Grover's Algorithm with PennyLane

Grover's Algorithm with PennyLane

This PennyLane demo implements Grover's algorithm, the canonical quantum search routine that finds a marked item in an unstructured space of N possibilities using only about sqrt(N) oracle queries, a quadratic speedup over the O(N) any classical search requires. The tutorial builds the algorithm from its components using PennyLane's circuit primitives: preparing a uniform superposition over all candidates with Hadamard gates, applying an oracle that flips the phase of the marked state, and applying the Grover diffusion (amplitude-amplification) operator that reflects the state about its mean to boost the marked amplitude. It shows how to repeat the oracle-plus-diffusion step the optimal number of times and how the success probability evolves with the number of iterations, then measures the register to recover the marked item. PennyLane's built-in GroverOperator makes the diffusion step concise, and the demo visualizes how the amplitude concentrates on the solution. Because amplitude amplification underlies many quantum speedups, it is a foundational example for understanding how interference is engineered to surface a desired answer, presented with PennyLane.

Run it

pip install -r requirements.txt
python demo.py

Source and license

Imported from demonstrations_v2/tutorial_grovers_algorithm/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.