grover-visualizer 0.1.5

Visualize Grover's Algorithm with quantum state animations

Quantum Grover's Algorithm with Visualization

This project implements Grover's Algorithm using Qiskit and provides an animation of the probability amplitudes as the algorithm progresses through its iterations.

Features

• Grover's Search Algorithm: Implementation of the quantum search algorithm.
• Dynamic Oracle: Configurable target states for the search.
• State Visualization: Real-time animation of probability distributions after each iteration.
• Hybrid Backend Support: Automatically falls back to local AerSimulator if IBM Quantum connection is unavailable.
• Optimal Iteration Calculation: Automatically determines the number of iterations needed based on the number of qubits and target states.

Prerequisites

To run this project, you need Python 3.8+ installed along with the following libraries:

• qiskit>=1.0.0
• qiskit-aer>=0.13.0
• qiskit-ibm-runtime>=0.17.0
• matplotlib>=3.7.0
• numpy>=1.24.0

Installation

You can install the package directly from PyPI:

pip install grover-visualizer

Or you can clone the reposetory and isntall the dependencies with:

gh repo clone SilentSword123456/Groovers_Algorithm-Quantum
cd Groovers_Algorithm-Quantum
pip install -r requirements.txt

Usage

After installation, you can run the visualization using:

python -m grover_visualizer.grover

Configuration

By default, the visualizer searches for states '0101' and '1111'. You can run it with a custom configuration using:

python -m grover_visualizer.grover --custom

This will prompt you to enter the number of qubits and the target states you want to search for.

Alternatively, you can still modify the targets list directly in grover_visualizer/grover.py.

Project Structure

• grover_visualizer/grover.py: The core script that constructs the quantum circuit, runs the simulation, and manages the algorithm's iterations.
• grover_visualizer/animation.py: Contains the logic for the Matplotlib-based animation of quantum states.

How it Works

1. Initialization: The circuit starts by applying Hadamard gates to all qubits, creating a uniform superposition of all possible states.
2. Oracle: A phase-flip oracle marks the target state(s) by reversing their signs.
3. Diffusion (Inversion about the Mean): This operator amplifies the probability amplitude of the marked state while decreasing the amplitudes of other states.
4. Repetition: The Oracle and Diffusion steps are repeated for the optimal number of times ($\approx \frac{\pi}{4}\sqrt{2^n/M}$).
5. Animation: The grover_visualizer/animation.py module tracks the statevector after each iteration to visualize how the probability of the target state grows.
6. Measurement: Finally, the qubits are measured, and the results are displayed as a histogram of counts.