easy-vqe 1.2.0

A user-friendly wrapper for setting up and running VQE calculations with Qiskit.

Easy VQE

A user-friendly Python package for setting up and running Variational Quantum Eigensolver (VQE) calculations using Qiskit. Define Hamiltonians as simple strings and ansatz circuits using nested lists/tuples.

Features

• Simple Hamiltonian Definition: Define Hamiltonians using intuitive strings (e.g., "1.0 * XX - 0.5 * ZI + YZ"). Handles various formats, signs, and spacing.
• Flexible Ansatz Creation: Build parameterized circuits using a list-based structure with standard Qiskit gate names. Auto-generates parameters.
• Automated VQE Workflow: Handles parameter management, basis rotations, measurement simulation, expectation value calculation, and optimization loop.
• Built-in Optimization: Uses scipy.optimize.minimize with configurable methods (COBYLA, Nelder-Mead, etc.) and options.
• Simulation Backend: Leverages qiskit-aer for efficient classical simulation.
• Convergence Plotting: Optionally save plots of the energy convergence during optimization.
• Flexible Initial Parameters: Supports random initialization, zero initialization, or providing specific lists/NumPy arrays for starting parameters.
• Result Summarization: Includes helper functions to print a clean summary of the VQE results.
• Theoretical Energy Calculation: Provides a utility to compute the exact ground state energy via matrix diagonalization for comparison.
• Circuit Visualization: Includes a helper function to draw the final ansatz circuit with the optimized parameters bound.

Installation

You can install easy_vqe using pip:

pip install easy-vqe

Or, for development, clone this repository and install in editable mode:

git clone https://github.com/7Abdoman7/easy_vqe.git 
cd easy_vqe
pip install -e .[dev] 

Quick Start

Here's a basic example to find the ground state energy of a 3-qubit Hamiltonian:

import numpy as np
from easy_vqe import find_ground_state

# 1. Define the Hamiltonian string
hamiltonian_str = "-1.0 * ZZI + 0.9 * ZIZ - 0.5 * IZZ + 0.2 * XXX"

# 2. Define the Ansatz Structure
ansatz_block = [
    ('ry', [0, 1, 2]), # Ry on qubits 0, 1, 2 (each gets a parameter)
    ('cx', [0, 1]),    # CNOT 0 -> 1
    ('cx', [1, 2]),    # CNOT 1 -> 2
    ('rz', [0, 1, 2]), # Rz on qubits 0, 1, 2 (each gets a parameter)
]
# Build the full ansatz: Hadamard layer, block, CNOT, block
ansatz_structure = [
    ('h', [0, 1, 2]),
    ansatz_block,
    ('cx', [0, 2]),
    ('barrier', []), # Optional barrier for visual separation
    ansatz_block,
]

# 3. Run the VQE algorithm
results = find_ground_state(
    ansatz_structure=ansatz_structure,
    hamiltonian_expression=hamiltonian_str,
    optimizer_method='COBYLA',
    optimizer_options={'maxiter': 200, 'tol': 1e-5}, # Optimizer settings
    n_shots=4096, # Number of shots for expectation value
    plot_filename="vqe_convergence.png" # Save the plot
)

# 4. Print Results
if 'error' not in results:
    print(f"Optimization Successful: {results['success']}")
    print(f"Optimizer Message: {results['message']}")
    print(f"Minimum Energy Found: {results['optimal_value']:.6f}")
    print(f"Optimal Parameters: {np.round(results['optimal_params'], 4)}")
else:
    print(f"VQE Failed: {results['error']}")

See the examples/ directory for more usage scenarios.

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests. (Add more details here if you like - code style, testing requirements etc.)

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.