Class Description: Data

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Class: Data

The Data class is used to manage and process activation matrices for quantum circuits. It includes the following attributes and methods:

Attributes

• qubits

  The number of qubits, which corresponds to the number of rows in the activation matrices.

• depth

  The circuit depth, which corresponds to the number of columns in the matrices.

• activation_matrices

  A list of three-dimensional activation matrices that represent the circuits.

Methods

• get_data

  This method loads the values for qubits, depth, and activation_matrices from the file data.json.

  • If var/data.json cannot be found, it returns Error Code: 1028 - Data.json not found.
  • Additionally, it checks if var/train.json can be found; if not, it returns Error Code: 1029 - train.json not found.

• return_matrices

  Returns the stored activation matrices.

  • Verifies that the activation matrices have the following properties:
    • Each matrix has a name.
    • The activation matrices have the format:
      • Number of rows equals the value of qubits.
      • Number of columns equals depth * 3.
    • If this check fails, it verifies if an activation matrix has 3 pages. If so, it outputs Error Code: 1030 - Activation matrix conversion unsuccessful.
    • train.json

• convert_matrices

  This method converts the activation matrices as follows:

  • Each matrix consists of 3 pages.

  • They contain as many rows as the number of qubits and as many columns as the circuit depth (depth).

  • Intermediate check:

    • Before starting the conversion, verify the following properties in the matrices:

      • Each activation matrix has 3 pages.
      • Each activation matrix has a name.
      • Each activation matrix has dimensions:
        • Rows = qubits
        • Columns = depth

      If any of these checks fail, return Error Code: 1031 - Activation matrix invalid before conversion.

  Conversion Process:

  • For each cell in the matrices, the values from the 3 pages are combined. For example:
    • Page 1, Row 1, Column 1: Value A
    • Page 2, Row 1, Column 1: Value B
    • Page 3, Row 1, Column 1: Value C
    • → The final matrix for Row 1, Column 1 contains a page with values: Position 1 = A, Position 2 = B, Position 3 = C
  • After conversion, the resulting matrices are stored in activation_matrices.
  • Finally, each matrix is checked to ensure it has a name, and its dimensions are validated using the method check_final_matrix.

  Error Code: 1008 - Activation matrix does not meet required dimensions.
  

• check_final_matrix

  This method checks if a given matrix meets the following conditions:

  • It has as many rows as the number of qubits.
  • It has as many columns as three times the depth value.

  This check is performed for each individual matrix to ensure the dimensions are correct.

• create_training_matrix

  This method creates a training matrix based on the existing values:

  • The training matrix has as many rows as the number of qubits and as many columns as three times the depth value.
  • Values are randomly generated and range between -2π and 2π.
  • After creation, the matrix is validated using check_final_matrix to ensure the dimensions meet the requirements.
  • If the dimensions do not match:

  Error Code: 1007 - Training matrix does not meet required dimensions.
  

  The matrix is then saved in train.json.