Hello,

This is the code:

```
nqb = 3
dev = qml.device('default.qubit', wires=nqb, shots=1000)
def block(x, pars):
print("pars[0]= ",pars[0])
broadcast(unitary=qml.RX, pattern="single", wires=range(nqb), parameters=pars[0])
broadcast(unitary=qml.RY, pattern="single", wires=range(nqb), parameters=x[0])
broadcast(unitary=qml.RX, pattern="single", wires=range(nqb), parameters=pars[1])
broadcast(unitary=qml.RY, pattern="single", wires=range(nqb), parameters=x[1])
broadcast(unitary=qml.RX, pattern="single", wires=range(nqb), parameters=pars[2])
broadcast(unitary=qml.RY, pattern="single", wires=range(nqb), parameters=x[0])
broadcast(unitary=qml.RX, pattern="single", wires=range(nqb), parameters=pars[3])
broadcast(unitary=qml.RY, pattern="single", wires=range(nqb), parameters=x[1])
@qml.qnode(dev)
def circuit(x, params):
block(x, pars=params)
return [qml.expval(qml.PauliZ(wires=i)) for i in range(nqb)]
x_t = np.array(x_b, requires_grad=False)
print(x_t)
[[0.1 0.3 0.5]
[0.2 0.4 0.6]]
params_t = np.array(params_b, requires_grad=True)
print(params_t)
[[ 0.7312 0.7312 0.7312]
[ 0.9535 0.9535 0.9535]
[-0.7562 -0.7562 -0.7562]
[ 1.5118 1.5118 1.5118]]
qml.specs(circuit)(x_t, params_t)
{'gate_sizes': defaultdict(int, {1: 24}),
'gate_types': defaultdict(int, {'RX': 12, 'RY': 12}),
'num_operations': 24,
'num_observables': 3,
'num_diagonalizing_gates': 0,
'num_used_wires': 3,
'depth': 8,
'num_trainable_params': 12,
'num_device_wires': 3,
'device_name': 'default.qubit',
'expansion_strategy': 'gradient',
'gradient_options': {},
'interface': 'autograd',
'diff_method': 'best',
'gradient_fn': 'pennylane.gradients.parameter_shift.param_shift',
'num_gradient_executions': 24}
```

Here the points to classify are (0.1,0.2), (0.3,0.4) and (0.5,0.6), one for each qubit.

Indeed, it seems that I can not use the broadcast method as a kind of linking a reference to a parameter over several wires, as I was hoping, I end up with multiplying them for each wire, instead.

I wonder if there is such a possibility in Pennylane.