orquestra.forest device provided by the PennyLane-Orquestra plugin allows you to use PennyLane
to deploy and run your quantum machine learning models on the backends and simulators provided
by Rigetti Forest SDK.
You can instantiate a
'orquestra.forest' device for PennyLane with:
import pennylane as qml dev = qml.device('orquestra.forest', wires=2)
This device can then be used just like other devices for the definition and evaluation of QNodes within PennyLane. A simple quantum function that returns the expectation value of a measurement and depends on three classical input parameters would look like:
@qml.qnode(dev) def circuit(x, y, z): qml.RZ(z, wires=) qml.RY(y, wires=) qml.RX(x, wires=) qml.CNOT(wires=[0, 1]) return qml.expval(qml.PauliZ(wires=1))
You can then execute the circuit like any other function to get the quantum mechanical expectation value.
circuit(0.2, 0.1, 0.3)
By default, the
orquestra.forest device uses the
'wavefunction-simulator' backend, but this may be changed to hardware
'3q-noisy-qvm'. For more information on available
backends, please visit the Orquestra interfaces documentation.
dev = qml.device('orquestra.forest', wires=2, backend='3q-noisy-qvm')