PennyLane
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Even with a small number of available qubits, large quantum circuits can be cut up into fragments and their results classically recombined into the desired, full computation. In this how-to we present a short introduction to making quantum circuit cuts in PennyLane.

Have you ever wanted to befriend a quantum computer or two? Join us at the PennyLane Device Zoo, a place where everybody can find the right device for their problem and make their code fly!

In this how-to, we will learn about the best practices in PennyLane to deal with batches and collections of circuits.

The purpose of this article is to explain how to obtain information about devices and circuits using the qml.specs and qml.Tracker functions.

In this how-to we show how to use PennyLane to create your own custom devices with a simple one-qubit example.

Hartree-Fock is a standard classical state-preparation step for running quantum chemistry workflows. This post goes through how to make the process painless with PennyLane!

In this how-to, we will explore a few methods to encode different types of data in a quantum computer.

In quantum machine learning (QML), we need to optimize parameters of a variational quantum ansatz to minimize some specified cost function. How we choose to optimize the parameters is one of the key steps of every QML workflow. Specifically, the choice of different optimizers can highly influence the parameters obtained after an optimization routine. For this reason, in this How-to blog, we will learn how to implement our own custom-built optimizer.

A short introduction to using FlamingPy for creating and visualizing cluster states that are relevant in measurement-based quantum computing.

When using PennyLane to design quantum circuits we need to consider both the sequence of gates that we want to apply and also the kind of output that we want to obtain. At the end of the day, we will get back a quantity through a specific measurement. In this how-to, we go through the different type of measurements offered by PennyLane and we discuss when to use them.

Variational hybrid algorithms often task a quantum processor to prepare, parametrically, a quantum state and a classical computer to optimize these parameters. Whether you are looking for the ground state of a molecule or training a quantum neural network this how-to will guide you through the choice of the most convenient optimizer featured in PennyLane.

If machine learning is interesting, quantum machine learning (QML) is twice as interesting. It’s incredible that you can combine these two fields into one, and it is an area that has seen huge interest and growth in the past few years. In this blog post we will highlight some …

Quantum transforms are an exciting new feature of PennyLane. Learn how transforms can help you construct and manipulate quantum functions with ease!

QML algorithms often involve a set of trainable parameters, in most cases, these parameters need to be initialized in a certain fashion before, say, starting a gradient descent. In this how-to, we’ll see how to do precisely that.

Implementing your own gates in PennyLane does not require deep manipulation of the code base but is as easy as listing some core properties of the operation to be added. Even easier, you can construct your own templates to create structured special purpose circuits and simplify your workflow.

One of the advantages of PennyLane is that it can be run on many different simulators and real quantum devices from a variety of external providers through our Plugins, as well as Xanadu’s own devices! In this how-to, we will show you how to create a simple program in …

Already built your fancy quantum circuit and wanna have a look at it? PennyLane supports visualizing quantum circuits once you have defined them.

Near-term quantum devices are noisy, a fact we need to account for in our simulations. In this how-to guide, we give three simple methods for simulating noise: classical jitter, built-in PennyLane support for mixed states and quantum channels, and finally plugins to Cirq and Qiskit.

Constructing different types of Hamiltonians is easy in Pennylane. In this how-to, we will show you how to easily create Hamiltonians for problems in combinatorial optimization, quantum many-body physics and quantum chemistry.

In PennyLane, QNodeCollection execution can be parallelized across remote devices. In this how-to guide, we demonstrate the advantage of parallel execution through the example of qubit state tomography.

In PennyLane, we can easily construct VQE and QAOA-like models. However, the flexibility of PennyLane allows much more interesting models to be constructed. In this how-to, we’ll show you how to chain two QNodes together.

PennyLane QNodes don’t just support computing the derivative—they also support computing the second derivative. Further, the second derivative can be computed on both simulators and quantum hardware. In this how-to, we’ll show you how you can extract the Hessian of a hybrid model using PennyLane in three …