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Advanced Variational Quantum Algorithms (VQA) Framework
This module provides a comprehensive implementation of state-of-the-art variational quantum algorithms including VQE, QAOA, VQA with advanced optimizers, and novel variational approaches for quantum machine learning and optimization.
Structs§
- AdvancedVQA
Trainer - Advanced Variational Quantum Algorithm trainer
- Bayesian
Model - Bayesian optimization model
- Finite
Difference Gradient - Example gradient calculators Finite difference gradient calculator
- Hamiltonian
Term - Hamiltonian terms
- Ising
Cost Function - Example implementations of cost functions Ising model cost function for QAOA
- Mixer
Hamiltonian - Mixer Hamiltonian for QAOA
- Optimizer
State - Optimizer internal state
- Parameter
Shift Gradient - Parameter shift rule gradient calculator
- Problem
Hamiltonian - Problem Hamiltonian for optimization problems
- VQAConfig
- VQA configuration
- VQAResult
- VQA optimization result
- VQATrainer
State - VQA trainer state
- VQATraining
Stats - VQA training statistics
- Warm
Restart Config - Warm restart configuration
Enums§
- Acquisition
Function - Acquisition functions for Bayesian optimization
- Advanced
Optimizer Type - Advanced VQA optimizer types
- Compression
Method - Compression methods for tensor networks
- Growth
Criterion - Growth criteria for adaptive ansatz
- Mixer
Type - Mixer types for QAOA
- Network
Connectivity - Network connectivity patterns
- Optimization
Problem Type - Optimization problem types
- Quantum
Activation - Quantum activation functions
- Tensor
Topology - Tensor network topologies
- Variational
Ansatz - Variational ansatz types
Traits§
- Cost
Function - Cost function trait
- Gradient
Calculator - Gradient calculation methods
Functions§
- benchmark_
advanced_ vqa - Benchmark function for VQA performance