PennyLane
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Catalyst, our next-generation compilation engine, brings just-in-time compilation to quantum programming. Automatically compile hybrid programs for execution on CPUs, GPUs, and QPUs, and unlock new ways of programming quantum computers.

PennyLane v0.29 is out, with features including pulse programming, trainable special unitary operations, new hardware-compatible gradient methods, and more.

Quantum coding challenges are back for QHack 2023, and they’re more exciting than ever. This year, you can help two intrepid heroes save the day in a galaxy-wide romp featuring lazy coworkers, photocopiers, timbits, and evil spin chains. Read on to learn more!

QHack 2023 is a free event running from February 13th–28th, and featuring top-notch scientific talks, an array of coding challenges and an open hackathon.

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!

PennyLane v0.28 is out, with features including custom measurements, ZX-calculus diagrams, PubChem database access, and more.

Katharine Hyatt (Scientist @ Amazon Braket) and Daniela Becker (Senior Product Manager @ Amazon Braket) demonstrate how using the adjoint differentiation method on SV1 can deliver a strong performance improvement in finding molecular ground state energies.

In celebration of PennyLane’s fourth birthday, more than 800 coders from over 70 different countries joined us for the first-ever PennyLane Code Camp — a three-week virtual trip to the great outdoors — to tackle increasingly difficult quantum coding challenges and compete for prizes.

PennyLane v0.27 is out, with features including downloadable quantum datasets, adaptive optimization, automatic interfacing, and more.

The PennyLane Code Camp, our virtual trip to the great outdoors has started! For the next three weeks, quantum coders will test their skills against increasingly difficult coding challenges to learn, earn badges and compete for prizes.

We discuss how hybrid algorithms can help the design of quantum communication networks. To demonstrate, we share recent results from a collaboration between Xanadu and the University of Illinois at Urbana-Champaign, in which we show that variational quantum optimization can find maximally nonlocal correlations in noisy quantum networks.

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.

PennyLane v0.26 is out, with features including classical shadows, qutrits, the QNSPSA optimizer, and more.

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!

Unitary Fund would like to invite the PennyLane community to take the Quantum Open Source Software (QOSS) Survey. Help us collect a dataset representative of everyone who codes for quantum technologies, to better serve users of the quantum computing ecosystem.

PennyLane v0.25 is out, with features including a resource estimation module, new measurements, differentiable error mitigation, upgrades to operator arithmetic, and more.

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

Learn how PennyLane’s lightning.gpu device uses the NVIDIA cuQuantum SDK to speed up the simulation of quantum circuits.

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.

PennyLane v0.24 is out, with features including a brand new quantum information module, parameter broadcasting, improvements to the JAX JIT interface, and more.

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.

We are excited to announce that our high-performance simulators lightning.qubit and lightning.gpu are now supported on Amazon Braket Hybrid Jobs! 🎉 With Amazon Braket Hybrid jobs, you can run hybrid algorithms using PennyLane without needing to manage the underlying infrastructure. Whether you’re using a notebook instance, a local …

PennyLane v0.23 is out, with features including automatic circuit cutting, a unification of our quantum chemistry packages, new templates and compilation transforms, and more.

Every year, QHack brings together people from all over the world to celebrate quantum computing. QHack 2022 was bigger, better, and groovier than ever; featuring world-class live stream talks, coding challenges, an open-hackathon, a design competition, a meme contest, and life-changing prizes. Read on for: QHack 2022 at a glance …

PennyLane v0.22 is out, with features including executing large circuits with fewer qubits, differentiable mid-circuit measurements, a new high-performance GPU simulator, tools for quantum debugging, and more.

Benchmarks, exponential speedups, expressivity…the question of how we measure if a quantum machine learning method is “good” can be a lot more tricky than one may think.

PennyLane v0.21 is out, with methods to reduce qubit counts, experimental Qiskit Runtime support, improved quantum aware optimizers, better JAX support, and more.

PennyLane v0.20 is out, with a new graphical circuit drawer, more quantum-aware optimizers, faster performance, smarter circuit decompositions, general hardware gradient support, and more.

Announcing PennyLane and Mitiq integration for error mitigation of noisy circuits

We share two short lessons regarding the leading quantum algorithms for quantum chemistry: the variational quantum eigensolver and quantum phase estimation.

PennyLane v0.19 is out, with an included differentiable Hartree-Fock solver, error mitigation using Mitiq, powerful new transforms, improved support for batch execution, and much more.

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 …

PennyLane v0.18 is out, with an included high-performance simulator, backpropagation with PyTorch, improved quantum-aware optimizers, the ability to define custom quantum gradients, and much more.

Wrapping up PennyLane: Code Together 🙌🙌 What a tight race it was! The contributors to Code Together were so excited about solving the issues that they raced head-to-head to the finish line to be the first to get their pull request merged. Some even managed to solve several issues. Something else …

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

We’re excited to announce PennyLane v0.17. It’s more powerful than ever with support for circuit compilation routines, gradient transforms, Docker containerization, along with many new and improved templates, optimizers, and quality-of-life updates.

Want to get involved with quantum open-source software, but don’t know how to begin? Good news: the PennyLane team will now be offering a regular series of community calls to help you on your journey. Each week, join the PennyLane dev team, along with our growing network of contributors …

Announcing PennyLane: Code Together 🙌🙌 This is your chance to join the quantum software community, supercharge your coding skills, interact with the PennyLane team, and—most importantly—win some awesome swag! Join us on GitHub August 16th-27th. Participation is simple: be the first to solve an open issue with the label …

We’re excited to announce PennyLane v0.16. This release comes with many new modules, features, and improvements. Some highlights are the new Fourier and kernels modules, resource estimation, new transforms, and new operations.

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.

PennyLane’s very own Maria Schuld recently gave a talk at the inaugural QML Meetup, summarizing some critical quantum machine learning insights. Check out the video below to learn why variational quantum models have a scaling problem, why the way we encode data must become more than an incidental choice …

Interested in hearing more about what it’s like to develop PennyLane? On the latest Quantum Computing Now podcast, Ethan Hansen sits down with PennyLane developer Jack Ceroni to hear about his experiences getting started with quantum, how he got into quantum software development, and answer the question “what in …

QHack 2021, the quantum machine learning (QML) hackathon, ran earlier this year from 17-26 February. A big portion of the event was the QML Challenge Leaderboard, where hundreds of teams raced to solve QML programming problems in order to claim hardware credits to help with their Open Hackathon projects. Solutions …

We are excited to announce that PennyLane now natively supports quantum computing devices provided by IonQ! 🎉🍾 IonQ is the leading provider of quantum computers based on trapped ions. With long coherence times and all-to-all connectivity, trapped ions make a great hardware substrate for testing complex quantum computing algorithms, like those …

We’re excited to announce PennyLane v0.15, which comes with many new and exciting features. These include a new hardware device integration, more flexible shot control, brand new operations, and new methods for working with more powerful quantum circuits.

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 …

Part hackathon, part fan expo, part scientific conference, QHack offered a new kind of experience in the quantum space. Like all the best events, it had a teaser trailer, a vintage poster, its own theme music (imagine Daft Punk remixed the Knight Rider intro music), and a top-notch swag game …