quantrs2_device/cloud/
mod.rs

1//! Quantum Cloud Resource Management System
2//!
3//! This module provides comprehensive cloud resource management for quantum computing across
4//! multiple providers (IBM Quantum, AWS Braket, Azure Quantum, Google Quantum AI) with
5//! intelligent allocation, cost optimization, multi-provider coordination, and advanced
6//! analytics using SciRS2's optimization and machine learning capabilities.
7
8use std::collections::{BTreeMap, HashMap, HashSet, VecDeque};
9use std::sync::{Arc, Mutex, RwLock};
10use std::time::{Duration, Instant, SystemTime, UNIX_EPOCH};
11
12use quantrs2_circuit::prelude::*;
13use quantrs2_core::{
14    error::{QuantRS2Error, QuantRS2Result},
15    gate::GateOp,
16    qubit::QubitId,
17};
18
19// SciRS2 dependencies for advanced cloud analytics and optimization
20#[cfg(feature = "scirs2")]
21use scirs2_linalg::{det, eig, inv, matrix_norm, prelude::*, svd, LinalgError, LinalgResult};
22#[cfg(feature = "scirs2")]
23use scirs2_optimize::{minimize, OptimizeResult};
24use scirs2_stats::ttest::Alternative;
25#[cfg(feature = "scirs2")]
26use scirs2_stats::{corrcoef, distributions, mean, pearsonr, spearmanr, std, var};
27
28// Fallback implementations when SciRS2 is not available
29#[cfg(not(feature = "scirs2"))]
30mod fallback_scirs2 {
31    use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
32
33    pub fn mean(_data: &ArrayView1<f64>) -> Result<f64, String> {
34        Ok(0.0)
35    }
36    pub fn std(_data: &ArrayView1<f64>, _ddof: i32) -> Result<f64, String> {
37        Ok(1.0)
38    }
39    pub fn pearsonr(
40        _x: &ArrayView1<f64>,
41        _y: &ArrayView1<f64>,
42        _alt: &str,
43    ) -> Result<(f64, f64), String> {
44        Ok((0.0, 0.5))
45    }
46
47    pub struct OptimizeResult {
48        pub x: Array1<f64>,
49        pub fun: f64,
50        pub success: bool,
51        pub nit: usize,
52        pub nfev: usize,
53        pub message: String,
54    }
55
56    pub fn minimize(
57        _func: fn(&Array1<f64>) -> f64,
58        _x0: &Array1<f64>,
59        _method: &str,
60    ) -> Result<OptimizeResult, String> {
61        Ok(OptimizeResult {
62            x: Array1::zeros(2),
63            fun: 0.0,
64            success: true,
65            nit: 0,
66            nfev: 0,
67            message: "Fallback optimization".to_string(),
68        })
69    }
70
71    pub fn genetic_algorithm(
72        _func: fn(&Array1<f64>) -> f64,
73        _bounds: &[(f64, f64)],
74    ) -> Result<OptimizeResult, String> {
75        Ok(OptimizeResult {
76            x: Array1::zeros(2),
77            fun: 0.0,
78            success: true,
79            nit: 0,
80            nfev: 0,
81            message: "Fallback genetic algorithm".to_string(),
82        })
83    }
84
85    pub fn random_forest(_x: &Array2<f64>, _y: &Array1<f64>) -> Result<String, String> {
86        Ok("fallback_model".to_string())
87    }
88}
89
90#[cfg(not(feature = "scirs2"))]
91use fallback_scirs2::*;
92
93#[cfg(feature = "security")]
94use chrono::{DateTime, Duration as ChronoDuration, Utc};
95use scirs2_core::ndarray::{s, Array1, Array2, Array3, Array4, ArrayView1, ArrayView2, Axis};
96use scirs2_core::random::prelude::*;
97use scirs2_core::Complex64;
98use serde::{Deserialize, Serialize};
99use tokio::sync::{broadcast, mpsc, RwLock as TokioRwLock, Semaphore};
100use uuid::Uuid;
101
102use crate::{
103    backend_traits::{query_backend_capabilities, BackendCapabilities},
104    calibration::{CalibrationManager, DeviceCalibration},
105    integrated_device_manager::{
106        DeviceInfo, IntegratedExecutionResult, IntegratedQuantumDeviceManager,
107    },
108    job_scheduling::{JobConfig, JobPriority, JobStatus, QuantumJob, QuantumJobScheduler},
109    noise_model::CalibrationNoiseModel,
110    topology::HardwareTopology,
111    CircuitExecutor, CircuitResult, DeviceError, DeviceResult, QuantumDevice,
112};
113
114// Module declarations
115pub mod allocation;
116pub mod cost_estimation;
117pub mod cost_management;
118pub mod monitoring;
119pub mod orchestration;
120pub mod provider_migration;
121pub mod provider_optimizations;
122pub mod providers;
123
124// Re-exports for public API
125pub use allocation::*;
126pub use cost_estimation::*;
127pub use cost_management::*;
128pub use monitoring::*;
129pub use orchestration::*;
130pub use provider_migration::*;
131pub use provider_optimizations::*;
132pub use providers::*;
133
134// Re-export specific configuration types
135pub use orchestration::load_balancing::CloudLoadBalancingConfig;
136pub use orchestration::performance::AutoScalingConfig;
137pub use orchestration::performance::CloudPerformanceConfig;
138pub use orchestration::CloudSecurityConfig;
139
140/// Configuration for Quantum Cloud Resource Management System
141#[derive(Debug, Clone, Serialize, Deserialize)]
142pub struct QuantumCloudConfig {
143    /// Multi-provider configuration
144    pub provider_config: MultiProviderConfig,
145    /// Resource allocation and optimization
146    pub allocation_config: ResourceAllocationConfig,
147    /// Cost management and optimization
148    pub cost_config: CostManagementConfig,
149    /// Performance optimization settings
150    pub performance_config: CloudPerformanceConfig,
151    /// Load balancing and failover
152    pub load_balancing_config: CloudLoadBalancingConfig,
153    /// Security and compliance
154    pub security_config: CloudSecurityConfig,
155    /// Monitoring and analytics
156    pub monitoring_config: CloudMonitoringConfig,
157    /// Machine learning and prediction
158    pub ml_config: CloudMLConfig,
159    /// Auto-scaling and elasticity
160    pub scaling_config: AutoScalingConfig,
161    /// Budget and quota management
162    pub budget_config: BudgetConfig,
163}
164
165/// Machine learning configuration for cloud optimization
166#[derive(Debug, Clone, Serialize, Deserialize)]
167pub struct CloudMLConfig {
168    /// Enable ML-driven optimization
169    pub enable_ml_optimization: bool,
170    /// ML models for resource optimization
171    pub optimization_models: Vec<String>,
172    /// Predictive analytics for resource planning
173    pub predictive_analytics: bool,
174    /// Automated decision making threshold
175    pub automated_decision_threshold: f64,
176    /// Model training configuration
177    pub model_training_enabled: bool,
178    /// Feature engineering configuration
179    pub feature_engineering_enabled: bool,
180}
181
182impl Default for CloudMLConfig {
183    fn default() -> Self {
184        Self {
185            enable_ml_optimization: false,
186            optimization_models: vec![],
187            predictive_analytics: false,
188            automated_decision_threshold: 0.8,
189            model_training_enabled: false,
190            feature_engineering_enabled: false,
191        }
192    }
193}
194
195// TODO: Add the main implementation structs and functions that were in the original file
196// This would include the QuantumCloudManager struct and its implementation
197// For now, this refactoring focuses on organizing the massive configuration types
quantrs2_device/cloud/mod.rs

quantrs2_device/cloud/
mod.rs
