qolumbina.utils.data_conversion module

qolumbina.utils.data_conversion module#

A collection of utility functions that can be used for preprocessing and postprocessing of the test process.

counts_to_expectation(counts, observable)[source]#

Convert counts to expectation value of a given observable.

Parameters:

  • counts (dict[str, int]) – Measurement counts as a dictionary mapping bitstrings to their counts

  • observable (dict[str, float]) – A dictionary mapping bitstrings to their observable values

Returns:

The expectation value as a float.

Return type:

float

Example

>>> counts_to_expectation({'00': 500, '01': 300, '10': 100, '11': 100},
...                       {'00': 1.0, '01': -1.0, '10': -1.0, '11': 1.0})
0.2  # (0.5*1.0 + 0.3*(-1.0) + 0.1*(-1.0) + 0.1*1.0 = 0.2)
counts_to_probabilities(counts)[source]#

Convert counts to probabilities.

Parameters:

counts (dict[str, int]) – Measurement counts as a dictionary mapping bitstrings to their counts

Returns:

A dictionary mapping bitstrings to their probabilities.

Return type:

dict[str, float]

Example

>>> counts_to_probabilities({'00': 500, '01': 300, '10': 100, '11': 100})
{'00': 0.5, '01': 0.3, '10': 0.1, '11': 0.1}
decompose_joint_state(statevector, target_subsystem, atol=1e-10)[source]#

Return the reduced density matrix of the given subsystem.

Parameters:

  • statevector (Statevector) – Joint pure state \(\ket{\psi_{AB}}\) of the entire system consisting of subsystems \(A\) and \(B\), represented as a Statevector object.

  • target_subsystem (list[int]) – List of qubit indices for the target subsystem \(A\)

  • atol (float) – Absolute tolerance for numerical operations

Returns:

A tuple containing a string indicating the type ('pure_state'

or 'mixed_state') and the corresponding Statevector or DensityMatrix of the target subsystem \(A\).

Raises:

ValueError – if target_subsystem indices exceed the number of qubits in statevector

Return type:

tuple[str, Statevector | DensityMatrix]

Example

>>> from qiskit.quantum_info import Statevector
>>> import numpy as np
>>> psi_AB = Statevector.from_label('00') + Statevector.from_label('11')
>>> psi_AB = psi_AB / np.linalg.norm(psi_AB.data)  # Normalize
>>> decompose_joint_state(psi_AB, target_subsystem=[0])
('mixed_state', DensityMatrix([[0.5+0.j, 0. +0.j],
       [0. +0.j, 0.5+0.j]],
      dims=(2,)))
frac_to_gate_list(value, n_bits, tol=1e-09)[source]#

Convert a fractional decimal number to a quantum gate list using fixed-point binary representation, with precision check.

Parameters:

  • value (float) – Fractional decimal number to convert (must be in \([0, 1)\))

  • n_bits (int) – Number of bits in the output gate list (must be positive)

  • tol (float) – Tolerance for precision check (default: 1e-9)

Returns:

Representation of a gate list

Raises:

  • ValueError – if value is not in \([0, 1)\), or if n_bits is not positive

  • TypeError – if value is not a float, or if n_bits is not an integer

Return type:

list[str]

Example

>>> frac_to_gate_list(0.375, 4)   # 0.25 + 0.125 = 0.011 in binary
['i', 'x', 'x', 'i']
int_to_gate_list(value, n_bits)[source]#

Convert a decimal integer to gate list representation of a quantum state using binary encoding.

Parameters:

  • value (int) – Non-negative integer to convert

  • n_bits (int) – Number of bits in the output gate list

Returns:

Representation of a gate list

Raises:

  • ValueError – if value is negative, or if n_bits is not positive

  • TypeError – if value is not an integer, or if n_bits is not an integer

Return type:

list[str]

Example

>>> int_to_gate_list(3, 4)
['x', 'x', 'i', 'i']
# 3 = 0b0011
# (q0, q1, q2, q3) -> ('x', 'x', 'i', 'i')
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