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Quantum-Inspired Classical Algorithms Framework
This module provides a comprehensive implementation of quantum-inspired classical algorithms that leverage quantum mechanical principles, quantum physics concepts, and quantum computation techniques while running on classical computers. These algorithms often provide advantages over traditional classical algorithms by incorporating quantum-inspired heuristics.
Structs§
- Algorithm
Config - Algorithm-specific configuration
- Benchmarking
Config - Benchmarking configuration
- Benchmarking
Results - Benchmarking results
- Community
Detection Params - Community detection parameters
- Comparison
Stats - Performance comparison statistics
- Convergence
Analysis - Convergence analysis
- Execution
Stats - Execution statistics
- Graph
Config - Graph algorithm configuration
- Graph
Metrics - Graph metrics
- Graph
Result - Graph algorithm result
- Linalg
Config - Linear algebra configuration
- Linalg
Result - Linear algebra result
- MLConfig
- Machine learning configuration
- MLTraining
Result - Machine learning training result
- Network
Architecture - Network architecture configuration
- Optimization
Config - Optimization configuration
- Optimization
Result - Optimization result
- Performance
Analysis Config - Performance analysis configuration
- Quantum
Advantage Metrics - Quantum advantage metrics
- Quantum
Inspired Config - Quantum-inspired classical algorithms configuration
- Quantum
Inspired Framework - Main quantum-inspired classical algorithms framework
- Quantum
Inspired State - Framework state
- Quantum
Inspired Stats - Framework statistics
- Quantum
Inspired Utils - Utility functions for quantum-inspired algorithms
- Quantum
Parameters - Quantum-inspired parameters
- Quantum
Walk Params - Quantum walk parameters
- Runtime
Stats - Runtime statistics
- Sample
Statistics - Sample statistics
- Sampling
Config - Sampling configuration
- Sampling
Result - Sampling result
- Statistical
Analysis - Statistical analysis results
- Tensor
Network Config - Tensor network configuration
- Training
Config - Training configuration
- Walk
Statistics - Walk statistics
- Wave
Function Config - Wave function configuration
Enums§
- Activation
Function - Activation functions
- Algorithm
Category - Categories of quantum-inspired algorithms
- Constraint
Method - Constraint handling methods
- Contraction
Method - Contraction methods
- Graph
Algorithm - Quantum-inspired graph algorithms
- Linalg
Algorithm - Quantum-inspired linear algebra algorithms
- MLAlgorithm
- Quantum-inspired machine learning algorithms
- Objective
Function - Objective function types
- Optimization
Algorithm - Quantum-inspired optimization algorithms
- Optimizer
Type - Optimizer types
- Proposal
Distribution - Proposal distributions
- Sampling
Algorithm - Quantum-inspired sampling algorithms
- Temperature
Schedule - Temperature schedule for simulated annealing-like algorithms
- Tensor
Topology - Tensor network topologies
- Wave
Function Type - Wave function types
Functions§
- benchmark_
quantum_ inspired_ algorithms - Benchmark quantum-inspired algorithms