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Hardware details are a first-class concern, letting algorithm implementations target real devices. Circuits run on built-in simulators or on supported quantum hardware platforms.\u003C\u002Fp>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Value\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>Language\u003C\u002Ftd>\n\u003Ctd>Python 3.11+\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Platform\u003C\u002Ftd>\n\u003Ctd>Linux, macOS, Windows, Google Colab\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Focus\u003C\u002Ftd>\n\u003Ctd>NISQ quantum circuit programming\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>License\u003C\u002Ftd>\n\u003Ctd>Apache-2.0\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Cblockquote>\n\u003Cp>Write quantum programs for today's hardware with hardware-aware abstractions.\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Ch2 id=\"key-features\">Key Features\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Flexible gates\u003C\u002Fstrong>: supports custom gate definitions and flexible circuit construction\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Symbolic parameters\u003C\u002Fstrong>: circuits can use symbolic variables resolved at run time\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Circuit transformation\u003C\u002Fstrong>: tools for transforming, compiling, and optimizing circuits\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Device modeling\u003C\u002Fstrong>: models the topology and constraints of target devices\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Noise modeling\u003C\u002Fstrong>: built-in noise models for simulating noisy circuits\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Multiple simulators\u003C\u002Fstrong>: several quantum circuit simulators included\u003C\u002Fli>\n\u003Cli>\u003Cstrong>High-performance simulation\u003C\u002Fstrong>: integrates with qsim for large-scale fast simulation\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Ecosystem interop\u003C\u002Fstrong>: works seamlessly with NumPy and SciPy\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"target-audience\">Target Audience\u003C\u002Fh2>\u003Cul>\n\u003Cli>Quantum algorithm researchers\u003C\u002Fli>\n\u003Cli>Quantum software developers\u003C\u002Fli>\n\u003Cli>Teachers and students in quantum computing courses\u003C\u002Fli>\n\u003Cli>Engineers working on quantum error correction\u003C\u002Fli>\n\u003Cli>Research teams needing NISQ hardware-aware programming\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"quick-start\">Quick Start\u003C\u002Fh2>\u003Col>\n\u003Cli>Install Cirq\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-bash\">pip install cirq\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"2\">\n\u003Cli>Run your first quantum circuit simulation\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-python\">import cirq\n\nqubit = cirq.GridQubit(0, 0)\ncircuit = cirq.Circuit(\n    cirq.X(qubit)**0.5,\n    cirq.measure(qubit, key='m')\n)\nsimulator = cirq.Simulator()\nresult = simulator.run(circuit, repetitions=20)\nprint(result)\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"3\">\n\u003Cli>Follow the official tutorials to keep learning circuit building and simulation\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Ch2 id=\"core-advantages\">Core Advantages\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Hardware aware\u003C\u002Fstrong>: abstractions designed for NISQ devices and real hardware constraints\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Strong performance\u003C\u002Fstrong>: combines with qsim and related tools for high-performance simulation\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Rich ecosystem\u003C\u002Fstrong>: integrates with Qualtran, OpenFermion, TensorFlow Quantum, and more\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Complete docs\u003C\u002Fstrong>: official tutorials, videos, and examples from beginner to advanced\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"system-requirements\">System Requirements\u003C\u002Fh2>\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Requirement\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>OS\u003C\u002Ftd>\n\u003Ctd>Linux, macOS, Windows\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Language\u003C\u002Ftd>\n\u003Ctd>Python 3.11 or later\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Optional\u003C\u002Ftd>\n\u003Ctd>Google Colab\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"notes\">Notes\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Version requirement\u003C\u002Fstrong>: only Python 3.11 and later are supported\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Hardware access\u003C\u002Fstrong>: running on real quantum hardware requires accounts and authorization on the corresponding platform\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Examples\u003C\u002Fstrong>: the examples directory in the repo covers textbook algorithms through advanced methods\u003C\u002Fli>\n\u003C\u002Ful>\n",1793197930619]