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google quantum ai superconducting quantum platform 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 platforms google quantum ai superconducting google quantum ai first to claim quantum supremacy in 2019 willow 2024 demonstrated below threshold quantum error correction a foundational milestone for fault tolerant quantum computing 105 qubits willow 99 7 gate fidelity below threshold qec cirq sdk explore google quantum ai first circuit tutorial about google quantum ai from quantum supremacy to below threshold error correction google s quantum computing effort began with the xmon qubit architecture and the bristlecone 72 qubit processor before culminating in the 2019 announcement that sycamore had performed a specific sampling task in 200 seconds that google estimated would take a classical supercomputer 10 000 years the result published in nature is the most cited quantum supremacy demonstration to date though classical simulation algorithms have since closed some of that gap in december 2024 google published results from willow a 105 qubit processor demonstrating that surface code logical error rates decrease as the code distance grows past a threshold this is the first experimental confirmation of below threshold error correction meaning adding more physical qubits to the error correcting code actively improves rather than just changes the logical qubit fidelity it is a landmark result for the long term viability of fault tolerant quantum computing the primary sdk for google s hardware is cirq an open source python framework for writing simulating and running quantum circuits cirq is designed around the specific gate sets and connectivity of google s hardware with native support for the fsim gate family that appears on sycamore architecture processors alongside cirq openfermion handles quantum chemistry tensorflow quantum handles quantum machine learning and recirq provides research grade circuit tools used in google s published experiments hardware specifications system specs at a glance specification value current systems willow 105q 2024 sycamore 53q 2019 bristlecone 72q research qubit technology superconducting transmon qubits xmon design two qubit gate fidelity 99 7 on willow readout fidelity 99 connectivity 2d grid nearest neighbor error correction below threshold surface code demonstrated willow 2024 benchmarking method cross entropy benchmarking xeb cloud access google cloud research program application required primary sdk cirq open source python supported sdks cirq openfermion tensorflow quantum recirq google willow 105 qubits google s 2024 flagship processor and the site of the below threshold error correction milestone willow also demonstrated computational speed on a random circuit sampling benchmark that would take classical supercomputers an estimated 10 septillion years current focus for google s research collaboration program google sycamore 53 qubits the processor behind google s 2019 quantum supremacy claim sycamore uses a 2d grid of xmon transmon qubits with tunable couplers it served as the primary research platform for time crystal demonstrations 2021 and many foundational algorithm experiments before willow bristlecone 72 qubits a 2018 research processor that established google s path toward quantum supremacy bristlecone used a rectangular grid layout and demonstrated the scalability of google s xmon qubit fabrication process it is a research predecessor not an active production system use cases where google quantum ai hardware excels quantum error correction research willow s 2024 demonstration that surface code error rates decrease as the code grows past a threshold is the defining result for the platform researchers focused on qec will find no more relevant hardware quantum supremacy experiments cross entropy benchmarking xeb was developed by google to certify that sycamore s output was classically hard to simulate google hardware remains the benchmark for supremacy style circuit sampling experiments quantum chemistry with openfermion openfermion maps fermionic molecular hamiltonians to qubit operators for cirq the combination is google s primary toolchain for variational quantum eigensolver experiments in computational chemistry fermionic simulation google s fsim gate is natively implemented on sycamore family hardware circuits that use fsim directly avoid decomposition overhead making the hardware efficient for fermionic simulation problems quantum machine learning with tensorflow quantum tensorflow quantum integrates cirq circuits into tensorflow s differentiable programming model researchers can define hybrid quantum classical models and train them with standard tf optimizers time crystals and exotic phases google demonstrated a discrete time crystal on sycamore in 2021 showing the platform s strength for studying non equilibrium quantum phases of matter that are difficult to access classically getting started start building with cirq 1 install cirq pip install cirq cirq installs with all simulators included no api key is needed to run circuits against the local simulator for quantum chemistry also install openfermion which now includes the former openfermion cirq integration for qml install tensorflow quantum 2 build and simulate a bell state import cirq define two qubits on a grid q0 q1 cirq linequbit range 2 build a bell state circuit circuit cirq circuit cirq h q0 cirq cnot q0 q1 cirq measure q0 q1 key result print circuit simulate locally simulator cirq simulator result simulator run circuit repetitions 1000 print result histogram key result cirq circuits use explicit qubit objects gridqubit linequbit namedqubit gridqubit matches google s 2d hardware connectivity linequbit is convenient for linear experiments 3 use google s native fsim gate import cirq import numpy as np q0 q1 cirq gridqubit 0 0 cirq gridqubit 0 1 fsim is google s native 2 qubit gate theta phi parameterized fsim cirq fsimgate theta np pi 4 phi np pi 6 circuit cirq circuit fsim q0 q1 cirq measure q0 q1 key m print circuit the fsim gate is native to sycamore family hardware writing circuits directly with fsim avoids decomposition overhead that occurs when using cnot on google hardware see the cirq reference for gate decomposition details 4 add noise modeling for realistic simulation import cirq q0 q1 cirq linequbit range 2 build a noise model approximating sycamore class hardware noise cirq constantqubitnoisemodel cirq depolarize p 0 005 circuit cirq circuit cirq h q0 cirq cnot q0 q1 cirq measure q0 q1 key m noisy_simulator cirq densitymatrixsimulator noise noise result noisy_simulator run circuit repetitions 500 print result histogram key m depolarizing noise with p 0 005 approximates sycamore two qubit gate error rates use noise modeling during algorithm development before applying for hardware access 5 apply for hardware access google quantum ai hardware access is not available via a public queue researchers can apply through the google quantum ai research program applications are reviewed based on scientific merit research goals and alignment with google s quantum computing roadmap students and academic groups with strong proposals are encouraged to apply access how to access google quantum ai hardware google does not offer a public pay per use hardware queue access to real qpus is through the research collaboration program however free simulation via cirq is available to anyone google quantum ai research program application based free for approved researchers qualified academic and research teams can apply for direct qpu access via the google quantum ai research program access is granted based on scientific merit and research alignment no public queue exists view details google cloud quantum computing service research program limited preview google cloud provides access to quantum hardware through a limited preview program integrated with google cloud infrastructure availability is restricted interested teams should apply via google cloud view details cirq simulator free unlimited free local simulation cirq s built in simulators run locally on any machine at no cost the noise model simulators can approximate sycamore class hardware behavior and statevector simulation is practical up to roughly 30 qubits on a standard workstation view details google colab cirq free cloud simulation google provides free colab notebooks with cirq pre installed all tutorials and algorithm examples on quantumai google run in colab against local simulators with no hardware cost or account setup required view details note on access unlike ibm quantum s public free tier google quantum ai hardware is not available on demand if your goal is learning quantum programming with a real backend ibm quantum s free open plan 10 minutes of runtime per 28 days on 100 qubit systems or ionq via aws braket are the most accessible entry points cirq s local simulators are excellent for learning the cirq programming model without hardware access learning resources tutorials and reference docs relevant tutorials hello world with cirq build and simulate your first cirq circuit getting started with cirq qubits gates circuits and simulators walkthrough quantum chemistry with openfermion fermionic hamiltonians and vqe on cirq framework references cirq reference gates qubits simulators noise models and google hardware pennylane reference pennylane supports cirq as a backend for qml experiments search start here start from zero no physics needed best courses 112 listed free tutorials 220 hands on glossary 241 terms qubit factory watch a qubit get built 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 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