Quantum VQE Optimization Project

Overview

This project focuses on optimizing Variational Quantum Eigensolver (VQE) circuits using gradient descent and parallel processing. It includes hyperparameter optimization through random search to identify the best parameters for the VQE circuits.

Best Parameters

The best parameters identified through random search are:

• Steps: 200
• Stepsize: 0.1
• Circuit: default

Results

The optimization process converged successfully, achieving a minimum cost value. The final report and graphs are saved in the results directory.

Graphs and Visualizations

• 
• 
• 
• 
• 

Process Description

1. Parameter Initialization: The initial parameters were randomly initialized.
2. Optimization: The optimization process was performed using a gradient descent optimizer.
3. Parallel Processing: The optimization and analysis were done in parallel to improve performance.
4. Analysis: The results were analyzed using various statistical metrics and visualizations.
5. Hyperparameter Search: A random search was conducted to find the best hyperparameters.

How to Run

1. Clone the repository:

   git clone https://github.com/dkrizhanovskyi/quantum-vqe-project.git
   cd quantum-vqe-project

2. Install dependencies:

   pip install -r requirements.txt

3. Run the random search for hyperparameter optimization:

   python random_search.py --config config.yaml

4. Run the optimization with the best parameters:

   python run_optimization.py --config config.yaml

Configuration

The config.yaml file contains the configuration parameters for the optimization process. Update the parameters as needed.

Example config.yaml

optimization:
  steps: 200  # Best number of steps
  stepsize: 0.1  # Best step size
  circuit: default  # Best circuit type
  save_path: results/state.json
  load_path: ''
results_dir: results
parallel_processes: 4  # Number of parallel processes