qrisp.QuantumArray.__imul__

QuantumArray.__imul__(other: ArrayLike) → QuantumArray

Performs element-wise in-place multiplication. Note that this modifies the original QuantumArray and does not create a new one.

Parameters:

otherArrayLike

The array or scalar to be multiplied with the QuantumArray. If an array is provided, it must have the same shape as the original QuantumArray. If a scalar is provided, it will be multiplied with each element of the QuantumArray. If the qtype of self is an unsigned QuantumFloat, the right-hand side must be non-negative.

Returns:

QuantumArray

The modified QuantumArray containing the result of the in-place multiplication. The qtype of the output will be the same as the qtype of self. This may lead to overflow.

Raises:

TypeError

If the qtypes of self and other are incompatible for multiplication.

ValueError

If other is an array (QuantumArray or numpy/jax array) and its shape does not match the shape of self.

TypeError

If other is a QuantumArray or QuantumVariable, since quantum-quantum in-place multiplication is not supported. Use out-of-place multiplication instead.

NotImplementedError

If in tracing mode and self’s qtype is not QuantumModulus, since quantum-classical in-place multiplication is not supported in tracing mode for non-QuantumModulus types.

Examples

Multiplying a scalar with a QuantumArray of QuantumFloats, and scaling a QuantumArray by a numpy array:

>>> import numpy as np
>>> from qrisp import QuantumArray, QuantumFloat
>>> qa = QuantumArray(QuantumFloat(8,-1), shape=(2,2))
>>> qa[:] = np.array([[1.0, 2.0], [3.0, 4.0]])
>>> qa *= 2.0
>>> print(qa)  # Output: [[2.0, 4.0], [6.0, 8.0]]
>>> qa *= np.array([[0.5, 1.5], [2.5, 3.5]])
>>> print(qa)  # Output: [[1.0, 6.0], [15.0, 28.0]]