pyqpanda_alg.QSEncode.QSEncode¶

Classes¶

QSpare_Code

Quantum Sparse State Encoding Class.

Module Contents¶

class pyqpanda_alg.QSEncode.QSEncode.QSpare_Code(prob_list=None, cut_length=None, mode='walsh')¶

Quantum Sparse State Encoding Class.

This class implements a quantum state preparation technique that encodes a sparse approximation of a probability distribution using either Walsh-Hadamard or Fourier basis. It allows for amplitude encoding and quantum circuit construction to simulate and analyze sparse distributions efficiently.

Parameters¶

prob_listlist[float] or np.ndarray: A list of probabilities representing the distribution to encode.
cut_lengthint, optional: The number of dominant components (in the transformed basis) to retain for sparse encoding. If not specified, defaults to 2 * log2(len(prob_list)).
modestr, optional: The basis transformation method, one of {‘walsh’, ‘fourier’}. Default is ‘walsh’.

Attributes¶

probnp.ndarray: Normalized probability distribution (padded to power-of-2 length).
qubits_numint: Number of qubits required for the state.
ampnp.ndarray: Square root of probabilities, used for amplitude encoding.
cutint: Number of top components retained after transformation.
modestr: Basis transform mode used for sparse encoding.

Methods¶

select_top_n_complex_numbers(arr, n): Selects top-n complex values by magnitude from the input array and zeroes out the rest.
Transform(amp): Applies Walsh-Hadamard or Fourier transform to the amplitude vector based on the mode.
quantum_cir(qubits): Constructs a quantum circuit that prepares the sparse encoded quantum state.
Quantum_Res(): Simulates the quantum circuit and returns the output probability distribution.

Examples

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from pyqpanda_alg.QSEncode import QSpare_Code
>>> mu = 0
>>> sigma = 1
>>> x = np.linspace(-3, 3, 2 ** 10)
>>> pdf_normal = (1 / (sigma * np.sqrt(2 * np.pi))) * np.exp(-(x - mu) ** 2 / (2 * sigma ** 2))
>>> # pdf_normal = (1 / (x * sigma * np.sqrt(2 * np.pi))) * np.exp(-(np.log(x) - mu) ** 2 / (2 * sigma ** 2))
>>> ini = pdf_normal / np.linalg.norm(pdf_normal)
>>> # res = QSEncode.QSpare_Code(ini ** 2, mode='walsh', cut_length=80).Quantum_Res()
>>> res = QSpare_Code(ini ** 2, mode='fourier', cut_length=20).Quantum_Res()

>>> plt.plot(x, res)
>>> plt.plot(x, ini ** 2)
>>> plt.show()

prob¶

qubits_num¶

amp¶

cut = None¶

mode = 'walsh'¶

select_top_n_complex_numbers(arr, n)¶

Selects top-n elements by magnitude from the input complex array.

Parameters¶

arrnp.ndarray: The input array of complex or real values.
nint: The number of largest-magnitude elements to keep.

Returns¶

np.ndarray: A new array with only top-n magnitudes retained, rest set to zero.

Transform(amp)¶

Applies the selected basis transformation to the input amplitude vector.

Parameters¶

ampnp.ndarray: Input amplitude vector.

Returns¶

np.ndarray: Transformed amplitude vector in the selected basis.

quantum_cir(qubits)¶

Constructs a quantum circuit to prepare the sparse-encoded quantum state.

Parameters¶

qubitslist[Qubit]: List of qubits to be used for the circuit.

Returns¶

QCircuit: A quantum circuit for sparse amplitude encoding.

Quantum_Res()¶

Simulates the sparse quantum state circuit and returns the resulting measurement distribution.

Returns¶

list[float]: The probability distribution from the simulated quantum circuit.