Quantum machine learning and optimization with PennyLane

Learn more about how to combine PennyLane with Amazon Braket.

Combining PennyLane with Amazon Braket

This tutorial shows you how to construct circuits and evaluate their gradients in PennyLane with execution performed using Amazon Braket.

Computing gradients in parallel with PennyLane-Braket

Learn how to speed up training of quantum circuits by using parallel execution on Amazon Braket. Quantum circuit training involving gradients requires multiple device executions. The Amazon Braket SV1 simulator can be used to overcome this. The tutorial benchmarks SV1 against a local simulator, showing that SV1 outperforms the local simulator for both executions and gradient calculations. This illustrates how parallel capabilities can be combined between PennyLane and SV1.

Graph optimization with QAOA

In this tutorial, you learn how quantum circuit training can be applied to a problem of practical relevance in graph optimization. It easy it is to train a QAOA circuit in PennyLane to solve the maximum clique problem on a simple example graph. The tutorial then extends to a more difficult 20-node graph and uses the parallel capabilities of the Amazon Braket SV1 simulator to speed up gradient calculations and hence train the quantum circuit faster, using around 1-2 minutes per iteration.

Hydrogen Molecule geometry with VQE

In this tutorial, you will learn how PennyLane and Amazon Braket can be combined to solve an important problem in quantum chemistry. The ground state energy of molecular hydrogen is calculated by optimizing a VQE circuit using the local Braket simulator. This tutorial highlights how qubit-wise commuting observables can be measured together in PennyLane and Amazon Braket, making optimization more efficient.