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pennylane reference dictionary skip to main content quantumcomputing courses com courses all courses course platforms coursera edx udemy brilliant hardware providers google quantum ai ibm quantum ionq quantinuum amazon braket azure quantum quera rigetti d wave tutorials all tutorials hello world qiskit hello world cirq hello world pennylane hello world braket quantum gates grover s algorithm shor s algorithm reference all frameworks qiskit cirq pennylane amazon braket pyquil tket d wave ocean q explore learn learning paths prerequisites programming guide case studies glossary books quantum news podcasts tools bloch sphere quantum pinball guides algorithm guide hardware guide qubit types framework comparison migration guide language timeline cheat sheets career events 2026 jobs careers certifications salary guide universities interview prep faq troubleshooting about about team search browse courses home reference pennylane framework reference pennylane xanadu s framework for quantum machine learning and differentiable quantum computing python version 0 45 x 11 sections quick reference for pennylane devices qnodes gradients templates and quantum machine learning patterns in this guide 11 sections 01 background and history 02 installation 03 key imports 04 core concepts 05 gates 06 measurements 07 gradients 08 variational quantum eigensolver vqe 09 templates 10 common patterns 11 pennylane vs qiskit and cirq at a glance language python version 0 45 x install pip install pennylane copy docs docs pennylane ai source pennylaneai pennylane background and history pennylane was created by xanadu a canadian quantum computing company founded in 2016 by christian weedbrook the framework was first released in november 2018 with a specific focus that distinguished it from existing tools differentiable quantum computing pennylane treats quantum circuits as differentiable computational graphs enabling automatic gradient computation through quantum operations using techniques like the parameter shift rule this design made it the first framework purpose built for quantum machine learning and variational quantum algorithms xanadu s initial research focus was photonic quantum computing and pennylane was designed to be hardware agnostic from the outset its plugin architecture allows the same quantum circuit code to run on backends from ibm via pennylane qiskit google via pennylane cirq amazon via pennylane braket and xanadu s own photonic hardware this cross platform approach filled an important gap in the ecosystem since most other frameworks at the time were tightly coupled to a single vendor s hardware pennylane s integration with classical machine learning frameworks has been a central feature it supports automatic differentiation through numpy via autograd pytorch tensorflow and jax allowing quantum circuits to be embedded as layers within standard deep learning pipelines the lightning qubit and lightning gpu high performance simulators developed alongside pennylane provide fast local execution for development and research the project has grown steadily since its release as of 2026 pennylane has over 3 000 github stars and a large library of tutorials demos and educational content hosted at pennylane ai xanadu has invested heavily in community building running the qhack quantum hackathon series and maintaining an active discussion forum pennylane is widely used in academic quantum machine learning research and has become one of the most commonly taught quantum frameworks in university courses alongside qiskit development continues at a rapid pace with frequent releases adding new features optimizations and hardware backend support installation pip install pennylane backend plugins optional pennylane s default qubit covers most needs pip install pennylane qiskit ibm backends pip install amazon braket pennylane plugin amazon braket backends pip install pennylane lightning gpu gpu accelerated key imports import pennylane as qml import pennylane numpy as np use this instead of plain numpy for gradients core concepts pennylane differs from qiskit cirq in a key way circuits are defined inside python functions decorated with qml qnode and they support automatic differentiation for training device a device is the backend that executes your circuit simulator or real hardware dev qml device default qubit wires 2 built in simulator dev qml device default qubit wires a b named wires dev qml device lightning qubit wires 4 fast c simulator dev qml device qiskit aer wires 2 qiskit aer backend qnode a qnode wraps a quantum function and binds it to a device this is pennylane s equivalent of a circuit execution combined qml qnode dev def circuit params qml ry params 0 wires 0 qml cnot wires 0 1 return qml expval qml pauliz 0 result circuit 0 5 gates qml hadamard wires 0 h qml paulix wires 0 x not qml pauliy wires 0 y qml pauliz wires 0 z qml s wires 0 s gate qml t wires 0 t gate qml cnot wires 0 1 controlled not qml cz wires 0 1 controlled z qml swap wires 0 1 swap qml toffoli wires 0 1 2 toffoli rotation gates angles in radians qml rx theta wires 0 qml ry theta wires 0 qml rz theta wires 0 arbitrary single qubit unitary qml rot phi theta omega wires 0 z y z euler decomposition measurements pennylane measurements are return values from qnodes a qnode returns one measurement and that return value chooses which qml qnode dev def circuit qml hadamard wires 0 return qml expval qml pauliz 0 expectation value the alternatives each valid in that same return position qml var qml pauliz 0 variance qml probs wires 0 1 probability distribution qml sample qml pauliz 0 raw samples qml counts wires 0 1 measurement counts qml state full statevector gradients this is pennylane s main advantage circuits are differentiable automatic gradient via parameter shift rule grad_fn qml grad circuit gradient grad_fn 0 5 jax interface dev qml device default qubit wires 2 qml qnode dev interface jax def circuit params qml ry params 0 wires 0 return qml expval qml pauliz 0 import jax import jax numpy as jnp gradient jax grad circuit jnp array 0 5 variational quantum eigensolver vqe import pennylane as qml from pennylane import numpy as np define a hamiltonian h qml hamiltonian 0 5 0 5 qml pauliz 0 qml pauliz 1 dev qml device default qubit wires 2 qml qnode dev def ansatz params qml ry params 0 wires 0 qml ry params 1 wires 1 qml cnot wires 0 1 return qml expval h params np array 0 1 0 2 requires_grad true opt qml gradientdescentoptimizer 0 1 for _ in range 100 params opt step ansatz params print ground state energy ansatz params templates pennylane provides pre built circuit templates qml templates basicentanglerlayers weights wires range 4 qml templates stronglyentanglinglayers weights wires range 4 qml templates angleembedding features wires range 4 qml templates amplitudeembedding features wires range 4 normalize true qml templates qaoaembedding features weights wires range 4 common patterns bell state dev qml device default qubit wires 2 qml qnode dev def bell qml hadamard wires 0 qml cnot wires 0 1 return qml probs wires 0 1 print bell 0 5 0 0 0 5 circuit drawing print qml draw circuit 0 5 qml draw_mpl circuit 0 5 matplotlib figure quantum transfer learning qml qnode dev def hybrid_model x weights qml amplitudeembedding x wires range 4 normalize true qml basicentanglerlayers weights wires range 4 return qml expval qml pauliz 0 pennylane vs qiskit and cirq choose pennylane when a project is fundamentally a machine learning problem you need gradients through a circuit you want that circuit sitting inside a pytorch tensorflow or jax model as just another differentiable layer or you are prototyping a variational algorithm and want to swap optimizers freely its plugin architecture also means the same circuit code can target ibm google or amazon hardware without a rewrite choose qiskit instead when the priority is running on ibm hardware specifically and you need deep control over transpilation error mitigation through qiskit runtime or access to ibm s fullest feature set choose cirq when google s sycamore family processors and their native gate set are the target pennylane s device abstraction is convenient for portability but it sits a layer above these vendor sdks rather than replacing the fine grained control they offer on their own hardware pennylane tutorials this page is the syntax these are the 26 tutorials where you run it quantum generative adversarial networks with pennylane advanced 70 minutes active space methods for quantum chemistry in pennylane advanced 40 min pennylane error mitigation zne and pec for nisq circuits intermediate 22 min read noise aware training for variational quantum circuits advanced 70 minutes all 26 pennylane tutorials other python frameworks amazon braket aws s fully managed quantum computing service and python sdk amazon braket sdk unified sdk for quantum computing on amazon braket bloqade quera s framework for neutral atom quantum computing and analog hamiltonian simulation cirq google s python framework for quantum circuits and algorithms where to go next the reference index puts pennylane next to the other 25 frameworks documented here and the comparison page runs the same circuit through each of the major sdks reference index compare frameworks on this page 01 background and history 02 installation 03 key imports 04 core concepts 05 gates 06 measurements 07 gradients 08 variational quantum eigensolver vqe 09 templates 10 common patterns 11 pennylane vs qiskit and cirq read next all 26 framework dictionaries the full index filterable by language the same circuit in every framework side by side syntax across the major sdks framework tutorials step by step hello world guides for every major quantum sdk get one quantum email a week new tutorials courses worth taking and what changed in qiskit cirq pennylane this week no spam unsubscribe anytime email address subscribe 112 courses 220 tutorials 241 glossary terms 26 framework references 34 case studies quantumcomputing courses com free tutorials curated courses framework references and tools for anyone learning quantum computing written and maintained by dr donovan who writes on quantum computing research hardware and industry at quantum zeitgeist learn all courses free tutorials learning paths compare frameworks algorithm guide case studies quantum news reference glossary framework docs hardware guide qubit types history timeline cheatsheets bloch sphere quantum programming careers careers guide salary guide certifications interview questions jobs team training post a job talent pool about about this site editorial policy team faq events 2026 podcasts books contact as an amazon associate i earn from qualifying purchases 2026 hadamard llc quantumcomputingcourses com affiliate disclosure privacy terms cookies we use cookies to improve your experience and track affiliate performance see our cookie policy decline accept