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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