cuquantum.densitymat.DenseOperator

class cuquantum.densitymat.DenseOperator(data, callback=None, copy=True)

Dense elementary operator from data buffer and optional callback.

Parameters

• data – Data buffer for operator elements.

• callback – An inplace or out-of-place callback function that modifies CPU or GPU buffer.

Note

• If number of dimensions in data is odd, the last dimension is assumed to be the batch dimension.

• If copy=True, a copy will be created on the data buffer and can be accessed through the data attribute. Note that if a np.ndarray is passed, it will be copied to GPU at a later stage.

• If copy=False, the provided data buffer is required to be a cp.ndarray and Fortran-contiguous.

• The current underlying data buffer is accessible via the data attribute. Modification of the underlying data buffer by the user will lead to undefined behaviour.

• If an out-of-place callback is provided, it needs to return an array needs that is consistent with the provided data buffer in terms of shape and data type.

• If an inplace callback is provided, it needs perform an inplace modification on an array that is provided as its third positional argument.

• The data buffer will be updated when this instance is involved in a compute method of an Operator or OperatorAction if a callback is passed.

Examples

>>> import numpy as np
>>> from cuquantum.densitymat import DenseOperator

Suppose we want to construct a creation operator on a Hilbert space of dimension 3 as a DenseOperator. It can be constructed from the data buffer directly as

>>> data = np.array([
>>>     [0, 0, 0],
>>>     [1.0, 0, 0],
>>>     [0, np.sqrt(2), 0],
>>> ])
>>> dense_op = DenseOperator(data)

Methods

__add__(other: Union[DenseOperator, MultidiagonalOperator]) → DenseOperator

Add an elementary operator to this instance and return a new DenseOperator.

__init__(data: Union[numpy.ndarray, cupy.ndarray], callback: Optional[Callback] = None, copy: bool = True) → None

Initialize a dense elementary operator from data buffer and optional callback.

__matmul__(other: Union[DenseOperator, MultidiagonalOperator]) → DenseOperator

Perform matrix multiplication between this instance and an elementary operator and return a new DenseOperator.

__mul__(scalar: Union[numbers.Number, numpy.ndarray, cupy.ndarray]) → DenseOperator

Multiply this instance with a scalar on the left.

__rmul__(scalar: Union[numbers.Number, numpy.ndarray]) → DenseOperator

Multiply this instance with a scalar on the right.

__sub__(other: Union[DenseOperator, MultidiagonalOperator]) → DenseOperator

Subtract an elementary operator from this instance and return a new DenseOperator.

dag() → DenseOperator

Return the conjugate complex transpose of this instance.

to_array(t: Optional[float] = None, args: Optional[Union[numpy.ndarray, cupy.ndarray]] = None, device: Optional[Union[int, str]] = None) → Union[numpy.ndarray, cupy.ndarray]

Return the array form of the dense elementary operator.

Parameters

• t – Time variable in callback, only required if callback is not None.

• args – Additional arguments in callback, only required if callback is not None.

• device – Device on which to return the array. Defaults to "cpu".

Note

• If the device is not specified, an ElementaryOperator without callback will return a reference to its current underlying data. Otherwise, the return location will match that of the Callback instance.

• If this instance has a callback, the callback arguments t and Beyond sys.argv are required.

• This call is blocking if it involves device-to-host or device-to-device transfer. Otherwise, it is stream-ordered on the current stream.

Attributes

data

Data buffer of the elementary operator.

device_id

Return device ID if stored on GPU and None if stored on CPU.