qml.sample

sample(op=None, wires=None)[source]

Sample from the supplied observable, with the number of shots determined from the dev.shots attribute of the corresponding device, returning raw samples. If no observable is provided then basis state samples are returned directly from the device.

Note that the output shape of this measurement process depends on the shots specified on the device.

Parameters
  • op (Observable or None) – a quantum observable object

  • wires (Sequence[int] or int or None) – the wires we wish to sample from, ONLY set wires if

  • is None (op) –

Raises

The samples are drawn from the eigenvalues \(\{\lambda_i\}\) of the observable. The probability of drawing eigenvalue \(\lambda_i\) is given by \(p(\lambda_i) = |\langle \xi_i | \psi \rangle|^2\), where \(| \xi_i \rangle\) is the corresponding basis state from the observable’s eigenbasis.

Example

dev = qml.device("default.qubit", wires=2, shots=4)

@qml.qnode(dev)
def circuit(x):
    qml.RX(x, wires=0)
    qml.Hadamard(wires=1)
    qml.CNOT(wires=[0, 1])
    return qml.sample(qml.PauliY(0))

Executing this QNode:

>>> circuit(0.5)
array([ 1.,  1.,  1., -1.])

If no observable is provided, then the raw basis state samples obtained from device are returned (e.g., for a qubit device, samples from the computational device are returned). In this case, wires can be specified so that sample results only include measurement results of the qubits of interest.

dev = qml.device("default.qubit", wires=2, shots=4)

@qml.qnode(dev)
def circuit(x):
    qml.RX(x, wires=0)
    qml.Hadamard(wires=1)
    qml.CNOT(wires=[0, 1])
    return qml.sample()

Executing this QNode:

>>> circuit(0.5)
array([[0, 1],
       [0, 0],
       [1, 1],
       [0, 0]])

Note

QNodes that return samples cannot, in general, be differentiated, since the derivative with respect to a sample — a stochastic process — is ill-defined. The one exception is if the QNode uses the parameter-shift method (diff_method="parameter-shift"), in which case qml.sample(obs) is interpreted as a single-shot expectation value of the observable obs.

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