Cirq Tutorials and Documentation (Google Quantum AI)

Google Quantum AI’s comprehensive, free tutorial collection for Cirq, the open-source Python framework for quantum computing developed and used by Google. All tutorials run in Google Colab notebooks directly in your browser, with no local installation required. The Quantum Virtual Machine (QVM) provides realistic simulated access to Google quantum hardware noise models.

Cirq is the framework behind Google’s quantum computing research, including the 2019 quantum supremacy demonstration and the 2024 below-threshold error correction experiment. Learning Cirq gives you access to the same tools Google researchers use, plus the ability to prototype circuits for Google quantum hardware.

What you’ll learn

• Cirq fundamentals: qubits as objects with spatial coordinates, gates as operations, moments as layers of simultaneous operations, and circuits as sequences of moments
• Circuit construction: building circuits gate by gate, decomposing high-level operations, and inspecting circuits with built-in visualisation tools
• Simulators in Cirq: the pure state simulator for noiseless simulation, density matrix simulation for mixed states, and the Clifford simulator for stabilizer circuits
• Noise models: depolarizing noise, amplitude damping, and how to apply realistic noise models to simulate hardware behaviour
• The Quantum Virtual Machine (QVM): Google’s virtual device that emulates real Google quantum processor noise, letting you prototype circuits that behave as they would on actual hardware
• Textbook algorithms in Cirq: quantum teleportation, the quantum Fourier transform, phase estimation, Grover’s algorithm, and the variational quantum eigensolver, each with complete working implementations
• Connecting to Google quantum hardware: the structure of Google’s quantum processors, how to express circuits for hardware topology, and the basics of accessing the Google Quantum Computing Service

Course structure

The tutorials are organized into categories on quantumai.google/cirq: getting started, core concepts, simulations, hardware access, and experiments. Each tutorial is a self-contained Jupyter notebook combining explanatory text, code, and output.

There is no fixed curriculum sequence; learners can follow the recommended getting started path or jump directly to specific topics. The textbook algorithms section provides complete, runnable implementations of canonical quantum algorithms.

Who is this for?

• Developers and researchers who want to learn quantum programming on a different framework from Qiskit, using Google’s approach
• Anyone interested in running circuits on Google quantum hardware or simulating Google hardware noise
• Quantum computing students who want complete implementations of standard algorithms to study and modify
• Researchers who want to understand the tools behind Google’s quantum computing experiments

Prerequisites

Python programming experience is required. Basic familiarity with quantum circuits, qubits, and gates is helpful. NumPy is used throughout the tutorials. No prior Cirq experience is needed; the getting started tutorials introduce the framework from scratch.

Hands-on practice

All tutorials run in Google Colab notebooks:

• Build your first quantum circuit in Cirq, add gates, and simulate the result
• Apply a noise model to a circuit and observe how depolarizing noise affects output
• Run a circuit on the Quantum Virtual Machine and compare to the noiseless simulator
• Implement the quantum Fourier transform in Cirq and verify the output
• Build Grover’s algorithm for a simple search problem and confirm the quadratic speedup
• Implement the variational quantum eigensolver for a small Hamiltonian
• Inspect the native gate set and connectivity of a Google quantum processor

Why take this course?

Cirq’s explicit treatment of hardware topology and noise from the very start makes it excellent for developing intuition about real quantum hardware constraints, even if you ultimately work with other frameworks. The spatial qubit model forces you to think about connectivity, which is one of the most important practical considerations for near-term quantum computing.

The Quantum Virtual Machine is a particularly valuable resource: it provides realistic noise simulation calibrated to actual Google hardware without requiring access to the hardware itself, letting you prototype circuits that behave as they would in practice.