# qml.math.is_abstract¶

is_abstract(tensor, like=None)[source]

Returns True if the tensor is considered abstract.

Abstract arrays have no internal value, and are used primarily when tracing Python functions, for example, in order to perform just-in-time (JIT) compilation.

Abstract tensors most commonly occur within a function that has been decorated using @tf.function or @jax.jit.

Note

Currently Autograd tensors and Torch tensors will always return False. This is because:

• Autograd does not provide JIT compilation, and

• @torch.jit.script is not currently compatible with QNodes.

Parameters
• tensor (tensor_like) – input tensor

• like (str) – The name of the interface. Will be determined automatically if not provided.

Returns

whether the tensor is abstract or not

Return type

bool

Example

Consider the following JAX function:

import jax
from jax import numpy as jnp

def function(x):
print("Value:", x)
print("Abstract:", qml.math.is_abstract(x))
return jnp.sum(x ** 2)


When we execute it, we see that the tensor is not abstract; it has known value:

>>> x = jnp.array([0.5, 0.1])
>>> function(x)
Value: [0.5, 0.1]
Abstract: False
DeviceArray(0.26, dtype=float32)


However, if we use the @jax.jit decorator, the tensor will now be abstract:

>>> x = jnp.array([0.5, 0.1])
>>> jax.jit(function)(x)
Value: Traced<ShapedArray(float32)>with<DynamicJaxprTrace(level=0/1)>
Abstract: True
DeviceArray(0.26, dtype=float32)


Note that JAX uses an abstract shaped array, so although we won’t be able to include conditionals within our function that depend on the value of the tensor, we can include conditionals that depend on the shape of the tensor.

Similarly, consider the following TensorFlow function:

import tensorflow as tf

def function(x):
print("Value:", x)
print("Abstract:", qml.math.is_abstract(x))
return tf.reduce_sum(x ** 2)

>>> x = tf.Variable([0.5, 0.1])
>>> function(x)
Value: <tf.Variable 'Variable:0' shape=(2,) dtype=float32, numpy=array([0.5, 0.1], dtype=float32)>
Abstract: False
<tf.Tensor: shape=(), dtype=float32, numpy=0.26>


If we apply the @tf.function decorator, the tensor will now be abstract:

>>> tf.function(function)(x)
Value: <tf.Variable 'Variable:0' shape=(2,) dtype=float32>
Abstract: True
<tf.Tensor: shape=(), dtype=float32, numpy=0.26>