Quantum image representation

We analysed the following image representation ways:

• qubit lattice [2];
• real ket [3];
• flexible representation of quantum images - FRQI [4];
• multi-channel Representation for Images - MCRQI [5];
• novel enhanced quantum representation of digital images - NEQR [6];
• novel quantum representation for log-polar images - QUALPI [7];
• quantum states for M colors and quantum states for N coordinates - QSMC and QSNC [8];
• a simple quantum representation - SQR [9];
• normal arbitrary quantum superposition state - NAQSS [10];
• generalized quantum image representation - GQIR [11];
• quantum representation of multi wavelength images - QRMW [12];
• quantum image representation based on bitplanes - BRQI [13];
• order-encoded quantum image model - OQIM [14];
• quantum representation of indexed images and its applications - QIIR [15];
• fourier transform qubit representation FTQR [16]; The underlined representations are already implemented in the current repo.

Implementations also include some of the image processing procedures which are discribed here.

Some attention to the Classical-to-quantum and Quantum-to-classical interfaces (C2QI and Q2CI) and testing with it the reliability of the quantum representation methods.

Classification

Metrics

• number of primitives (or big O notation);
• number of utilized qubits;
• circuit depth - read more;
• Quantum Volume - read more.

metric results of the gray-scaled images encoding:

Depth	Utilized qubits #	Quantum Volume

Authors

Marina Lisnichenko - [email protected];

Stanislav Protasov - [email protected].

Related publication

One day paper link will be here