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cirq

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Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

When to use

• Building, simulating, or optimizing NISQ circuits in Python
• Running jobs on Google Quantum AI processors (via `cirq-google`) or partner backends (IonQ, Azure Quantum, AQT, Pasqal)
• Modeling noise, compiling to hardware gatesets, or designing characterization experiments
• Using parameter sweeps, transformers, or the ReCirq experiment patterns

When not to use

• IBM hardware
• Quantum ML with autodiff
• Physics simulations

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