Common infrastructure for variational quantum algorithms (VQE, QAOA, etc.).
This namespace provides reusable components that are shared between different variational quantum algorithms, reducing code duplication and ensuring consistent behavior across algorithms.
Key Features:
Design Principles:
Common infrastructure for variational quantum algorithms (VQE, QAOA, etc.). This namespace provides reusable components that are shared between different variational quantum algorithms, reducing code duplication and ensuring consistent behavior across algorithms. Key Features: - Generic objective function creation - Common optimization method dispatching - Shared algorithm structure templates - Common result analysis and processing - Parameter initialization strategies Design Principles: - Algorithm-agnostic: Works with any parameterized quantum circuit - Composable: Functions can be mixed and matched as needed - Consistent: Uniform interfaces and error handling - Extensible: Easy to add new optimization methods or analysis functions
(analyze-convergence-history optimization-result)Analyze convergence from optimization history for any variational algorithm.
This function analyzes the optimization trajectory to provide insights into convergence behavior, energy improvement, and optimization quality.
Parameters:
Returns: Map with convergence analysis
Analyze convergence from optimization history for any variational algorithm. This function analyzes the optimization trajectory to provide insights into convergence behavior, energy improvement, and optimization quality. Parameters: - optimization-result: Result map from optimization containing :history Returns: Map with convergence analysis
(analyze-optimization-convergence optimization-result)Analyze convergence properties of optimization results.
Parameters:
Returns: Map with convergence analysis
Analyze convergence properties of optimization results. Parameters: - optimization-result: Result map from optimization Returns: Map with convergence analysis
(analyze-parameter-sensitivity sensitivities)Analyze parameter sensitivity for variational algorithms.
This function identifies which parameters have the most impact on the objective function by computing normalized sensitivities and providing ranking.
Parameters:
Returns: Map with sensitivity analysis
Analyze parameter sensitivity for variational algorithms. This function identifies which parameters have the most impact on the objective function by computing normalized sensitivities and providing ranking. Parameters: - sensitivities: Vector of parameter sensitivities from landscape analysis Returns: Map with sensitivity analysis
(analyze-variational-landscape objective-fn
optimal-params
&
{:keys [perturbation-size]
:or {perturbation-size 0.01}})Analyze the energy landscape around optimal parameters for any variational algorithm.
This function performs parameter sensitivity analysis by perturbing each parameter and measuring the energy change. It provides insights into which parameters have the most impact on the objective function.
Parameters:
Returns: Map with landscape analysis including gradient norms and parameter sensitivities
Analyze the energy landscape around optimal parameters for any variational algorithm. This function performs parameter sensitivity analysis by perturbing each parameter and measuring the energy change. It provides insights into which parameters have the most impact on the objective function. Parameters: - objective-fn: Objective function to analyze - optimal-params: Optimal parameters found by optimization - perturbation-size: Size of parameter perturbations for analysis (default: 0.01) Returns: Map with landscape analysis including gradient norms and parameter sensitivities
(convergence-monitor history options)Monitor variational algorithm convergence with sophisticated stopping criteria.
This function tracks optimization progress and implements intelligent stopping criteria based on energy convergence, gradient norms, and parameter stability. Works with any variational quantum algorithm.
Parameters:
Returns: Map with convergence analysis and recommendations
Monitor variational algorithm convergence with sophisticated stopping criteria.
This function tracks optimization progress and implements intelligent
stopping criteria based on energy convergence, gradient norms, and
parameter stability. Works with any variational quantum algorithm.
Parameters:
- history: Vector of optimization steps {:iteration :energy :gradients :parameters}
- options: Convergence options map
- :tolerance - Energy convergence tolerance (default: 1e-6)
- :gradient-tolerance - Gradient norm tolerance (default: 1e-4)
- :min-iterations - Minimum iterations before convergence checking (default: 10)
- :patience - Window size for convergence analysis (default: 20)
Returns:
Map with convergence analysis and recommendations(enhanced-variational-objective hamiltonian
circuit-construction-fn
backend
execution-options)Create enhanced variational objective function that provides gradients.
This function creates an enhanced objective that computes both energy and gradients efficiently using the parameter shift rule. The gradient computation is integrated with the result framework to enable sophisticated gradient-based optimization methods.
Parameters:
Returns: Function that takes parameters and returns {:energy value :gradients [...] :quantum-state state}
Create enhanced variational objective function that provides gradients.
This function creates an enhanced objective that computes both energy and gradients
efficiently using the parameter shift rule. The gradient computation is integrated
with the result framework to enable sophisticated gradient-based optimization methods.
Parameters:
- hamiltonian: Hamiltonian to minimize
- circuit-construction-fn: Function that takes parameters and returns a circuit
- backend: Quantum backend for circuit execution
- execution-options: Execution options (can include :parallel? for gradient computation)
Returns:
Function that takes parameters and returns {:energy value :gradients [...] :quantum-state state}(enhanced-variational-optimization objective-fn initial-parameters options)Run variational algorithm optimization with integrated convergence monitoring.
This function wraps optimization methods with intelligent convergence monitoring, allowing for early stopping based on energy changes, gradient norms, and parameter stability. It tracks the full optimization history and provides detailed convergence analysis.
Supports enhanced objectives that provide gradients, falling back to standard optimization for regular objective functions.
Parameters:
Returns: Map with optimization results and convergence analysis
Run variational algorithm optimization with integrated convergence monitoring. This function wraps optimization methods with intelligent convergence monitoring, allowing for early stopping based on energy changes, gradient norms, and parameter stability. It tracks the full optimization history and provides detailed convergence analysis. Supports enhanced objectives that provide gradients, falling back to standard optimization for regular objective functions. Parameters: - objective-fn: Objective function to minimize (can be enhanced or standard) - initial-parameters: Starting parameter values - options: Optimization options including convergence monitoring parameters - :optimization-method - Method to use (default: :adam) - :max-iterations - Maximum iterations (default: 500) - :tolerance - Energy convergence tolerance (default: 1e-6) - :gradient-tolerance - Gradient norm tolerance (default: 1e-4) - :min-iterations - Minimum iterations before convergence (default: 10) - :patience - Convergence analysis window (default: 20) - :learning-rate - Learning rate for gradient descent (default: 0.01) Returns: Map with optimization results and convergence analysis
(random-parameter-initialization num-parameters
&
{:keys [range] :or {range [-0.1 0.1]}})Generate random initial parameters for variational algorithms.
Parameters:
Returns: Vector of random initial parameters
Generate random initial parameters for variational algorithms. Parameters: - num-parameters: Number of parameters to initialize - range: Parameter range as [min max] (default: [-0.1 0.1]) Returns: Vector of random initial parameters
(summarize-algorithm-performance algorithm-result algorithm-name)Create a summary of variational algorithm performance.
Parameters:
Returns: Map with performance summary
Create a summary of variational algorithm performance. Parameters: - algorithm-result: Complete result map from algorithm execution - algorithm-name: Name of the algorithm (e.g., 'VQE', 'QAOA') Returns: Map with performance summary
(variational-algorithm-template config algorithm-fns)Template for implementing variational quantum algorithms.
This function provides a common structure that can be used to implement new variational algorithms or refactor existing ones. It handles the common workflow and delegates algorithm-specific tasks to provided functions.
Parameters:
Returns: Complete algorithm result map
Template for implementing variational quantum algorithms. This function provides a common structure that can be used to implement new variational algorithms or refactor existing ones. It handles the common workflow and delegates algorithm-specific tasks to provided functions. Parameters: - config: Algorithm configuration map - algorithm-fns: Map of algorithm-specific functions: - :hamiltonian-constructor - (fn [config] -> hamiltonian) - :circuit-constructor - (fn [config] -> circuit-construction-fn) - :parameter-count - (fn [config] -> number) - :result-processor - (fn [optimization-result config] -> final-result) Returns: Complete algorithm result map
(variational-objective hamiltonian
circuit-construction-fn
backend
execution-options)Create a generic objective function for variational quantum algorithms.
This function provides a common interface for creating objective functions that work with both VQE and QAOA (and future variational algorithms). It abstracts the common pattern of:
The result extraction infrastructure handles backend capabilities transparently, so this always uses result-specs for consistent and efficient operation.
Parameters:
Returns: Function that takes parameters and returns energy expectation value
Examples: ;; For VQE: (create-variational-objective h2-hamiltonian ansatz-fn backend options)
;; For QAOA: (create-variational-objective problem-hamiltonian (partial qaoa-ansatz-circuit problem-h mixer-h num-qubits) backend options)
Create a generic objective function for variational quantum algorithms.
This function provides a common interface for creating objective functions
that work with both VQE and QAOA (and future variational algorithms). It
abstracts the common pattern of:
1. Convert parameters to quantum circuit
2. Execute circuit on backend
3. Extract Hamiltonian expectation value using result-specs
4. Return energy for optimization
The result extraction infrastructure handles backend capabilities transparently,
so this always uses result-specs for consistent and efficient operation.
Parameters:
- hamiltonian: Hamiltonian to minimize (collection of Pauli terms)
- circuit-construction-fn: Function that takes parameters and returns a circuit
- backend: Quantum backend for circuit execution
- execution-options: Options for circuit execution (shots, etc.)
Returns:
Function that takes parameters and returns energy expectation value
Examples:
;; For VQE:
(create-variational-objective h2-hamiltonian ansatz-fn backend options)
;; For QAOA:
(create-variational-objective problem-hamiltonian
(partial qaoa-ansatz-circuit problem-h mixer-h num-qubits)
backend options)(variational-optimization objective-fn initial-parameters options)Run optimization for variational quantum algorithms using specified method.
This function provides a common interface for optimization that works with both VQE and QAOA. It handles the method dispatching and delegates to the appropriate optimization functions from the qopt namespace.
Supported optimization methods:
Parameters:
Returns: Map with optimization results including convergence information
Run optimization for variational quantum algorithms using specified method. This function provides a common interface for optimization that works with both VQE and QAOA. It handles the method dispatching and delegates to the appropriate optimization functions from the qopt namespace. Supported optimization methods: - :gradient-descent - Basic gradient descent with parameter shift gradients - :adam - Adam optimizer with parameter shift gradients (recommended default) - :quantum-natural-gradient - Quantum Natural Gradient using Fisher Information Matrix - :nelder-mead - Derivative-free Nelder-Mead simplex method - :powell - Derivative-free Powell's method - :cmaes - Covariance Matrix Adaptation Evolution Strategy (robust) - :bobyqa - Bound Optimization BY Quadratic Approximation (handles bounds well) - :gradient - Fastmath gradient-based optimizers - :lbfgsb - L-BFGS-B optimization Parameters: - objective-fn: Objective function to minimize - initial-parameters: Starting parameter values - options: Optimization options map Returns: Map with optimization results including convergence information
(zero-parameter-initialization num-parameters)Generate zero initial parameters for variational algorithms.
Parameters:
Returns: Vector of zero initial parameters
Generate zero initial parameters for variational algorithms. Parameters: - num-parameters: Number of parameters to initialize Returns: Vector of zero initial parameters
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