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description=Side-by-side code comparisons for migrating between quantum computing frameworks - Qiskit, Cirq, PennyLane, Braket, PyQuil, and more.;
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forms 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 migration guide developer reference quantum framework migration guide you may want to switch frameworks when targeting different hardware when a new framework better fits your use case or when your team standardises on a single sdk 6 operations three frameworks each 5 common migration paths runnable python this guide shows the same operations in qiskit cirq and pennylane side by side so you can translate your existing code without guesswork with additional notes on braket pyquil and tket as a universal compiler layer full syntax comparison framework reference in this guide 11 sections 01 migration paths 02 side by side code 03 bell state 04 parameterized gates 05 run on simulator 06 statevector 07 noise model 08 vqe variational 09 key differences 10 tket compiler 11 braket pyquil 01 common migration paths not every migration is equally common these are the five transitions practitioners encounter most often with the main reasons behind each qiskit cirq moving to google hardware sycamore willow or needing lower level control over qubit placement and gate timing cirq exposes individual qubit objects and moment structure that qiskit abstracts away the main cost is rewriting qubit indexing from integers to linequbit or gridqubit objects cirq reference qiskit pennylane adding quantum machine learning hybrid classical quantum models or differentiable programming pennylane circuits are python functions decorated with qml qnode which makes gradient computation via parameter shift rules or autograd feel natural gate names are mostly one to one qc h 0 becomes qml hadamard wires 0 pennylane reference qiskit braket deploying on aws infrastructure or accessing hardware from ionq rigetti quera and iqm through a single sdk braket uses a circuit class with method chaining gate names differ slightly cx becomes cnot and parameterized circuits use freeparameter instead of qiskit s parameter braket reference pyquil qiskit rigetti s public cloud access has been curtailed and the ibm ecosystem is significantly larger the main concept change is from quil program list of quil instructions to qiskit quantumcircuit a structured circuit object classical registers and measurement are handled differently qiskit requires explicit classicalregister allocation qiskit reference any framework tket adding hardware agnostic circuit optimisation without abandoning your current framework tket sits as a compilation layer import your qiskit or cirq circuit apply optimisation passes and submit to any supported backend you do not need to rewrite your circuit construction logic only add the compilation step tket reference 02 side by side code examples each operation below is shown in qiskit cirq and pennylane all examples are runnable python using standard library versions as of 2026 1 create a bell state a bell state requires a hadamard on qubit 0 followed by a cnot the structural difference here is that cirq uses qubit objects while qiskit and pennylane use integer indices or wire labels qiskit from qiskit import quantumcircuit qc quantumcircuit 2 2 qc h 0 qc cx 0 1 qc measure 0 1 0 1 cirq import cirq q0 q1 cirq linequbit range 2 circuit cirq circuit cirq h q0 cirq cnot q0 q1 cirq measure q0 q1 key result pennylane import pennylane as qml dev qml device default qubit wires 2 qml qnode dev def bell_state qml hadamard wires 0 qml cnot wires 0 1 return qml probs wires 0 1 2 parameterized rotation gate rx parameterized circuits are handled differently in each framework qiskit uses a parameter symbol bound at execution time cirq uses sympy symbols resolved via paramresolver pennylane accepts a plain python float directly and autograd tracks it automatically qiskit from qiskit import quantumcircuit from qiskit circuit import parameter theta parameter theta qc quantumcircuit 1 qc rx theta 0 bind the parameter before running bound_qc qc assign_parameters theta 1 0472 print bound_qc cirq import cirq import sympy theta sympy symbol theta q0 cirq linequbit 0 circuit cirq circuit cirq rx theta q0 resolve the symbol before simulation resolver cirq paramresolver theta 1 0472 resolved cirq resolve_parameters circuit resolver print resolved pennylane import pennylane as qml from pennylane import numpy as np dev qml device default qubit wires 1 qml qnode dev def rx_circuit theta qml rx theta wires 0 return qml expval qml pauliz 0 plain float works autograd tracks it theta_val np array 1 0472 requires_grad true print rx_circuit theta_val print qml grad rx_circuit theta_val 3 run on simulator and get counts each framework has its own simulator api qiskit uses aersimulator from qiskit aer cirq s built in simulator uses repetitions pennylane returns exact probability distributions by default use shots and qml counts to get a sample based histogram qiskit from qiskit import quantumcircuit transpile from qiskit_aer import aersimulator qc quantumcircuit 2 2 qc h 0 qc cx 0 1 qc measure 0 1 0 1 sim aersimulator compiled transpile qc sim result sim run compiled shots 1024 result print result get_counts cirq import cirq q0 q1 cirq linequbit range 2 circuit cirq circuit cirq h q0 cirq cnot q0 q1 cirq measure q0 q1 key result sim cirq simulator result sim run circuit repetitions 1024 print result histogram key result pennylane import pennylane as qml dev qml device default qubit wires 2 qml qnode dev shots 1024 def bell_counts qml hadamard wires 0 qml cnot wires 0 1 return qml counts wires 0 1 print bell_counts 4 statevector simulation getting the full statevector is useful for debugging and small circuit analysis qiskit provides a statevector class in qiskit quantum_info cirq s simulate returns a result object with final_state_vector pennylane returns the statevector via qml state as a numpy array qiskit from qiskit import quantumcircuit from qiskit quantum_info import statevector qc quantumcircuit 2 qc h 0 qc cx 0 1 sv statevector qc print sv data 0 70710678 0 j 0 0 j 0 0 j 0 70710678 0 j print sv probabilities_dict cirq import cirq q0 q1 cirq linequbit range 2 circuit cirq circuit cirq h q0 cirq cnot q0 q1 sim cirq simulator result sim simulate circuit print result final_state_vector 0 707 0 j 0 0 j 0 0 j 0 707 0 j pennylane import pennylane as qml dev qml device default qubit wires 2 qml qnode dev def get_state qml hadamard wires 0 qml cnot wires 0 1 return qml state sv get_state print sv 0 707 0 j 0 0 j 0 0 j 0 707 0 j 5 depolarizing noise model noise models simulate realistic hardware behaviour in software qiskit uses noisemodel from qiskit_aer noise injected into aersimulator cirq s densitymatrixsimulator applies noise as channels on the circuit pennylane uses the default mixed device which works with density matrices natively qiskit from qiskit import quantumcircuit transpile from qiskit_aer import aersimulator from qiskit_aer noise import noisemodel depolarizing_error noise_model noisemodel dep_err depolarizing_error 0 01 1 noise_model add_all_qubit_quantum_error dep_err h rx ry rz sim aersimulator noise_model noise_model qc quantumcircuit 1 1 qc h 0 qc measure 0 0 result sim run transpile qc sim shots 1024 result print result get_counts cirq import cirq q0 cirq linequbit 0 insert depolarizing noise after each gate noisy_circuit cirq circuit cirq h q0 cirq depolarize p 0 01 q0 cirq measure q0 key m densitymatrixsimulator handles mixed states sim cirq densitymatrixsimulator result sim run noisy_circuit repetitions 1024 print result histogram key m pennylane import pennylane as qml default mixed supports noise channels dev qml device default mixed wires 1 qml qnode dev def noisy_circuit qml hadamard wires 0 apply depolarizing channel p total error qml depolarizingchannel 0 01 wires 0 return qml probs wires 0 print noisy_circuit slightly perturbed from ideal 0 5 0 5 6 vqe variational circuit optimisation variational algorithms minimise a cost function by optimising circuit parameters qiskit uses its estimator primitive with scipy s minimize cirq computes expectation values manually via simulate and expectation_values_from_state_vector pennylane s gradientdescentoptimizer uses parameter shift gradients automatically qiskit import numpy as np from scipy optimize import minimize from qiskit import quantumcircuit from qiskit circuit import parameter from qiskit quantum_info import sparsepauliop from qiskit_aer primitives import estimator theta parameter theta qc quantumcircuit 1 qc ry theta 0 minimise expectation of z ground state 1 observable sparsepauliop z estimator estimator def cost params job estimator run qc observable parameter_values params return job result values 0 result minimize cost x0 0 1 method cobyla print optimal theta result x print min energy result fun cirq import numpy as np import cirq from scipy optimize import minimize q0 cirq linequbit 0 sim cirq simulator def cirq_cost params theta params 0 circuit cirq circuit cirq ry theta q0 result sim simulate circuit sv result final_state_vector expectation of z z a0 2 a1 2 probs np abs sv 2 return float probs 0 probs 1 result minimize cirq_cost x0 0 1 method cobyla print optimal theta result x print min energy result fun pennylane import pennylane as qml from pennylane import numpy as np dev qml device default qubit wires 1 qml qnode dev def cost_circuit theta qml ry theta wires 0 return qml expval qml pauliz 0 parameter shift gradient is automatic opt qml gradientdescentoptimizer stepsize 0 4 theta np array 0 1 requires_grad true for step in range 50 theta cost opt step_and_cost cost_circuit theta print optimal theta float theta print min energy float cost 03 key conceptual differences these are the mental model shifts that trip up developers most often when switching frameworks getting these right prevents most migration bugs concept qiskit cirq pennylane qubit representation integer index 0 1 2 within a quantumcircuit qubit objects linequbit 0 gridqubit 0 1 integer wire labels passed to each gate call circuit execution backend run transpile qc backend or sampler estimator primitives cirq simulator simulate circuit or run circuit repetitions n calling the decorated qml qnode function like a regular python function measurement explicit qc measure writes to classical bits classical register required cirq measure q key m result accessed by key name declared in the return statement qml probs qml expval qml sample parameterisation parametervector or parameter bound with assign_parameters sympy symbol resolved with cirq paramresolver direct python float or pennylane numpy array autograd tracks automatically transpilation transpile qc backend maps to device gate set and topology manual decomposition or cirq google for sycamore targets device plugin handles mapping user rarely calls transpilation directly gradients via qiskit machine learning not built into the core runtime not built in manual finite differences or parameter shift via third party tools first class parameter shift adjoint and backpropagation differentiation built in 04 tket as a universal compiler layer tket from quantinuum can sit between any high level framework and any hardware backend instead of rewriting your circuits you import them into tket apply optimisation passes and submit to whichever backend you need this means you can keep writing qiskit or cirq code and still benefit from tket s compiler passes write your circuit in qiskit cirq or any supported framework convert to tket using the appropriate extension pytket qiskit pytket cirq optimise using tket s sequencepass and pass manager submit to any backend via tket s backend plugins tket via pytket qiskit from qiskit import quantumcircuit from pytket extensions qiskit import qiskit_to_tk aerbackend from pytket passes import sequencepass fullpeepholeoptimise decomposeboxes 1 write the circuit in qiskit as normal qc quantumcircuit 2 2 qc h 0 qc cx 0 1 qc measure 0 1 0 1 2 convert to a tket circuit tk_circ qiskit_to_tk qc 3 apply optimisation passes optimiser sequencepass decomposeboxes fullpeepholeoptimise optimiser apply tk_circ 4 submit to a backend here aer via tket backend aerbackend compiled backend get_compiled_circuit tk_circ result backend run_circuit compiled n_shots 1024 print result get_counts install with pip install pytket pytket qiskit for cirq circuits use pytket cirq and cirq_to_tk backends exist for ibm ionq quantinuum braket rigetti and more 05 braket and pyquil migration notes migrating to amazon braket braket has its own circuit class that is structurally similar to qiskit but with different naming conventions and a fluent chaining api quantumcircuit n n becomes circuit no classical bits declared upfront qc cx 0 1 becomes circuit cnot 0 1 qc rx theta 0 becomes circuit rx 0 theta qubit index first angle second parameterized gates use freeparameter theta instead of qiskit s parameter theta measurement is implicit no measure call required for the local simulator circuits are run via device run circuit shots n result braket bell state from braket circuits import circuit from braket devices import localsimulator circuit circuit h 0 cnot 0 1 device localsimulator result device run circuit shots 1024 result print result measurement_counts full braket reference migrating from pyquil pyquil uses quil an assembly like instruction set migrating to qiskit or cirq means moving from a list of instruction objects to a structured circuit abstraction pyquil s program is a list of gate and measurement instructions qiskit s quantumcircuit is a graph based circuit object gates are imported individually from pyquil gates e g h 0 cnot 0 1 in qiskit they are methods on the circuit object classical registers must be declared explicitly in qiskit quantumcircuit 2 2 pyquil s quilc compiler is called via get_qc qiskit uses transpile qc backend pyquil s wavefunctionsimulator corresponds to qiskit s statevector class parametric gates use p declare angle real qiskit uses parameter theta pyquil bell state for comparison from pyquil import program from pyquil gates import h cnot measure p program ro p declare ro bit 2 p h 0 p cnot 0 1 p measure 0 ro 0 p measure 1 ro 1 p wrap_in_numshots_loop 1024 full pyquil reference related resources full syntax comparison qiskit cirq pennylane braket pyquil tket cheat sheets hardware...