Quantum function transform that substitutes operations conditioned on measurement outcomes to controlled operations.
This transform uses the deferred measurement principle and applies to qubit-based quantum functions.
Support for mid-circuit measurements is device-dependent. If a device doesn’t support mid-circuit measurements natively, then the QNode will apply this transform.
The transform uses the
ctrl()transform to implement operations controlled on mid-circuit measurement outcomes. The set of operations that can be controlled as such depends on the set of operations supported by the chosen device.
Devices that inherit from
QubitDevicemust be initialized with an additional wire for each mid-circuit measurement after which the measured wire is reused or reset for
defer_measurementsto transform the quantum tape correctly. Hence, devices and quantum tapes must also be initialized without custom wire labels for correct behaviour.
This transform does not change the list of terminal measurements returned by the quantum function.
When applying the transform on a quantum function that returns
state()as the terminal measurement or contains the
Snapshotinstruction, state information corresponding to simulating the transformed circuit will be obtained. No post-measurement states are considered.
tape (QuantumTape) – a quantum tape
Suppose we have a quantum function with mid-circuit measurements and conditional operations:
def qfunc(par): qml.RY(0.123, wires=0) qml.Hadamard(wires=1) m_0 = qml.measure(1) qml.cond(m_0, qml.RY)(par, wires=0) return qml.expval(qml.PauliZ(0))
defer_measurementstransform allows executing such quantum functions without having to perform mid-circuit measurements:
>>> dev = qml.device('default.qubit', wires=2) >>> transformed_qfunc = qml.defer_measurements(qfunc) >>> qnode = qml.QNode(transformed_qfunc, dev) >>> par = np.array(np.pi/2, requires_grad=True) >>> qnode(par) tensor(0.43487747, requires_grad=True)
We can also differentiate parameters passed to conditional operations:
>>> qml.grad(qnode)(par) tensor(-0.49622252, requires_grad=True)
Reusing and reseting measured wires will work as expected with the
dev = qml.device("default.qubit", wires=3) @qml.qnode(dev) def func(x, y): qml.RY(x, wires=0) qml.CNOT(wires=[0, 1]) m_0 = qml.measure(1, reset=True) qml.cond(m_0, qml.RY)(y, wires=0) qml.RX(np.pi/4, wires=1) return qml.probs(wires=[0, 1])
Executing this QNode:
>>> pars = np.array([0.643, 0.246], requires_grad=True) >>> func(*pars) tensor([0.76960924, 0.13204407, 0.08394415, 0.01440254], requires_grad=True)
- What is PennyLane?
- Quantum circuits
- Gradients and training
- Quantum operators
- Inspecting circuits
- Compiling circuits
- Quantum Chemistry
- Quantum Datasets
- QNode returns