gate.gate_type, InterfaceGateType::SWAP))
1278                .count(),
1279            depth_reduction: (self.calculate_circuit_depth(original) as f64
1280                - self.calculate_circuit_depth(optimized) as f64)
1281                / self.calculate_circuit_depth(original) as f64,
1282            error_rate_improvement: {
1283                let original_error = self.estimate_error_rate(original).unwrap_or(1e-2);
1284                let optimized_error = self.estimate_error_rate(optimized).unwrap_or(1e-2);
1285                (original_error - optimized_error) / original_error
1286            },
1287            optimization_passes: 1,
1288        }
1289    }
1290
1291    /// Estimate error rate for optimized circuit
1292    fn estimate_error_rate(&self, circuit: &InterfaceCircuit) -> Result<f64> {
1293        let mut total_error = 0.0;
1294
1295        for gate in &circuit.gates {
1296            let gate_name = format!("{:?}", gate.gate_type);
1297            let gate_error = self
1298                .device_metrics
1299                .gate_error_rates
1300                .get(&gate_name)
1301                .unwrap_or(&1e-3);
1302            total_error += gate_error;
1303        }
1304
1305        // Add measurement errors
1306        let measurement_error = self
1307            .device_metrics
1308            .measurement_error_rates
1309            .mean()
1310            .unwrap_or(1e-2);
1311        total_error += measurement_error * circuit.num_qubits as f64;
1312
1313        Ok(total_error)
1314    }
1315
1316    // Additional helper methods and implementations would go here...
1317    // Due to length constraints, I'm including the key methods above
1318
1319    /// Generate cache key for circuit
1320    fn generate_cache_key(&self, circuit: &InterfaceCircuit) -> String {
1321        format!(
1322            "{}_{}_{}_{:?}",
1323            circuit.num_qubits,
1324            circuit.gates.len(),
1325            self.calculate_circuit_depth(circuit),
1326            self.config.target_architecture
1327        )
1328    }
1329
1330    /// Update compilation statistics
1331    fn update_compilation_stats(
1332        &mut self,
1333        compilation_time_ms: u64,
1334        result: &HardwareOptimizedCircuit,
1335    ) {
1336        self.circuit_compiler.compilation_stats.total_compilations += 1;
1337
1338        let total_time = self
1339            .circuit_compiler
1340            .compilation_stats
1341            .avg_compilation_time_ms
1342            .mul_add(
1343                (self.circuit_compiler.compilation_stats.total_compilations - 1) as f64,
1344                compilation_time_ms as f64,
1345            );
1346
1347        self.circuit_compiler
1348            .compilation_stats
1349            .avg_compilation_time_ms =
1350            total_time / self.circuit_compiler.compilation_stats.total_compilations as f64;
1351    }
1352
1353    /// Select optimal ansatz pattern based on multiple criteria
1354    fn select_optimal_ansatz_pattern<'a>(
1355        &self,
1356        patterns: &'a [AnsatzPattern],
1357        target_expressivity: f64,
1358    ) -> Result<&'a AnsatzPattern> {
1359        // Score patterns based on multiple criteria
1360        let scored_patterns: Vec<_> = patterns.iter()
1361            .filter(|p| p.expressivity >= target_expressivity * 0.95) // Allow 5% tolerance
1362            .map(|pattern| {
1363                let connectivity_score = self.calculate_connectivity_compatibility(pattern);
1364                let gate_fidelity_score = self.calculate_gate_fidelity_score(pattern);
1365                let depth_efficiency_score = 1.0 / (pattern.gate_sequence.len() as f64 + 1.0);
1366
1367                // Weighted combination of scores
1368                let total_score =
1369                    gate_fidelity_score.mul_add(0.2, pattern.hardware_efficiency.mul_add(0.4, connectivity_score * 0.3)) +
1370                    depth_efficiency_score * 0.1;
1371
1372                (pattern, total_score)
1373            })
1374            .collect();
1375
1376        scored_patterns
1377            .iter()
1378            .max_by(|a, b| a.1.partial_cmp(&b.1).unwrap())
1379            .map(|(pattern, _)| *pattern)
1380            .ok_or_else(|| {
1381                SimulatorError::InvalidConfiguration("No suitable ansatz pattern found".to_string())
1382            })
1383    }
1384
1385    fn add_ansatz_layer(
1386        &self,
1387        circuit: &mut InterfaceCircuit,
1388        pattern: &AnsatzPattern,
1389        layer: usize,
1390    ) -> Result<()> {
1391        // Add ansatz layer based on pattern with hardware-aware qubit mapping
1392        let mut qubit_pairs_used = HashSet::new();
1393
1394        for (i, gate_type) in pattern.gate_sequence.iter().enumerate() {
1395            match gate_type {
1396                // Single-qubit gates
1397                InterfaceGateType::Hadamard
1398                | InterfaceGateType::PauliX
1399                | InterfaceGateType::PauliY
1400                | InterfaceGateType::PauliZ
1401                | InterfaceGateType::RX(_)
1402                | InterfaceGateType::RY(_)
1403                | InterfaceGateType::RZ(_) => {
1404                    let qubit = (layer + i) % circuit.num_qubits;
1405                    circuit.add_gate(InterfaceGate::new(gate_type.clone(), vec![qubit]));
1406                }
1407                // Two-qubit gates - use hardware-aware mapping
1408                InterfaceGateType::CNOT | InterfaceGateType::CZ | InterfaceGateType::CPhase(_) => {
1409                    let control = (layer + i) % circuit.num_qubits;
1410                    let target = (control + 1) % circuit.num_qubits;
1411
1412                    // Check if this qubit pair is available based on connectivity
1413                    let pair = if control < target {
1414                        (control, target)
1415                    } else {
1416                        (target, control)
1417                    };
1418
1419                    if !qubit_pairs_used.contains(&pair)
1420                        && self.is_qubit_pair_connected(control, target)
1421                    {
1422                        circuit
1423                            .add_gate(InterfaceGate::new(gate_type.clone(), vec![control, target]));
1424                        qubit_pairs_used.insert(pair);
1425                    } else {
1426                        // Find alternative connected pair
1427                        if let Some((alt_control, alt_target)) = self
1428                            .find_available_connected_pair(circuit.num_qubits, &qubit_pairs_used)
1429                        {
1430                            circuit.add_gate(InterfaceGate::new(
1431                                gate_type.clone(),
1432                                vec![alt_control, alt_target],
1433                            ));
1434                            qubit_pairs_used
1435                                .insert((alt_control.min(alt_target), alt_control.max(alt_target)));
1436                        }
1437                    }
1438                }
1439                _ => {
1440                    // Default single-qubit mapping for other gates
1441                    let qubit = (layer + i) % circuit.num_qubits;
1442                    circuit.add_gate(InterfaceGate::new(gate_type.clone(), vec![qubit]));
1443                }
1444            }
1445        }
1446        Ok(())
1447    }
1448
1449    fn analyze_training_data(&self, _data: &Array2<f64>) -> Result<TrainingDataAnalysis> {
1450        Ok(TrainingDataAnalysis::default())
1451    }
1452
1453    const fn optimize_batch_size(&self, _analysis: &TrainingDataAnalysis) -> Result<usize> {
1454        Ok(32) // Default batch size
1455    }
1456
1457    const fn should_enable_mixed_precision(&self) -> Result<bool> {
1458        Ok(true) // Enable for noise-aware optimization
1459    }
1460
1461    fn start_performance_monitoring(&mut self) -> Result<()> {
1462        // Initialize performance monitoring
1463        self.performance_monitor
1464            .timestamps
1465            .push_back(Instant::now());
1466        Ok(())
1467    }
1468
1469    pub fn check_adaptation_trigger(
1470        trigger: &AdaptationTrigger,
1471        performance: &PerformanceMetrics,
1472    ) -> Result<bool> {
1473        match trigger {
1474            AdaptationTrigger::ErrorRateThreshold(threshold) => {
1475                Ok(performance.error_rate > *threshold)
1476            }
1477            AdaptationTrigger::ExecutionTimeThreshold(threshold) => {
1478                Ok(performance.avg_execution_time > *threshold)
1479            }
1480            _ => Ok(false), // Simplified for demonstration
1481        }
1482    }
1483
1484    const fn determine_adaptation_action(
1485        trigger: &AdaptationTrigger,
1486        _performance: &PerformanceMetrics,
1487    ) -> Result<AdaptationAction> {
1488        match trigger {
1489            AdaptationTrigger::ErrorRateThreshold(_) => Ok(AdaptationAction::RecompileCircuit(
1490                HardwareOptimizationLevel::Aggressive,
1491            )),
1492            AdaptationTrigger::ExecutionTimeThreshold(_) => {
1493                Ok(AdaptationAction::AdjustBatchSize(16))
1494            }
1495            _ => Ok(AdaptationAction::RecompileCircuit(
1496                HardwareOptimizationLevel::Balanced,
1497            )),
1498        }
1499    }
1500
1501    /// Calculate how well a pattern matches device connectivity
1502    fn calculate_connectivity_compatibility(&self, pattern: &AnsatzPattern) -> f64 {
1503        let mut compatibility_score = 0.0;
1504        let total_connections = pattern.gate_sequence.len();
1505
1506        if total_connections == 0 {
1507            return 1.0;
1508        }
1509
1510        // Check each gate in the pattern against device connectivity
1511        for gate_type in &pattern.gate_sequence {
1512            let gate_compatibility = match gate_type {
1513                InterfaceGateType::CNOT | InterfaceGateType::CZ | InterfaceGateType::CPhase(_) => {
1514                    // Two-qubit gates need good connectivity
1515                    if self.device_metrics.connectivity_graph.len() > 2 {
1516                        0.8 // Assume good connectivity for now
1517                    } else {
1518                        0.3
1519                    }
1520                }
1521                _ => 1.0, // Single-qubit gates are always compatible
1522            };
1523            compatibility_score += gate_compatibility;
1524        }
1525
1526        compatibility_score / total_connections as f64
1527    }
1528
1529    /// Calculate gate fidelity score for a pattern
1530    fn calculate_gate_fidelity_score(&self, pattern: &AnsatzPattern) -> f64 {
1531        let mut total_fidelity = 0.0;
1532        let total_gates = pattern.gate_sequence.len();
1533
1534        if total_gates == 0 {
1535            return 1.0;
1536        }
1537
1538        for gate_type in &pattern.gate_sequence {
1539            let gate_name = format!("{gate_type:?}");
1540            let error_rate = self
1541                .device_metrics
1542                .gate_error_rates
1543                .get(&gate_name)
1544                .unwrap_or(&1e-3);
1545            let fidelity = 1.0 - error_rate;
1546            total_fidelity += fidelity;
1547        }
1548
1549        total_fidelity / total_gates as f64
1550    }
1551
1552    /// Check if two qubits are connected in the device topology
1553    fn is_qubit_pair_connected(&self, qubit1: usize, qubit2: usize) -> bool {
1554        // Check connectivity graph for direct connection
1555        if let Some(&is_connected) = self.device_metrics.connectivity_graph.get((qubit1, qubit2)) {
1556            is_connected
1557        } else {
1558            // If no connectivity info, assume linear connectivity for adjacent qubits
1559            (qubit1 as i32 - qubit2 as i32).abs() == 1
1560        }
1561    }
1562
1563    /// Find an available connected qubit pair
1564    fn find_available_connected_pair(
1565        &self,
1566        num_qubits: usize,
1567        used_pairs: &HashSet<(usize, usize)>,
1568    ) -> Option<(usize, usize)> {
1569        for i in 0..num_qubits {
1570            for j in (i + 1)..num_qubits {
1571                let pair = (i, j);
1572                if !used_pairs.contains(&pair) && self.is_qubit_pair_connected(i, j) {
1573                    return Some((i, j));
1574                }
1575            }
1576        }
1577        None
1578    }
1579
1580    /// Analyze circuit (public version)
1581    pub fn analyze_circuit_public(&self, circuit: &InterfaceCircuit) -> Result<CircuitAnalysis> {
1582        self.analyze_circuit(circuit)
1583    }
1584
1585    /// Optimize qubit mapping (public version)
1586    pub fn optimize_qubit_mapping_public(
1587        &self,
1588        circuit: &InterfaceCircuit,
1589        analysis: &CircuitAnalysis,
1590    ) -> Result<HashMap<usize, usize>> {
1591        self.optimize_qubit_mapping(circuit, analysis)
1592    }
1593
1594    /// Check if gate is directly executable (public version)
1595    pub const fn is_gate_directly_executable_public(
1596        &self,
1597        gate_type: &InterfaceGateType,
1598        qubits: &[usize],
1599    ) -> bool {
1600        self.is_gate_directly_executable(gate_type, qubits)
1601    }
1602
1603    /// Decompose or route gate (public version)
1604    pub fn decompose_or_route_gate_public(
1605        &self,
1606        gate_type: &InterfaceGateType,
1607        qubits: &[usize],
1608    ) -> Result<Vec<InterfaceGate>> {
1609        self.decompose_or_route_gate(gate_type, qubits)
1610    }
1611
1612    /// Apply IBM optimizations (public version)
1613    pub fn apply_ibm_optimizations_public(&self, circuit: &mut InterfaceCircuit) -> Result<()> {
1614        self.apply_ibm_optimizations(circuit)
1615    }
1616
1617    /// Check if gates cancel (public version)
1618    pub fn gates_cancel_public(&self, gate1: &InterfaceGate, gate2: &InterfaceGate) -> bool {
1619        self.gates_cancel(gate1, gate2)
1620    }
1621
1622    /// Estimate error rate (public version)
1623    pub fn estimate_error_rate_public(&self, circuit: &InterfaceCircuit) -> Result<f64> {
1624        self.estimate_error_rate(circuit)
1625    }
1626
1627    /// Start performance monitoring (public version)
1628    pub fn start_performance_monitoring_public(&mut self) -> Result<()> {
1629        self.start_performance_monitoring()
1630    }
1631
1632    /// Get performance monitor reference
1633    pub const fn get_performance_monitor(&self) -> &PerformanceMonitoringData {
1634        &self.performance_monitor
1635    }
1636}
1637
1638/// Circuit analysis result
1639#[derive(Debug, Clone, Default)]
1640pub struct CircuitAnalysis {
1641    pub gate_counts: HashMap<String, usize>,
1642    pub two_qubit_gates: Vec<Vec<usize>>,
1643    pub parameter_count: usize,
1644    pub circuit_depth: usize,
1645    pub entanglement_measure: f64,
1646}
1647
1648/// Training data analysis
1649#[derive(Debug, Clone, Default)]
1650pub struct TrainingDataAnalysis {
1651    pub data_size: usize,
1652    pub feature_dimension: usize,
1653    pub complexity_measure: f64,
1654}
1655
1656impl HardwareAwareAnsatz {
1657    fn new(_config: &HardwareAwareConfig) -> Result<Self> {
1658        Ok(Self {
1659            architecture_patterns: HashMap::new(),
1660            entangling_patterns: Vec::new(),
1661            parameter_efficiency: 0.8,
1662            hardware_cost: 1.0,
1663        })
1664    }
1665}
1666
1667impl HardwareCircuitCompiler {
1668    fn new() -> Self {
1669        Self {
1670            compilation_cache: HashMap::new(),
1671            routing_algorithms: vec![RoutingAlgorithm::ShortestPath],
1672            optimization_passes: vec![OptimizationPass::GateCancellation],
1673            compilation_stats: CompilationStatistics::default(),
1674        }
1675    }
1676}
1677
1678impl DynamicAdaptationStrategy {
1679    fn new(_config: &HardwareAwareConfig) -> Result<Self> {
1680        Ok(Self {
1681            triggers: vec![
1682                AdaptationTrigger::ErrorRateThreshold(0.1),
1683                AdaptationTrigger::ExecutionTimeThreshold(Duration::from_secs(10)),
1684            ],
1685            actions: Vec::new(),
1686            history: Vec::new(),
1687            current_state: AdaptationState::default(),
1688        })
1689    }
1690}
1691
1692/// Benchmark function for hardware-aware QML optimization
1693pub fn benchmark_hardware_aware_qml() -> Result<()> {
1694    println!("Benchmarking Hardware-Aware QML Optimization...");
1695
1696    let config = HardwareAwareConfig {
1697        target_architecture: HardwareArchitecture::IBMQuantum,
1698        ..Default::default()
1699    };
1700
1701    let mut optimizer = HardwareAwareQMLOptimizer::new(config)?;
1702
1703    // Create test circuit
1704    let mut circuit = InterfaceCircuit::new(4, 0);
1705    circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![0]));
1706    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![0, 1]));
1707    circuit.add_gate(InterfaceGate::new(InterfaceGateType::RY(0.5), vec![2]));
1708    circuit.add_gate(InterfaceGate::new(
1709        InterfaceGateType::Toffoli,
1710        vec![0, 1, 2],
1711    ));
1712
1713    let start_time = Instant::now();
1714
1715    // Optimize circuit for hardware
1716    let optimized_result = optimizer.optimize_qml_circuit(&circuit, None)?;
1717
1718    let duration = start_time.elapsed();
1719
1720    println!("✅ Hardware-Aware QML Optimization Results:");
1721    println!("   Original Gates: {}", circuit.gates.len());
1722    println!(
1723        "   Optimized Gates: {}",
1724        optimized_result.circuit.gates.len()
1725    );
1726    println!(
1727        "   Gate Count Optimization: {:?}",
1728        optimized_result.gate_count_optimization
1729    );
1730    println!(
1731        "   Depth Optimization: {:?}",
1732        optimized_result.depth_optimization
1733    );
1734    println!(
1735        "   Expected Error Rate: {:.6}",
1736        optimized_result.expected_error_rate
1737    );
1738    println!(
1739        "   Gates Eliminated: {}",
1740        optimized_result.optimization_stats.gates_eliminated
1741    );
1742    println!(
1743        "   SWAP Gates Added: {}",
1744        optimized_result.optimization_stats.swap_gates_inserted
1745    );
1746    println!(
1747        "   Compilation Time: {}ms",
1748        optimized_result.compilation_time_ms
1749    );
1750    println!("   Total Optimization Time: {:.2}ms", duration.as_millis());
1751
1752    // Test hardware-efficient ansatz generation
1753    let ansatz_circuit = optimizer.generate_hardware_efficient_ansatz(4, 3, 0.8)?;
1754    println!("   Generated Ansatz Gates: {}", ansatz_circuit.gates.len());
1755
1756    Ok(())
1757}
1758
1759#[cfg(test)]
1760mod tests {
1761    use super::*;
1762
1763    #[test]
1764    fn test_hardware_aware_optimizer_creation() {
1765        let config = HardwareAwareConfig::default();
1766        let optimizer = HardwareAwareQMLOptimizer::new(config);
1767        assert!(optimizer.is_ok());
1768    }
1769
1770    #[test]
1771    fn test_circuit_analysis() {
1772        let config = HardwareAwareConfig::default();
1773        let optimizer = HardwareAwareQMLOptimizer::new(config).unwrap();
1774
1775        let mut circuit = InterfaceCircuit::new(2, 0);
1776        circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![0]));
1777        circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![0, 1]));
1778
1779        let analysis = optimizer.analyze_circuit(&circuit);
1780        assert!(analysis.is_ok());
1781
1782        let analysis = analysis.unwrap();
1783        assert_eq!(analysis.two_qubit_gates.len(), 1);
1784        assert!(analysis.gate_counts.contains_key("Hadamard"));
1785    }
1786
1787    #[test]
1788    fn test_qubit_mapping_optimization() {
1789        let config = HardwareAwareConfig::default();
1790        let optimizer = HardwareAwareQMLOptimizer::new(config).unwrap();
1791
1792        let circuit = InterfaceCircuit::new(4, 0);
1793        let analysis = optimizer.analyze_circuit(&circuit).unwrap();
1794        let mapping = optimizer.optimize_qubit_mapping(&circuit, &analysis);
1795
1796        assert!(mapping.is_ok());
1797        let mapping = mapping.unwrap();
1798        assert_eq!(mapping.len(), 4);
1799    }
1800
1801    #[test]
1802    fn test_hardware_specific_optimizations() {
1803        let config = HardwareAwareConfig {
1804            target_architecture: HardwareArchitecture::IBMQuantum,
1805            ..Default::default()
1806        };
1807        let optimizer = HardwareAwareQMLOptimizer::new(config).unwrap();
1808
1809        let mut circuit = InterfaceCircuit::new(2, 0);
1810        circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(0.1), vec![0]));
1811        circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(0.2), vec![0]));
1812
1813        let original_gates = circuit.gates.len();
1814        optimizer.apply_ibm_optimizations(&mut circuit).unwrap();
1815
1816        // Should fuse consecutive RZ gates
1817        assert!(circuit.gates.len() <= original_gates);
1818    }
1819
1820    #[test]
1821    fn test_gate_cancellation() {
1822        let config = HardwareAwareConfig::default();
1823        let optimizer = HardwareAwareQMLOptimizer::new(config).unwrap();
1824
1825        let gate1 = InterfaceGate::new(InterfaceGateType::PauliX, vec![0]);
1826        let gate2 = InterfaceGate::new(InterfaceGateType::PauliX, vec![0]);
1827
1828        assert!(optimizer.gates_cancel(&gate1, &gate2));
1829
1830        let gate3 = InterfaceGate::new(InterfaceGateType::PauliY, vec![0]);
1831        assert!(!optimizer.gates_cancel(&gate1, &gate3));
1832    }
1833
1834    #[test]
1835    fn test_error_rate_estimation() {
1836        let config = HardwareAwareConfig::default();
1837        let optimizer = HardwareAwareQMLOptimizer::new(config).unwrap();
1838
1839        let mut circuit = InterfaceCircuit::new(2, 0);
1840        circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![0, 1]));
1841
1842        let error_rate = optimizer.estimate_error_rate(&circuit);
1843        assert!(error_rate.is_ok());
1844        assert!(error_rate.unwrap() > 0.0);
1845    }
1846
1847    #[test]
1848    fn test_cross_device_compatibility() {
1849        let config = HardwareAwareConfig::default();
1850        let optimizer = HardwareAwareQMLOptimizer::new(config).unwrap();
1851
1852        let compatibility = optimizer.get_cross_device_compatibility(
1853            HardwareArchitecture::IBMQuantum,
1854            HardwareArchitecture::GoogleQuantumAI,
1855        );
1856
1857        assert!((0.0..=1.0).contains(&compatibility));
1858    }
1859}
quantrs2_sim/hardware_aware_qml.rs

quantrs2_sim/
hardware_aware_qml.rs
