Grover's Algorithm High-Performance Implementation

Overview

This project implements Grover's Algorithm using Qiskit, focusing on performance optimization. The implementation demonstrates quantum speedup for unstructured search problems and is validated on simulators.

Features

• Modular implementation: Oracle, Diffuser, and Grover Circuit
• Performance monitoring: gate count, depth, execution time

Project Structure

grovers_algorithm_project/
├── src/
│   ├── __init__.py
│   ├── oracle.py
│   ├── diffuser.py
│   ├── grover_circuit.py
├── tests/
│   ├── __init__.py
│   ├── test_oracle.py
│   ├── test_diffuser.py
│   ├── test_grover_circuit.py
├── docs/
│   ├── design.md
│   ├── user_guide.md
|
├── notebooks/
│   ├── grover_demo2.ipynb
├── requirements.txt
└── README.md

Setup Instructions

1. Clone the repository

git clone https://github.com/alephnan/GroverAlgorithm.git
cd GroversAlgorithm

2. Create and activate a Python virtual environment

python -m venv venv
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Run tests

pytest

Usage

There are two main ways to run the Grover's algorithm simulation:

1. Command-Line Interface (CLI)

Use the run_grover.py script located in the project root. This script allows you to specify the number of qubits, the target state, and the number of simulation shots.

Basic Syntax:

python run_grover.py -n <num_qubits> -m <target_state_binary> [-s <shots>]

Arguments:

• -n, --num_qubits: (Required) The total number of qubits for the search.
• -m, --marked_state: (Required) The binary string representing the state to search for (e.g., '101'). The length must match num_qubits.
• -s, --shots: (Optional) The number of times the simulation is run to gather statistics (default: 1024).

Example: To run a simulation with 3 qubits searching for the state |101>:

python run_grover.py -n 3 -m 101

The Python API also supports searching for multiple states by passing a list of binary strings to create_grover_circuit.

To see all options:

python run_grover.py --help

2. Jupyter Notebook

A demonstration notebook is available in the notebooks/ directory.

1. Ensure you have Jupyter Notebook or JupyterLab installed (pip install notebook or pip install jupyterlab).
2. Start the Jupyter server from your project root directory:

   jupyter notebook
   # or
   jupyter lab

3. Navigate to the notebooks/ folder in the Jupyter interface.
4. Open grover_demo.ipynb.
5. Run the cells in the notebook sequentially to see the algorithm in action, including visualizations.

Contributing

• Follow security and code quality guidelines
• Run tests and security scans before submitting PRs

License

MIT