Measurements in PennyLane are special. They’re flexible, allowing you to perform measurements on simulators or actual quantum hardware–and they’re varied, allowing you to customize your quantum algorithms to your specific needs. Let’s look into different PennyLane measurements and how you can use them to your advantage. We will explore these measurements in the light of a simple, noiseless, two-qubit circuit where the first qubit is in the $\vert 0 \rangle$ state and the second in the $\vert 1 \rangle$ state. We will refer to it as our “simple circuit”:

In PennyLane, we implement it as follows ```python dev = qml.device("default.qubit", wires = 2, shots =1) @qml.qnode(dev) def simple_circuit(): qml.PauliX(1) return my_measurement # Replace with a PennyLane measurement ``` The table below summarizes the measurements you could put in place of `my_measurement`. Use it as a cheat sheet when you're already familiar with PennyLane measurements. However, if you’re looking to understand them in depth, make sure to read the relevant sections below.

* Read the next topic to understand differentiability!
Measurement	PennyLane function	Output type	Requires shots>0	Is differentiable*	Requires an observable	Example output
Sample	[qml.sample](https://docs.pennylane.ai/en/stable/code/api/pennylane.sample.html)	Array	Yes	No	No	`Out: array([0, 1])`
Counts	[qml.counts](https://docs.pennylane.ai/en/stable/code/api/pennylane.counts.html)	Dict	Yes	No	No	`Out: {'01': 1}`
Probability	[qml.probs](https://docs.pennylane.ai/en/stable/code/api/pennylane.probs.html)	Array	No	Yes	No	`Out: array([0., 1., 0., 0.])`
Expectation value	[qml.expval](https://docs.pennylane.ai/en/stable/code/api/pennylane.expval.html)	Float	No	Yes	Yes	`qml.expval(qml.Z(wires=0)), qml.expval(qml.Z(wires=1))` `Out: (1.0, -1.0)`

To understand how measurements work in PennyLane, we must first look into observables (pun intended).

Codercise PF.3.1 — Taking samples

Codercise PF.3.2 — Hermitian observable

Codercise PF.3.3 — Tensor observables

PennyLane measurements cheat sheet

Observables

Samples

Counts

Probabilities

Expectation values

Multiple measurements

Interfaces