Introduction to Mid-Circuit Measurements in PennyLane

Introduction to Mid-Circuit Measurements in PennyLane

This PennyLane tutorial introduces mid-circuit measurements (MCMs), the capability to measure qubits partway through a circuit and use the outcomes to influence the rest of the computation, a feature central to dynamic circuits, quantum error correction, and many modern protocols. In a conventional circuit all measurements happen at the end, but MCMs let a circuit branch on intermediate results: measuring a qubit, optionally resetting it, and conditioning later operations on what was observed (feedforward). The tutorial shows PennyLane's syntax for mid-circuit measurements, how to apply gates conditioned on a measurement result, how to reuse measured qubits, and the different ways to collect statistics over MCM outcomes (such as sampling, expectation values, and counts) across the various supported measurement-handling methods. It explains how PennyLane simulates these stochastic, branch-dependent circuits and the subtleties of post-selection versus deferred measurement. By establishing the MCM primitives clearly, the tutorial gives the foundation for dynamic circuits, teleportation, and error-correction demos. It is a practical, beginner-friendly entry point to measurement-driven quantum programming in PennyLane.

Run it

pip install -r requirements.txt
python demo.py

Source and license

Imported from demonstrations_v2/tutorial_mcm_introduction/demo.py in PennyLaneAI/demos at c52c0abeb5122218aa96b38eea848864cce7323f, under the Apache License 2.0. Original authors: Xanadu and the PennyLane community. The upstream LICENSE is included alongside this example.