sf.tdm.reshape_samples

reshape_samples(all_samples, modes, N, timebins)[source]

Reshapes the samples dict so that they have the expected correct shape.

Corrects the all_samples dictionary so that the measured modes are the ones defined to be measured in the circuit, instead of being spread over a larger number of modes due to the mode-shifting occurring in TDMProgram.

The function iterates through samples obtained from the unrolled circuit to populate and return a new samples dictionary with the shape {spatial mode: (shots, timebins)}. E.g., this unrolled circuit:

...
MeasureHomodyne(0) | (q[0])  # shot 0, timebin 0, spatial mode 0  (sample 0)
MeasureHomodyne(0) | (q[2])  # shot 0, timebin 0, spatial mode 2  (sample 1)
...
MeasureHomodyne(0) | (q[0])  # shot 0, timebin 1, spatial mode 0  (sample 2)
MeasureHomodyne(0) | (q[1])  # shot 0, timebin 1, spatial mode 2  (sample 3)
...
MeasureHomodyne(0) | (q[0])  # shot 1, timebin 0, spatial mode 0  (sample 4)
MeasureHomodyne(0) | (q[2])  # shot 1, timebin 0, spatial mode 2  (sample 5)
...
MeasureHomodyne(0) | (q[0])  # shot 1, timebin 1, spatial mode 0  (sample 6)
MeasureHomodyne(0) | (q[1])  # shot 1, timebin 1, spatial mode 2  (sample 7)

would return the dictionary

{
    0: np.array([(sample 0), (sample 2), (sample 4), (sample 6)]),
    2: np.array([(sample 1), (sample 5)]),
    1: np.array([(sample 3), (sample 7)]),
}

which would then be reshaped, and returned, as follows:

{
    0: np.array([[(sample 0), (sample 2)],
                 [(sample 4), (sample 6)]]),
    2: np.array([[(sample 1), (sample 3)],
                 [(sample 5), (sample 7)]]),
}
Parameters
  • all_samples (dict[int, list]) – the raw measured samples

  • modes (Sequence[int]) – the modes that are measured in the circuit

  • N (Sequence[int]) – the number of concurrent modes per belt/spatial modes

  • timebins (int) – the number of timebins/temporal modes in the program per shot

Returns

the re-shaped samples, where each key correspond to a spatial

mode and the values have shape (shots, timebins)

Return type

dict[int, array]