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st released as an open source python framework in july 2018 the project was led by dave bacon craig gidney and other members of google s quantum computing team with the goal of providing a circuit level programming tool that could precisely target the constraints of near term quantum hardware unlike higher level frameworks that abstract away device topology cirq was designed from the start to give users fine grained control over qubit placement gate scheduling and moment structure which reflects google s emphasis on hardware aware algorithm design cirq s development was closely tied to google s superconducting qubit processors when google announced its quantum supremacy experiment on the 53 qubit sycamore processor in october 2019 the random circuit sampling benchmarks were implemented in cirq the framework s gridqubit type directly maps to the 2d grid layout of google s chips making it the natural choice for anyone targeting google hardware cirq reached version 1 0 in july 2022 signaling api stability the framework lives under the quantumlib github organization alongside related google quantum projects such as openfermion tensorflow quantum and stim a fast clifford circuit simulator for quantum error correction research cirq serves as the circuit construction backend for tensorflow quantum and integrates with the google quantum computing service for cloud access to google s processors as of 2026 cirq has over 4 000 github stars and a steady contributor community though it is smaller than qiskit s its niche is strongest among researchers working directly with google hardware studying quantum error correction or building low level circuit optimizations cirq s sympy based parameterization system and support for parameter sweeps make it particularly well suited for variational algorithm research where many circuit evaluations are needed installation pip install cirq for google cloud quantum computing service access pip install cirq google key imports import cirq from cirq import circuit linequbit gridqubit simulator qubit types cirq has three qubit types the type you use often depends on hardware layout type use case cirq linequbit n 1d chain good for algorithms cirq gridqubit row col 2d grid matches google hardware cirq namedqubit name arbitrary label readable circuits q0 q1 cirq linequbit range 2 q cirq gridqubit 0 1 q cirq namedqubit ancilla gates gates are applied to qubits using the on method or by calling the gate directly cirq h q0 hadamard cirq x q0 pauli x cirq y q0 pauli y cirq z q0 pauli z cirq s q0 s gate cirq t q0 t gate cirq cnot q0 q1 controlled not cirq cz q0 q1 controlled z cirq swap q0 q1 swap cirq toffoli q0 q1 q2 toffoli rotation gates cirq rx rads 1 57 q0 rx rotation cirq ry rads 1 57 q0 ry rotation cirq rz rads 1 57 q0 rz rotation parameterised gates import sympy theta sympy symbol theta cirq rx theta q0 circuit construction circuit cirq circuit add individual moments circuit append cirq h q0 circuit append cirq h q0 cirq x q1 parallel ops moment by moment control circuit append cirq moment cirq h q0 cirq h q1 one liner circuit cirq circuit cirq h q0 cirq cnot q0 q1 measurement measure specific qubits with a key cirq measure q0 q1 key result measure all at end circuit append cirq measure q0 q1 key m simulators simulator statevector sim cirq simulator result sim simulate circuit print result final_state_vector densitymatrixsimulator sim cirq densitymatrixsimulator result sim simulate circuit sample based like hardware sim cirq simulator result sim run circuit repetitions 1024 print result histogram key result print result measurements common patterns bell state q0 q1 cirq linequbit range 2 circuit cirq circuit cirq h q0 cirq cnot q0 q1 cirq measure q0 q1 key m parameterised sweep import sympy numpy as np theta sympy symbol theta circuit cirq circuit cirq ry theta q0 cirq measure q0 key m sweep cirq linspace theta start 0 stop 2 np pi length 10 results cirq simulator run_sweep circuit params sweep repetitions 100 noise model noise cirq constantqubitnoisemodel cirq depolarize 0 01 sim cirq densitymatrixsimulator noise noise circuit visualisation print circuit ascii diagram circuit to_text_diagram grover s search 2 qubit a minimal grover s algorithm that searches for the marked state 11 the oracle uses a cz gate to flip the phase of the target state and the diffuser applies the standard inversion about the mean operator for two qubits a single grover iteration is sufficient to reach the answer with certainty import cirq q0 q1 cirq linequbit range 2 initialize put both qubits into equal superposition init cirq h q0 cirq h q1 oracle mark 11 by flipping its phase with cz oracle cirq cz q0 q1 diffuser inversion about the mean apply h x to both qubits then cz then x h again diffuser cirq h q0 cirq h q1 cirq x q0 cirq x q1 cirq cz q0 q1 cirq x q0 cirq x q1 cirq h q0 cirq h q1 measurement meas cirq measure q0 q1 key result build full circuit init oracle diffuser measure circuit cirq circuit init oracle diffuser meas print circuit run and verify 11 is found with high probability sim cirq simulator result sim run circuit repetitions 1024 print result histogram key result expected output counter 3 1024 3 in decimal is 11 vqe on h2 with cirq and openfermion a simplified variational quantum eigensolver vqe that estimates the ground state energy of molecular hydrogen this example uses openfermion for the hamiltonian and a single parameter hardware efficient ansatz in practice you would use a classical optimizer such as scipy optimize minimize here we sweep over parameter values to illustrate the energy landscape pip install cirq openfermion openfermionpyscf import cirq import sympy import numpy as np import openfermion from openfermion chem import moleculardata from openfermionpyscf import run_pyscf 1 build the h2 hamiltonian at bond length 0 74 å geometry h 0 0 0 0 0 0 h 0 0 0 0 0 74 basis sto 3g multiplicity 1 charge 0 molecule moleculardata geometry basis multiplicity charge molecule run_pyscf molecule get the qubit hamiltonian via jordan wigner transformation hamiltonian openfermion jordan_wigner molecule get_molecular_hamiltonian hamiltonian_sparse openfermion get_sparse_operator hamiltonian 2 build a simple parameterised ansatz on 2 qubits q0 q1 cirq linequbit range 2 theta sympy symbol theta ansatz cirq circuit cirq x q0 start in 10 single excitation reference cirq ry theta q1 parameterised rotation cirq cnot q0 q1 entangle 3 sweep theta and compute expectation value ψ θ h ψ θ sim cirq simulator angles np linspace 0 2 np pi 50 energies for angle in angles resolver cirq paramresolver theta angle result sim simulate ansatz param_resolver resolver state result final_state_vector pad state to match hamiltonian dimension 4 qubits for sto 3g h2 full_state np zeros hamiltonian_sparse shape 0 dtype complex for i amp in enumerate state full_state i amp energy np real full_state conj hamiltonian_sparse toarray full_state energies append energy min_energy min energies best_angle angles np argmin energies print f vqe minimum energy min_energy 6f ha at theta best_angle 4f print f exact ground state energy molecule fci_energy 6f ha note the sto 3g h2 hamiltonian acts on 4 qubits but our 2 qubit ansatz explores only a subspace a production vqe would use all 4 qubits with a more expressive ansatz such as uccsd and a classical optimizer loop running on google hardware via cirq google to execute circuits on google s quantum processors you use the cirq_google module and the quantum engine api access requires a google cloud project that has been approved for the quantum computing service import cirq import cirq_google authenticate and create an engine client requires google_cloud_project env var or explicit project id engine cirq_google get_engine project_id your gcp project list available processors processors engine list_processors for p in processors print p processor_id select a processor by id use an id from list_processors processor engine get_processor your processor id device processor get_device build a circuit using gridqubits that match the device layout q0 cirq gridqubit 5 3 q1 cirq gridqubit 5 4 circuit cirq circuit cirq h q0 cirq cnot q0 q1 cirq measure q0 q1 key m validate the circuit against the device s connectivity and gate set device validate_circuit circuit run on hardware sampler processor get_sampler result sampler run circuit repetitions 1000 print result histogram key m native gates on sycamore family processors google s processors support a specific native gate set the sycamore gate cirq_google syc is a two qubit gate native to google s hardware and more natural than cnot for these devices the sycamore gate a native fsim gate with specific angles syc cirq_google syc print cirq unitary syc round 3 use the sycamore gate directly in a circuit circuit cirq circuit cirq_google syc q0 q1 cirq measure q0 q1 key m compile an arbitrary circuit to the device s native gate set the old optimized_for_sycamore helper was deprecated and removed use the transformer api instead compiled cirq optimize_for_target_gateset circuit gateset cirq_google sycamoretargetgateset serialization cirq supports json serialization for circuits making it straightforward to save load and share circuit definitions import cirq q0 q1 cirq linequbit range 2 circuit cirq circuit cirq h q0 cirq cnot q0 q1 cirq measure q0 q1 key m serialize to json string json_str cirq to_json circuit print json_str write directly to a file cirq to_json circuit my_circuit json read back from a file loaded_circuit cirq read_json my_circuit json assert circuit loaded_circuit read from a json string loaded_from_str cirq read_json json_string json_str assert circuit loaded_from_str serialization covers most built in cirq objects including gates qubits circuits and noise models custom gate types need to implement the cirq json_serialization protocol or be registered with a custom resolver cirq vs other frameworks google hardware access cirq is the only framework with first party support for google quantum ai processors qiskit targets ibm hardware and pennylane connects to multiple backends through plugins but has no native google integration if you are running experiments on sycamore family chips cirq is the required tool moment based circuit construction cirq organizes operations into discrete moment objects giving explicit control over which gates execute in parallel qiskit builds circuits gate by gate and relies on transpiler passes to schedule parallelism pennylane uses a tape based model focused on differentiability cirq s approach is particularly useful when optimizing circuit depth for noisy hardware hardware topology matching with gridqubit cirq s gridqubit row col type maps directly to the 2d grid layout of superconducting processors letting you reason about qubit connectivity at the circuit design stage qiskit handles topology mapping during transpilation and pennylane abstracts physical layout entirely for hardware aware algorithm design cirq s model reduces the gap between logical and physical circuits native sycamore gate cirq provides cirq_google syc the fsim family gate native to google s processors as a first class object other frameworks would need to decompose this gate or define it manually working with the native gate set avoids unnecessary decomposition overhead and gives more accurate noise modeling parameter sweeps for variational algorithms cirq s run_sweep and linspace points zip sweep objects let you evaluate a parameterized circuit across many parameter values in a single call with efficient batching on both simulators and hardware qiskit supports parameter binding but without the same sweep abstraction pennylane takes a different approach integrating directly with autodiff frameworks pytorch jax for gradient computation which is more natural for machine learning workloads ecosystem scope qiskit has the largest ecosystem transpiler runtime primitives aer noise simulation and a broad provider network pennylane excels at differentiable quantum programming and hybrid quantum classical ml cirq is more focused and lower level which makes it powerful for researchers who want precise circuit control but means it has fewer high level abstractions for application development learning resources cirq documentation official guides api reference and tutorials from google quantum ai cirq on github source code issue tracker and example notebooks hello world with cirq build and simulate your first cirq circuit step by step variational algorithms in cirq hands on tutorial covering vqe and qaoa implementations cirq tutorials this page is the syntax these are the 13 tutorials where you run it quantum chemistry with openfermion advanced 16 min read noise modeling in cirq intermediate 13 min read simulating google sycamore circuits with cirq advanced 70 minutes getting started with cirq beginner 25 min read all 13 cirq 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 classiq high level quantum algorithm synthesis and optimization platform where to go next the reference index puts cirq 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 qubit types 05 gates 06 circuit construction 07 measurement 08 simulators 09 common patterns 10 circuit visualisation 11 cirq vs other frameworks 12 learning resources 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 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