Quantum-Computing

Quantum Computing has paved its path from being a theory to physical read-to-use machines. This project reflects on the implmentation of Quantum image processing with FRQI image model in Qiskit

Getting Started

Prerequisites

Python 3.5+ , qiskit , matplot and numpy. Installing Qiskit with visualization can be done using pip

pip install qiskit[visualization]

Running the program and seeing the result

Use runner.py to run the program and generate result.

Selecting images to check

There are 3 options for the image which can be selected from the Utils.py class:-

1. To select cat image call - util.get_Cat_image()
2. To select MNIST Image call - util.get_MNIST_data()
3. To select python generated image call - util.generate_image()

Image transformation

1. To rotate the image uncomment below line in runner.py

qed.quantum_rotate_image(qc)

2. To generate edge detection uncomment below line in runner.py

qed.quantum_edge_detection()

Running the noise model

To add moise model to the simulation uncomment below lines from the runner.py class

backend = provider.get_backend('ibmq_16_melbourne')
noise_model = NoiseModel.from_backend(backend)
coupling_map = backend.configuration().coupling_map
basis_gates = noise_model.basis_gates
result = execute(qc, Aer.get_backend('qasm_simulator'), shots=numOfShots,coupling_map=coupling_map,
                 basis_gates=basis_gates,
                 noise_model=noise_model).result()

Result

Result will be generated in the form of 'Result.png' and saved in the main folder.