Transformation of Classical to Quantum Image, Representation, Processing and Noise Mitigation

Abstract

Quantum and classical computers have drastically different image representations. In a classical computer, bits are used. However, in a quantum computer, qubits are used. In this paper, the quantum image representation is the similar as the classical image representation. To represent quantum images, qubits and their associated properties have been used. Quantum imaging has previously been done via superposition. As a result, quantum imaging implemented using the superposition feature. Unitary matrices are then used to represent quantum circuits. For the quantum representation, we've gone with a modest image. To create quantum circuits, IBM's Qiskit software and Anaconda Python was used. On an IBM real time computer and an Aer simulator, a quantum circuit with 10,000 shots runs. Noise has been reduced more in the IBM real time computer than in the IBM Aer simulator. As a result, the Aer simulator's noise and qubit errors are higher than the IBM real time computer's. Quantum circuit design and image processing are both done with Qiskit programming, which is an appendix at the end of the paper. As the number of shots raise, the noise level decreases even further. Noise and qubit errors increase when the image operates at a low number of shots. Quantum image processing, noise reduction, and error correction done by circuit computation shots increase. Quantum image processing, representation, noise reduction, and error correction all make use of the quantum superposition concept.