mindquantum.algorithm.nisq.IQPEncoding

class mindquantum.algorithm.nisq.IQPEncoding(n_feature, first_rotation_gate=RZ, second_rotation_gate=RZ, num_repeats=1, prefix: str = '', suffix: str = '')

General IQP Encoding.

For more information, please refer to Supervised learning with quantum-enhanced feature spaces.

Parameters

• n_feature (int) – The number of feature of data you want to encode with IQPEncoding.

• first_rotation_gate (ParameterGate) – One of the rotation gate RX, RY or RZ.

• second_rotation_gate (ParameterGate) – One of the rotation gate RX, RY or RZ.

• num_repeats (int) – Number of encoding iterations.

• prefix (str) – The prefix of parameters. Default: ''.

• suffix (str) – The suffix of parameters. Default: ''.

Examples

>>> import numpy as np
>>> from mindquantum.algorithm.nisq import IQPEncoding
>>> iqp = IQPEncoding(3)
>>> iqp.circuit
      ┏━━━┓ ┏━━━━━━━━━━━━┓
q0: ──┨ H ┠─┨ RZ(alpha0) ┠───■─────────────────────────────■─────────────────────────────────────────
      ┗━━━┛ ┗━━━━━━━━━━━━┛   ┃                             ┃
      ┏━━━┓ ┏━━━━━━━━━━━━┓ ┏━┻━┓ ┏━━━━━━━━━━━━━━━━━━━━━┓ ┏━┻━┓
q1: ──┨ H ┠─┨ RZ(alpha1) ┠─┨╺╋╸┠─┨ RZ(alpha0 * alpha1) ┠─┨╺╋╸┠───■─────────────────────────────■─────
      ┗━━━┛ ┗━━━━━━━━━━━━┛ ┗━━━┛ ┗━━━━━━━━━━━━━━━━━━━━━┛ ┗━━━┛   ┃                             ┃
      ┏━━━┓ ┏━━━━━━━━━━━━┓                                     ┏━┻━┓ ┏━━━━━━━━━━━━━━━━━━━━━┓ ┏━┻━┓
q2: ──┨ H ┠─┨ RZ(alpha2) ┠─────────────────────────────────────┨╺╋╸┠─┨ RZ(alpha1 * alpha2) ┠─┨╺╋╸┠───
      ┗━━━┛ ┗━━━━━━━━━━━━┛                                     ┗━━━┛ ┗━━━━━━━━━━━━━━━━━━━━━┛ ┗━━━┛
>>> iqp.circuit.params_name
['alpha0', 'alpha1', 'alpha2', 'alpha0 * alpha1', 'alpha1 * alpha2']
>>> iqp.circuit.params_name
>>> a = np.array([0, 1, 2])
>>> iqp.data_preparation(a)
array([0, 1, 2, 0, 2])
>>> iqp.circuit.get_qs(pr=iqp.data_preparation(a))
array([-0.28324704-0.21159186j, -0.28324704-0.21159186j,
        0.31027229+0.16950252j,  0.31027229+0.16950252j,
        0.02500938+0.35266773j,  0.02500938+0.35266773j,
        0.31027229+0.16950252j,  0.31027229+0.16950252j])

data_preparation(data)

Prepare the classical data into suitable dimension for IQPEncoding.

The IQPEncoding ansatz provides an ansatz to encode classical data into quantum state.

Suppose the origin data has \(n\) features, then the output data will have \(2n-1\) features, with first \(n\) features will be the original data. For \(m > n\),

\[\text{data}_m = \text{data}_{m - n} * \text{data}_{m - n - 1}\]

Parameters

data ([list, numpy.ndarray]) – The classical data need to encode with IQPEncoding ansatz.

Returns

numpy.ndarray, the prepared data that is suitable for this ansatz.