Keras 3 Support

PennyLane Development
1

Hey team.
I’m not sure what the current state of KerasLayer development for Keras 3 is at, but I have a temp workaround that sorta works with the Keras 3 generic backend prototype.

class DataReuploader(Layer):
    def __init__(self,qnode,qubits,name,sum=False,weight_specs=None,**kwargs):
        weight_shapes = {"weights": (qubits,2)}
        self.qubits = qubits
        if k.backend.backend() =="tensorflow":
            self.circ = qml.qnn.KerasLayer(circuit,weight_shapes,output_dim=qubits)
        elif k.backend.backend() =="torch":
            self.circ = qml.qnn.TorchLayer(circuit,weight_shapes)
        super().__init__(name=name,*kwargs)
        self.sum = sum
        self.name = name
        self.weight_specs = weight_specs if weight_specs is not None else {}

    @property
    def trainable_weights(self):
        return self.circ.trainable_weights

    @property
    def trainable_variables(self):
        return self.circ.trainable_variables

    def get_config(self):
        config = super().get_config()
        config.update(self.circ.get_config())
        config.update({"name":self.name,"sum_units":self.sum})
        return config

    def compute_output_shape(self,input_shape):
           return self.circ.compute_output_shape(input_shape)
            
    def call(self, x):
        x = self.circ(x)
        return x

Essentially a hacky way of leveraging the exist TFKeras and Torch wrappers for qnodes to extend the functionallity to Keras 3.

Currently testing just replacing all the tf.** calls on the source code with keras.op.* to see if that also work with keras 3

2

Hi @vinayak19th ,

Thanks for sharing this!

I’ve shared it with our PennyLane dev team to see if we can support Keras3.
At the moment we don’t know if we can fully support it but your workaround may indeed help others!